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https://github.com/akio-tomiya/gaugefields.jl
Utilities of gauge fields
https://github.com/akio-tomiya/gaugefields.jl
hep hep-lat hmc julia julia-language julialang lattice-gauge-theory lattice-qcd latticeqcd monte-carlo particle-physics physics qcd sciml
Last synced: 13 days ago
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Utilities of gauge fields
- Host: GitHub
- URL: https://github.com/akio-tomiya/gaugefields.jl
- Owner: akio-tomiya
- License: mit
- Created: 2022-01-03T04:35:23.000Z (almost 3 years ago)
- Default Branch: master
- Last Pushed: 2024-10-08T04:10:33.000Z (about 1 month ago)
- Last Synced: 2024-10-31T17:26:14.346Z (20 days ago)
- Topics: hep, hep-lat, hmc, julia, julia-language, julialang, lattice-gauge-theory, lattice-qcd, latticeqcd, monte-carlo, particle-physics, physics, qcd, sciml
- Language: Julia
- Homepage:
- Size: 114 MB
- Stars: 9
- Watchers: 3
- Forks: 6
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Gaugefields
[](https://github.com/akio-tomiya/Gaugefields.jl/actions/workflows/CI.yml)
[](https://akio-tomiya.github.io//Gaugefields.jl/dev)
# Abstract
This is a package for lattice QCD codes.
Treating gauge fields (links), gauge actions with MPI and autograd.
This package is used in [LatticeQCD.jl](https://github.com/akio-tomiya/LatticeQCD.jl)
and a code in a project [JuliaQCD](https://github.com/JuliaQCD/).
[NOTE: This is an extended version in order to implement higher-form gauge fields
(i.e., 't Hooft twisted boundary condition/flux).
See [o-morikawa/Gaugefields.jl](https://github.com/o-morikawa/Gaugefields.jl)]
# What this package can do:
This package has following functionarities
- SU(Nc) (Nc > 1) gauge fields in 2 or 4 dimensions with arbitrary actions.
- **Z(Nc) 2-form gauge fields in 4 dimensions, which are given as 't Hooft flux.**
- U(1) gauge fields in 2 dimensions with arbitrary actions.
- Configuration generation
- Heatbath
- quenched Hybrid Monte Carlo
- quenched Hybrid Monte Carlo being subject to 't Hooft twisted b.c.
- with external (non-dynamical) Z(Nc) 2-form gauge fields
- quenched Hybrid Monte Carlo for SU(Nc)/Z(Nc) gauge theory
- with dynamical Z(Nc) 2-form gauge fields
- Gradient flow via RK3
- Yang-Mills gradient flow
- Yang-Mills gradient flow being subject to 't Hooft twisted b.c.
- Gradient flow for SU(Nc)/Z(Nc) gauge theory
- I/O: ILDG and Bridge++ formats are supported ([c-lime](https://usqcd-software.github.io/c-lime/) will be installed implicitly with [CLIME_jll](https://github.com/JuliaBinaryWrappers/CLIME_jll.jl) )
- MPI parallel computation (experimental. See documents.)
- quenched HMC with MPI being subject to 't Hooft twisted b.c.
**The implementation of higher-form gauge fields is based on
[arXiv:2303.10977 [hep-lat]](https://arxiv.org/abs/2303.10977).**
Dynamical fermions will be supported with [LatticeDiracOperators.jl](https://github.com/akio-tomiya/LatticeDiracOperators.jl).
In addition, this supports followings
- **Autograd for functions with SU(Nc) variables**
- Stout smearing (exp projecting smearing)
- Stout force via [backpropagation](https://arxiv.org/abs/2103.11965)
Autograd can be worked for general Wilson lines except for ones have overlaps.
# Install
In Julia REPL in the package mode,
```
add Gaugefields.jl
```
# How to use
## File loading
## ILDG format
[ILDG](https://www-zeuthen.desy.de/~pleiter/ildg/ildg-file-format-1.1.pdf) format is one of standard formats for LatticeQCD configurations.
We can read ILDG format like:
```julia
using Gaugefields
NX = 4
NY = 4
NZ = 4
NT = 4
NC = 3
Nwing = 1
Dim = 4
U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "cold")
filename = "hoge.ildg"
ildg = ILDG(filename)
i = 1
L = [NX,NY,NZ,NT]
load_gaugefield!(U,i,ildg,L,NC)
```
Then, we can calculate the plaquette:
```julia
temp1 = similar(U[1])
temp2 = similar(U[1])
comb = 6
factor = 1/(comb*U[1].NV*U[1].NC)
@time plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
println("plaq_t = $plaq_t")
poly = calculate_Polyakov_loop(U,temp1,temp2)
println("polyakov loop = $(real(poly)) $(imag(poly))")
```
We can write a configuration as the ILDG format like
```julia
filename = "hoge.ildg"
save_binarydata(U,filename)
```
## Text format for Bridge++
Gaugefields.jl also supports a text format for [Bridge++](https://bridge.kek.jp/Lattice-code/index_e.html).
### File loading
```julia
using Gaugefields
filename = "testconf.txt"
load_BridgeText!(filename,U,L,NC)
```
### File saving
```julia
filename = "testconf.txt"
save_textdata(U,filename)
```
## JLD2 format
Gaugefields.jl also supports [JLD2 format](https://github.com/JuliaIO/JLD2.jl).
### File saving and loading
```julia
function main()
using Gaugefields
function savingexample()
NX = 4
NY = 4
NZ = 4
NT = 4
NC = 3
Nwing = 0
Dim = 4
U = Initialize_Gaugefields(NC, Nwing, NX, NY, NZ, NT, condition="hot")
temp1 = similar(U[1])
temp2 = similar(U[1])
comb = 6
factor = 1 / (comb * U[1].NV * U[1].NC)
@time plaq_t = calculate_Plaquette(U, temp1, temp2) * factor
println("plaq_t = $plaq_t")
filename = "test.jld2"
saveU(filename, U)
end
function loadingexample()
filename = "test.jld2"
U = loadU(filename)
temp1 = similar(U[1])
temp2 = similar(U[1])
comb = 6
factor = 1 / (comb * U[1].NV * U[1].NC)
@time plaq_t = calculate_Plaquette(U, temp1, temp2) * factor
println("plaq_t = $plaq_t")
end
savingexample()
loadingexample()
```
## Z(Nc) 2-form gauge fields
SU(N) gauge fields possess Z(N) center symmetry,
which is called 1-form global symmetry, a type of generalized symmetry.
To gauge the 1-form center symmetry,
we can define the Z(N) 2-form gauge fields in four dimensions, B, as
```julia
NX = 4
NY = 4
NZ = 4
NT = 4
Nwing = 0
NC = 3
flux=[1,0,0,0,0,1] # FLUX=[Z12,Z13,Z14,Z23,Z24,Z34]
println("Flux is ", flux)
B = Initialize_Bfields(NC,flux,Nwing,NX,NY,NZ,NY,condition = "tflux")
println("Initial conf of B at [1,2][2,2,:,:,NZ,NT]")
display(B[1,2][2,2,:,:,NZ,NT])
```
## Heatbath updates (even-odd method)
```julia
using Gaugefields
function heatbath_SU3!(U,NC,temps,β)
Dim = 4
V = temps[5]
ITERATION_MAX = 10^5
temps2 = Array{Matrix{ComplexF64},1}(undef,5)
temps3 = Array{Matrix{ComplexF64},1}(undef,5)
for i=1:5
temps2[i] = zeros(ComplexF64,2,2)
temps3[i] = zeros(ComplexF64,NC,NC)
end
mapfunc!(A,B) = SU3update_matrix!(A,B,β,NC,temps2,temps3,ITERATION_MAX)
for μ=1:Dim
loops = loops_staple[(Dim,μ)]
iseven = true
evaluate_gaugelinks_evenodd!(V,loops,U,temps[1:4],iseven)
map_U!(U[μ],mapfunc!,V,iseven)
iseven = false
evaluate_gaugelinks_evenodd!(V,loops,U,temps[1:4],iseven)
map_U!(U[μ],mapfunc!,V,iseven)
end
end
function heatbathtest_4D(NX,NY,NZ,NT,β,NC)
Dim = 4
Nwing = 1
U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "cold")
temp1 = similar(U[1])
temp2 = similar(U[1])
# for heatbath update
temp3 = similar(U[1])
temp4 = similar(U[1])
temp5 = similar(U[1])
comb = 6
factor = 1/(comb*U[1].NV*U[1].NC)
@time plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
println("plaq_t = $plaq_t")
poly = calculate_Polyakov_loop(U,temp1,temp2)
println("polyakov loop = $(real(poly)) $(imag(poly))")
numhb = 40
for itrj = 1:numhb
heatbath_SU3!(U,NC,[temp1,temp2,temp3,temp4,temp5],β)
if itrj % 10 == 0
@time plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
println("$itrj plaq_t = $plaq_t")
poly = calculate_Polyakov_loop(U,temp1,temp2)
println("$itrj polyakov loop = $(real(poly)) $(imag(poly))")
end
end
return plaq_t
end
NX = 4
NY = 4
NZ = 4
NT = 4
Nwing = 1
β = 5.7
NC = 3
@time plaq_t = heatbathtest_4D(NX,NY,NZ,NT,β,NC)
```
## Heatbath updates with general actions
We can do heatbath updates with a general action.
```julia
using Gaugefields
function heatbathtest_4D(NX,NY,NZ,NT,β,NC)
Dim = 4
Nwing = 1
U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "cold")
println(typeof(U))
gauge_action = GaugeAction(U)
plaqloop = make_loops_fromname("plaquette",Dim=Dim)
append!(plaqloop,plaqloop')
βinp = β/2
push!(gauge_action,βinp,plaqloop)
rectloop = make_loops_fromname("rectangular",Dim=Dim)
append!(rectloop,rectloop')
βinp = β/2
push!(gauge_action,βinp,rectloop)
hnew = Heatbath_update(U,gauge_action)
show(gauge_action)
temp1 = similar(U[1])
temp2 = similar(U[1])
comb = 6
factor = 1/(comb*U[1].NV*U[1].NC)
@time plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
println("plaq_t = $plaq_t")
poly = calculate_Polyakov_loop(U,temp1,temp2)
println("polyakov loop = $(real(poly)) $(imag(poly))")
numhb = 1000
for itrj = 1:numhb
heatbath!(U,hnew)
plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
poly = calculate_Polyakov_loop(U,temp1,temp2)
if itrj % 40 == 0
println("$itrj plaq_t = $plaq_t")
println("$itrj polyakov loop = $(real(poly)) $(imag(poly))")
end
end
return plaq_t
end
NX = 4
NY = 4
NZ = 4
NT = 4
NC = 3
β = 5.7
heatbathtest_4D(NX,NY,NZ,NT,β,NC)
```
In this code, we consider the plaquette and rectangular actions.
## Gradient flow
We can use Lüscher's gradient flow.
```julia
NX = 4
NY = 4
NZ = 4
NT = 4
Nwing = 1
NC = 3
U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "hot")
temp1 = similar(U[1])
temp2 = similar(U[1])
temp3 = similar(U[1])
comb = 6
factor = 1/(comb*U[1].NV*U[1].NC)
g = Gradientflow(U)
for itrj=1:100
flow!(U,g)
@time plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
println("$itrj plaq_t = $plaq_t")
poly = calculate_Polyakov_loop(U,temp1,temp2)
println("$itrj polyakov loop = $(real(poly)) $(imag(poly))")
end
```
```julia
NX = 4
NY = 4
NZ = 4
NT = 4
Nwing = 0
NC = 3
flux=[1,0,0,0,0,1] # FLUX=[Z12,Z13,Z14,Z23,Z24,Z34]
U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "hot")
B = Initialize_Bfields(NC,flux,Nwing,NX,NY,NZ,NY,condition = "tflux")
temp1 = similar(U[1])
temp2 = similar(U[1])
comb = 6
factor = 1/(comb*U[1].NV*U[1].NC)
g = Gradientflow(U, B)
for itrj=1:100
flow!(U,B,g)
@time plaq_t = calculate_Plaquette(U,B,temp1,temp2)*factor
println("$itrj plaq_t = $plaq_t")
poly = calculate_Polyakov_loop(U,temp1,temp2)
println("$itrj polyakov loop = $(real(poly)) $(imag(poly))")
end
```
## Hybrid Monte Carlo
### HMC for SU(Nc) gauge theory
We can do the HMC simulations. The example code is as follows.
```julia
using Random
using Gaugefields
using LinearAlgebra
function calc_action(gauge_action,U,p)
NC = U[1].NC
Sg = -evaluate_GaugeAction(gauge_action,U)/NC #evaluate_Gauge_action(gauge_action,U) = tr(evaluate_Gaugeaction_untraced(gauge_action,U))
Sp = p*p/2
S = Sp + Sg
return real(S)
end
function MDstep!(gauge_action,U,p,MDsteps,Dim,Uold,temp1,temp2)
Δτ = 1.0/MDsteps
gauss_distribution!(p)
Sold = calc_action(gauge_action,U,p)
substitute_U!(Uold,U)
for itrj=1:MDsteps
U_update!(U,p,0.5,Δτ,Dim,gauge_action)
P_update!(U,p,1.0,Δτ,Dim,gauge_action,temp1,temp2)
U_update!(U,p,0.5,Δτ,Dim,gauge_action)
end
Snew = calc_action(gauge_action,U,p)
println("Sold = $Sold, Snew = $Snew")
println("Snew - Sold = $(Snew-Sold)")
ratio = min(1,exp(-Snew+Sold))
if rand() > ratio
substitute_U!(U,Uold)
return false
else
return true
end
end
function U_update!(U,p,ϵ,Δτ,Dim,gauge_action)
temps = get_temporary_gaugefields(gauge_action)
temp1 = temps[1]
temp2 = temps[2]
expU = temps[3]
W = temps[4]
for μ=1:Dim
exptU!(expU,ϵ*Δτ,p[μ],[temp1,temp2])
mul!(W,expU,U[μ])
substitute_U!(U[μ],W)
end
end
function P_update!(U,p,ϵ,Δτ,Dim,gauge_action,temp1,temp2) # p -> p +factor*U*dSdUμ
NC = U[1].NC
temp = temp1
dSdUμ = temp2
factor = -ϵ*Δτ/(NC)
for μ=1:Dim
calc_dSdUμ!(dSdUμ,gauge_action,μ,U)
mul!(temp,U[μ],dSdUμ) # U*dSdUμ
Traceless_antihermitian_add!(p[μ],factor,temp)
end
end
function HMC_test_4D(NX,NY,NZ,NT,NC,β)
Dim = 4
Nwing = 0
Random.seed!(123)
U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "hot",randomnumber="Reproducible")
#"Reproducible"
println(typeof(U))
temp1 = similar(U[1])
temp2 = similar(U[1])
if Dim == 4
comb = 6 #4*3/2
elseif Dim == 3
comb = 3
elseif Dim == 2
comb = 1
else
error("dimension $Dim is not supported")
end
factor = 1/(comb*U[1].NV*U[1].NC)
@time plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
println("0 plaq_t = $plaq_t")
poly = calculate_Polyakov_loop(U,temp1,temp2)
println("0 polyakov loop = $(real(poly)) $(imag(poly))")
gauge_action = GaugeAction(U)
plaqloop = make_loops_fromname("plaquette")
append!(plaqloop,plaqloop')
β = β/2
push!(gauge_action,β,plaqloop)
#show(gauge_action)
p = initialize_TA_Gaugefields(U) #This is a traceless-antihermitian gauge fields. This has NC^2-1 real coefficients.
Uold = similar(U)
substitute_U!(Uold,U)
MDsteps = 100
temp1 = similar(U[1])
temp2 = similar(U[1])
comb = 6
factor = 1/(comb*U[1].NV*U[1].NC)
numaccepted = 0
numtrj = 10
for itrj = 1:numtrj
t = @timed begin
accepted = MDstep!(gauge_action,U,p,MDsteps,Dim,Uold,temp1,temp2)
end
if get_myrank(U) == 0
println("elapsed time for MDsteps: $(t.time) [s]")
end
numaccepted += ifelse(accepted,1,0)
#plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
#println("$itrj plaq_t = $plaq_t")
if itrj % 10 == 0
@time plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
println("$itrj plaq_t = $plaq_t")
poly = calculate_Polyakov_loop(U,temp1,temp2)
println("$itrj polyakov loop = $(real(poly)) $(imag(poly))")
println("acceptance ratio ",numaccepted/itrj)
end
end
return plaq_t,numaccepted/numtrj
end
function main()
β = 5.7
NX = 8
NY = 8
NZ = 8
NT = 8
NC = 3
HMC_test_4D(NX,NY,NZ,NT,NC,β)
end
main()
```
## Non-dynamical higher-form gauge fields
We can do the HMC simulations with B fields. The example code is as follows.
```julia
using Random
using Gaugefields
using LinearAlgebra
function calc_action(gauge_action,U,B,p)
NC = U[1].NC
Sg = -evaluate_GaugeAction(gauge_action,U,B)/NC
Sp = p*p/2
S = Sp + Sg
return real(S)
end
function MDstep!(gauge_action,U,B,p,MDsteps,Dim,Uold,temp1,temp2)
Δτ = 1.0/MDsteps
gauss_distribution!(p)
Sold = calc_action(gauge_action,U,B,p)
substitute_U!(Uold,U)
for itrj=1:MDsteps
U_update!(U,p,0.5,Δτ,Dim,gauge_action)
P_update!(U,B,p,1.0,Δτ,Dim,gauge_action,temp1,temp2)
U_update!(U,p,0.5,Δτ,Dim,gauge_action)
end
Snew = calc_action(gauge_action,U,B,p)
println("Sold = $Sold, Snew = $Snew")
println("Snew - Sold = $(Snew-Sold)")
ratio = min(1,exp(-Snew+Sold)) # bug is fixed
if rand() > ratio
substitute_U!(U,Uold)
return false
else
return true
end
end
function U_update!(U,p,ϵ,Δτ,Dim,gauge_action)
temps = get_temporary_gaugefields(gauge_action)
temp1 = temps[1]
temp2 = temps[2]
expU = temps[3]
W = temps[4]
for μ=1:Dim
exptU!(expU,ϵ*Δτ,p[μ],[temp1,temp2])
mul!(W,expU,U[μ])
substitute_U!(U[μ],W)
end
end
function P_update!(U,B,p,ϵ,Δτ,Dim,gauge_action,temp1,temp2) # p -> p +factor*U*dSdUμ
NC = U[1].NC
temp = temp1
dSdUμ = temp2
factor = -ϵ*Δτ/(NC)
for μ=1:Dim
calc_dSdUμ!(dSdUμ,gauge_action,μ,U,B)
mul!(temp,U[μ],dSdUμ) # U*dSdUμ
Traceless_antihermitian_add!(p[μ],factor,temp)
end
end
function HMC_test_4D_tHooft(NX,NY,NZ,NT,NC,Flux,β)
Dim = 4
Nwing = 0
flux = Flux
println("Flux : ", flux)
Random.seed!(123)
U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "cold",randomnumber="Reproducible")
B = Initialize_Bfields(NC,flux,Nwing,NX,NY,NZ,NT,condition = "tflux")
temp1 = similar(U[1])
temp2 = similar(U[1])
if Dim == 4
comb = 6 #4*3/2
elseif Dim == 3
comb = 3
elseif Dim == 2
comb = 1
else
error("dimension $Dim is not supported")
end
factor = 1/(comb*U[1].NV*U[1].NC)
@time plaq_t = calculate_Plaquette(U,B,temp1,temp2)*factor
println("0 plaq_t = $plaq_t")
poly = calculate_Polyakov_loop(U,temp1,temp2)
println("0 polyakov loop = $(real(poly)) $(imag(poly))")
gauge_action = GaugeAction(U,B)
plaqloop = make_loops_fromname("plaquette")
append!(plaqloop,plaqloop')
β = β/2
push!(gauge_action,β,plaqloop)
#show(gauge_action)
p = initialize_TA_Gaugefields(U) #This is a traceless-antihermitian gauge fields. This has NC^2-1 real coefficients.
Uold = similar(U)
substitute_U!(Uold,U)
MDsteps = 50
temp1 = similar(U[1])
temp2 = similar(U[1])
comb = 6
factor = 1/(comb*U[1].NV*U[1].NC)
numaccepted = 0
numtrj = 100
for itrj = 1:numtrj
t = @timed begin
accepted = MDstep!(gauge_action,U,B,p,MDsteps,Dim,Uold,temp1,temp2)
end
if get_myrank(U) == 0
# println("elapsed time for MDsteps: $(t.time) [s]")
end
numaccepted += ifelse(accepted,1,0)
#plaq_t = calculate_Plaquette(U,B,temp1,temp2)*factor
#println("$itrj plaq_t = $plaq_t")
if itrj % 10 == 0
@time plaq_t = calculate_Plaquette(U,B,temp1,temp2)*factor
println("$itrj plaq_t = $plaq_t")
poly = calculate_Polyakov_loop(U,temp1,temp2)
println("$itrj polyakov loop = $(real(poly)) $(imag(poly))")
println("acceptance ratio ",numaccepted/itrj)
end
end
return plaq_t,numaccepted/numtrj
end
function main()
β = 5.7
NX = 4
NY = 4
NZ = 4
NT = 4
NC = 3
Flux = [0,0,1,1,0,0]
#HMC_test_4D(NX,NY,NZ,NT,NC,β)
HMC_test_4D_tHooft(NX,NY,NZ,NT,NC,Flux,β)
end
main()
```
## Dynamical higher-form gauge fields
HMC simulations with dynamical B fields are as follows:
```julia
using Random
using Gaugefields
using Wilsonloop
using LinearAlgebra
function calc_action(gauge_action,U,B,p)
NC = U[1].NC
Sg = -evaluate_GaugeAction(gauge_action,U,B)/NC
Sp = p*p/2
S = Sp + Sg
return real(S)
end
function MDstep!(gauge_action,U,B,p,MDsteps,Dim,Uold,temp1,temp2)
Δτ = 1.0/MDsteps
gauss_distribution!(p)
Sold = calc_action(gauge_action,U,B,p)
substitute_U!(Uold,U)
for itrj=1:MDsteps
U_update!(U,p,0.5,Δτ,Dim,gauge_action)
P_update!(U,B,p,1.0,Δτ,Dim,gauge_action,temp1,temp2)
U_update!(U,p,0.5,Δτ,Dim,gauge_action)
end
Snew = calc_action(gauge_action,U,B,p)
# println("Sold = $Sold, Snew = $Snew")
# println("Snew - Sold = $(Snew-Sold)")
ratio = min(1,exp(-Snew+Sold))
if rand() > ratio
substitute_U!(U,Uold)
return false
else
return true
end
end
function MDstep!(
gauge_action,
U,
B,
flux,
p,
MDsteps, # MDsteps should be an even integer
Dim,
Uold,
Bold,
flux_old,
temp1,
temp2
) # Halfway-updating HMC
Δτ = 1.0/MDsteps
gauss_distribution!(p)
Sold = calc_action(gauge_action,U,B,p)
substitute_U!(Uold,U)
substitute_U!(Bold,B)
flux_old[:] = flux[:]
for itrj=1:MDsteps
U_update!(U,p,0.5,Δτ,Dim,gauge_action)
P_update!(U,B,p,1.0,Δτ,Dim,gauge_action,temp1,temp2)
U_update!(U,p,0.5,Δτ,Dim,gauge_action)
if itrj == Int(MDsteps/2)
Flux_update!(B,flux)
end
end
Snew = calc_action(gauge_action,U,B,p)
# println("Sold = $Sold, Snew = $Snew")
# println("Snew - Sold = $(Snew-Sold)")
ratio = min(1,exp(-Snew+Sold))
if rand() > ratio
println("rejected! flux = ", flux_old)
substitute_U!(U,Uold)
substitute_U!(B,Bold)
flux[:] = flux_old[:]
return false
else
println("accepted! flux_old = ", flux_old, " -> flux_new = ", flux)
return true
end
end
function MDstep!(
gauge_action,
U,
B,
flux,
p,
MDsteps,
num_HMC,
Dim,
Uold1,
Uold2,
Bold,
flux_old,
temp1,
temp2
) # Double-tesing HMC
p0 = initialize_TA_Gaugefields(U)
Sold = calc_action(gauge_action,U,B,p0)
substitute_U!(Uold1,U)
substitute_U!(Bold, B)
flux_old[:] = flux[:]
Flux_update!(B,flux)
for ihmc=1:num_HMC
MDstep!(gauge_action,U,B,p,MDsteps,Dim,Uold2,temp1,temp2)
end
Snew = calc_action(gauge_action,U,B,p0)
println("Sold = $Sold, Snew = $Snew")
println("Snew - Sold = $(Snew-Sold)")
ratio = min(1,exp(-Snew+Sold))
if rand() > ratio
println("rejected! flux = ", flux_old)
substitute_U!(U,Uold1)
substitute_U!(B,Bold)
flux[:] = flux_old[:]
return false
else
println("accepted! flux_old = ", flux_old, " -> flux_new = ", flux)
return true
end
end
function Flux_update!(B,flux)
NC = B[1,2].NC
NDW = B[1,2].NDW
NX = B[1,2].NX
NY = B[1,2].NY
NZ = B[1,2].NZ
NT = B[1,2].NT
i = rand(1:6)
flux[i] += rand(-1:1)
flux[i] %= NC
flux[i] += (flux[i] < 0) ? NC : 0
# flux[:] = rand(0:NC-1,6)
B = Initialize_Bfields(NC,flux,NDW,NX,NY,NZ,NT,condition = "tflux")
end
function U_update!(U,p,ϵ,Δτ,Dim,gauge_action)
temps = get_temporary_gaugefields(gauge_action)
temp1 = temps[1]
temp2 = temps[2]
expU = temps[3]
W = temps[4]
for μ=1:Dim
exptU!(expU,ϵ*Δτ,p[μ],[temp1,temp2])
mul!(W,expU,U[μ])
substitute_U!(U[μ],W)
end
end
function P_update!(U,B,p,ϵ,Δτ,Dim,gauge_action,temp1,temp2) # p -> p +factor*U*dSdUμ
NC = U[1].NC
temp = temp1
dSdUμ = temp2
factor = -ϵ*Δτ/(NC)
for μ=1:Dim
calc_dSdUμ!(dSdUμ,gauge_action,μ,U,B)
mul!(temp,U[μ],dSdUμ) # U*dSdUμ
Traceless_antihermitian_add!(p[μ],factor,temp)
end
end
function HMC_test_4D_dynamicalB(NX,NY,NZ,NT,NC,β)
Dim = 4
Nwing = 0
Random.seed!(123)
flux = [1,1,1,1,2,0]
U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "cold",randomnumber="Reproducible")
B = Initialize_Bfields(NC,flux,Nwing,NX,NY,NZ,NT,condition = "tflux")
L = [NX,NY,NZ,NT]
filename = "test/confs/U_beta6.0_L8_F111120_4000.txt"
load_BridgeText!(filename,U,L,NC)
temp1 = similar(U[1])
temp2 = similar(U[1])
if Dim == 4
comb = 6 #4*3/2
elseif Dim == 3
comb = 3
elseif Dim == 2
comb = 1
else
error("dimension $Dim is not supported")
end
factor = 1/(comb*U[1].NV*U[1].NC)
@time plaq_t = calculate_Plaquette(U,B,temp1,temp2)*factor
println("0 plaq_t = $plaq_t")
poly = calculate_Polyakov_loop(U,temp1,temp2)
println("0 polyakov loop = $(real(poly)) $(imag(poly))")
gauge_action = GaugeAction(U,B)
plaqloop = make_loops_fromname("plaquette")
append!(plaqloop,plaqloop')
β = β/2
push!(gauge_action,β,plaqloop)
#show(gauge_action)
p = initialize_TA_Gaugefields(U) #This is a traceless-antihermitian gauge fields. This has NC^2-1 real coefficients.
Uold = similar(U)
substitute_U!(Uold, U)
Bold = similar(B)
substitute_U!(Bold,B)
flux_old = zeros(Int, 6)
MDsteps = 50 # even integer!!!
temp1 = similar(U[1])
temp2 = similar(U[1])
comb = 6
factor = 1/(comb*U[1].NV*U[1].NC)
numaccepted = 0
numtrj = 100
for itrj = 1:numtrj
t = @timed begin
accepted = MDstep!(
gauge_action,
U,
B,
flux,
p,
MDsteps,
Dim,
Uold,
Bold,
flux_old,
temp1,
temp2
)
end
if get_myrank(U) == 0
println("Flux : ", flux)
# println("elapsed time for MDsteps: $(t.time) [s]")
end
numaccepted += ifelse(accepted,1,0)
#plaq_t = calculate_Plaquette(U,B,temp1,temp2)*factor
#println("$itrj plaq_t = $plaq_t")
if itrj % 10 == 0
@time plaq_t = calculate_Plaquette(U,B,temp1,temp2)*factor
println("$itrj plaq_t = $plaq_t")
poly = calculate_Polyakov_loop(U,temp1,temp2)
println("$itrj polyakov loop = $(real(poly)) $(imag(poly))")
println("acceptance ratio ",numaccepted/itrj)
end
end
return plaq_t,numaccepted/numtrj
end
function main()
β = 6.0
NX = 8
NY = 8
NZ = 8
NT = 8
NC = 3
HMC_test_4D_dynamicalB(NX,NY,NZ,NT,NC,β)
end
main()
```
## Gradient flow with general terms
We can do the gradient flow with general terms with the use of Wilsonloop.jl, which is shown below.
The coefficient of the action can be complex. The complex conjugate of the action defined here is added automatically to make the total action hermitian.
The code is
```julia
using Random
using Test
using Gaugefields
using Wilsonloop
function gradientflow_test_4D(NX,NY,NZ,NT,NC)
Dim = 4
Nwing = 1
Random.seed!(123)
U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "hot",randomnumber="Reproducible")
temp1 = similar(U[1])
temp2 = similar(U[1])
if Dim == 4
comb = 6 #4*3/2
elseif Dim == 3
comb = 3
elseif Dim == 2
comb = 1
else
error("dimension $Dim is not supported")
end
factor = 1/(comb*U[1].NV*U[1].NC)
@time plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
println("0 plaq_t = $plaq_t")
poly = calculate_Polyakov_loop(U,temp1,temp2)
println("0 polyakov loop = $(real(poly)) $(imag(poly))")
#Plaquette term
loops_p = Wilsonline{Dim}[]
for μ=1:Dim
for ν=μ:Dim
if ν == μ
continue
end
loop1 = Wilsonline([(μ,1),(ν,1),(μ,-1),(ν,-1)],Dim = Dim)
push!(loops_p,loop1)
end
end
#Rectangular term
loops = Wilsonline{Dim}[]
for μ=1:Dim
for ν=μ:Dim
if ν == μ
continue
end
loop1 = Wilsonline([(μ,1),(ν,2),(μ,-1),(ν,-2)],Dim = Dim)
push!(loops,loop1)
loop1 = Wilsonline([(μ,2),(ν,1),(μ,-2),(ν,-1)],Dim = Dim)
push!(loops,loop1)
end
end
listloops = [loops_p,loops]
listvalues = [1+im,0.1]
g = Gradientflow_general(U,listloops,listvalues,eps = 0.01)
for itrj=1:100
flow!(U,g)
if itrj % 10 == 0
@time plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
println("$itrj plaq_t = $plaq_t")
poly = calculate_Polyakov_loop(U,temp1,temp2)
println("$itrj polyakov loop = $(real(poly)) $(imag(poly))")
end
end
return plaq_t
end
function gradientflow_test_2D(NX,NT,NC)
Dim = 2
Nwing = 1
U = Initialize_Gaugefields(NC,Nwing,NX,NT,condition = "hot",randomnumber="Reproducible")
temp1 = similar(U[1])
temp2 = similar(U[1])
if Dim == 4
comb = 6 #4*3/2
elseif Dim == 3
comb = 3
elseif Dim == 2
comb = 1
else
error("dimension $Dim is not supported")
end
factor = 1/(comb*U[1].NV*U[1].NC)
@time plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
println("0 plaq_t = $plaq_t")
poly = calculate_Polyakov_loop(U,temp1,temp2)
println("0 polyakov loop = $(real(poly)) $(imag(poly))")
#g = Gradientflow(U,eps = 0.01)
#listnames = ["plaquette"]
#listvalues = [1]
loops_p = Wilsonline{Dim}[]
for μ=1:Dim
for ν=μ:Dim
if ν == μ
continue
end
loop1 = Wilsonline([(μ,1),(ν,1),(μ,-1),(ν,-1)],Dim = Dim)
push!(loops_p,loop1)
end
end
loops = Wilsonline{Dim}[]
for μ=1:Dim
for ν=μ:Dim
if ν == μ
continue
end
loop1 = Wilsonline([(μ,1),(ν,2),(μ,-1),(ν,-2)],Dim = Dim)
push!(loops,loop1)
loop1 = Wilsonline([(μ,2),(ν,1),(μ,-2),(ν,-1)],Dim = Dim)
push!(loops,loop1)
end
end
listloops = [loops_p,loops]
listvalues = [1+im,0.1]
g = Gradientflow_general(U,listloops,listvalues,eps = 0.01)
for itrj=1:100
flow!(U,g)
if itrj % 10 == 0
@time plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
println("$itrj plaq_t = $plaq_t")
poly = calculate_Polyakov_loop(U,temp1,temp2)
println("$itrj polyakov loop = $(real(poly)) $(imag(poly))")
end
end
return plaq_t
end
const eps = 0.1
println("2D system")
@testset "2D" begin
NX = 4
#NY = 4
#NZ = 4
NT = 4
Nwing = 1
@testset "NC=1" begin
β = 2.3
NC = 1
println("NC = $NC")
@time plaq_t = gradientflow_test_2D(NX,NT,NC)
end
#error("d")
@testset "NC=2" begin
β = 2.3
NC = 2
println("NC = $NC")
@time plaq_t = gradientflow_test_2D(NX,NT,NC)
end
@testset "NC=3" begin
β = 5.7
NC = 3
println("NC = $NC")
@time plaq_t = gradientflow_test_2D(NX,NT,NC)
end
@testset "NC=4" begin
β = 5.7
NC = 4
println("NC = $NC")
@time plaq_t = gradientflow_test_2D(NX,NT,NC)
end
end
println("4D system")
@testset "4D" begin
NX = 4
NY = 4
NZ = 4
NT = 4
Nwing = 1
@testset "NC=2" begin
β = 2.3
NC = 2
println("NC = $NC")
@time plaq_t = gradientflow_test_4D(NX,NY,NZ,NT,NC)
end
@testset "NC=3" begin
β = 5.7
NC = 3
println("NC = $NC")
@time plaq_t = gradientflow_test_4D(NX,NY,NZ,NT,NC)
end
@testset "NC=4" begin
β = 5.7
NC = 4
println("NC = $NC")
val = 0.7301232810349298
@time plaq_t =gradientflow_test_4D(NX,NY,NZ,NT,NC)
end
end
```
```julia
using Random
using Test
using Gaugefields
using Wilsonloop
function gradientflow_test_4D(NX,NY,NZ,NT,NC)
Dim = 4
Nwing = 0
flux = [0,0,1,1,0,0]
Random.seed!(123)
U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "hot",randomnumber="Reproducible")
B = Initialize_Bfields(NC,flux,Nwing,NX,NY,NZ,NT,condition = "tflux")
temp1 = similar(U[1])
temp2 = similar(U[1])
if Dim == 4
comb = 6 #4*3/2
elseif Dim == 3
comb = 3
elseif Dim == 2
comb = 1
else
error("dimension $Dim is not supported")
end
factor = 1/(comb*U[1].NV*U[1].NC)
@time plaq_t = calculate_Plaquette(U,B,temp1,temp2)*factor
println("0 plaq_t = $plaq_t")
poly = calculate_Polyakov_loop(U,temp1,temp2)
println("0 polyakov loop = $(real(poly)) $(imag(poly))")
#Plaquette term
loops_p = Wilsonline{Dim}[]
for μ=1:Dim
for ν=μ:Dim
if ν == μ
continue
end
loop1 = Wilsonline([(μ,1),(ν,1),(μ,-1),(ν,-1)],Dim = Dim)
push!(loops_p,loop1)
end
end
#Rectangular term
loops = Wilsonline{Dim}[]
for μ=1:Dim
for ν=μ:Dim
if ν == μ
continue
end
loop1 = Wilsonline([(μ,1),(ν,2),(μ,-1),(ν,-2)],Dim = Dim)
push!(loops,loop1)
loop1 = Wilsonline([(μ,2),(ν,1),(μ,-2),(ν,-1)],Dim = Dim)
push!(loops,loop1)
end
end
listloops = [loops_p,loops]
listvalues = [1+im,0.1]
g = Gradientflow_general(U,B,listloops,listvalues,eps = 0.1)
for itrj=1:10
flow!(U,B,g)
if itrj % 10 == 0
@time plaq_t = calculate_Plaquette(U,B,temp1,temp2)*factor
println("$itrj plaq_t = $plaq_t")
poly = calculate_Polyakov_loop(U,temp1,temp2)
println("$itrj polyakov loop = $(real(poly)) $(imag(poly))")
end
end
return plaq_t
end
const eps = 0.1
println("4D system")
@testset "4D" begin
NX = 4
NY = 4
NZ = 4
NT = 4
Nwing = 1
@testset "NC=2" begin
β = 2.3
NC = 2
println("NC = $NC")
@time plaq_t = gradientflow_test_4D(NX,NY,NZ,NT,NC)
end
@testset "NC=3" begin
β = 5.7
NC = 3
println("NC = $NC")
@time plaq_t = gradientflow_test_4D(NX,NY,NZ,NT,NC)
end
@testset "NC=4" begin
β = 5.7
NC = 4
println("NC = $NC")
val = 0.7301232810349298
@time plaq_t =gradientflow_test_4D(NX,NY,NZ,NT,NC)
end
end
```
## HMC with MPI
Here, we show the HMC with MPI.
the REPL and Jupyternotebook can not be used when one wants to use MPI.
At first, in Julia REPL in the package mode,
```
add MPI
```
Then,
```julia
using MPI
MPI.install_mpiexecjl()
```
and
```
export PATH="//.julia/bin/:$PATH"
```
The command is like:
```
mpiexecjl -np 2 julia mpi_sample.jl 1 1 1 2 true
```
```1 1 1 2``` means ```PEX PEY PEZ PET```. In this case, the time-direction is diveded by 2.
The sample code is written as
```julia
using Random
using Gaugefields
using LinearAlgebra
using MPI
if length(ARGS) < 5
error("USAGE: ","""
mpiexecjl -np 2 exe.jl 1 1 1 2 true
""")
end
const pes = Tuple(parse.(Int64,ARGS[1:4]))
const mpi = parse(Bool,ARGS[5])
function calc_action(gauge_action,U,p)
NC = U[1].NC
Sg = -evaluate_GaugeAction(gauge_action,U)/NC #evaluate_Gauge_action(gauge_action,U) = tr(evaluate_Gaugeaction_untraced(gauge_action,U))
Sp = p*p/2
S = Sp + Sg
return real(S)
end
function MDstep!(gauge_action,U,p,MDsteps,Dim,Uold,temp1,temp2)
Δτ = 1.0/MDsteps
gauss_distribution!(p)
Sold = calc_action(gauge_action,U,p)
substitute_U!(Uold,U)
for itrj=1:MDsteps
U_update!(U,p,0.5,Δτ,Dim,gauge_action)
P_update!(U,p,1.0,Δτ,Dim,gauge_action,temp1,temp2)
U_update!(U,p,0.5,Δτ,Dim,gauge_action)
end
Snew = calc_action(gauge_action,U,p)
if get_myrank(U) == 0
println("Sold = $Sold, Snew = $Snew")
println("Snew - Sold = $(Snew-Sold)")
end
ratio = min(1,exp(-Snew+Sold))
r = rand()
if mpi
r = MPI.bcast(r, 0, MPI.COMM_WORLD)
end
#ratio = min(1,exp(Snew-Sold))
if r > ratio
substitute_U!(U,Uold)
return false
else
return true
end
end
function U_update!(U,p,ϵ,Δτ,Dim,gauge_action)
temps = get_temporary_gaugefields(gauge_action)
temp1 = temps[1]
temp2 = temps[2]
expU = temps[3]
W = temps[4]
for μ=1:Dim
exptU!(expU,ϵ*Δτ,p[μ],[temp1,temp2])
mul!(W,expU,U[μ])
substitute_U!(U[μ],W)
end
end
function P_update!(U,p,ϵ,Δτ,Dim,gauge_action,temp1,temp2) # p -> p +factor*U*dSdUμ
NC = U[1].NC
temp = temp1
dSdUμ = temp2
factor = -ϵ*Δτ/(NC)
for μ=1:Dim
calc_dSdUμ!(dSdUμ,gauge_action,μ,U)
mul!(temp,U[μ],dSdUμ) # U*dSdUμ
Traceless_antihermitian_add!(p[μ],factor,temp)
end
end
function HMC_test_4D(NX,NY,NZ,NT,NC,β)
Dim = 4
Nwing = 0
Random.seed!(123)
if mpi
PEs = pes#(1,1,1,2)
U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "hot",mpi=true,PEs = PEs,mpiinit = false)
else
U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "hot")
end
if get_myrank(U) == 0
println(typeof(U))
end
temp1 = similar(U[1])
temp2 = similar(U[1])
if Dim == 4
comb = 6 #4*3/2
elseif Dim == 3
comb = 3
elseif Dim == 2
comb = 1
else
error("dimension $Dim is not supported")
end
factor = 1/(comb*U[1].NV*U[1].NC)
@time plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
if get_myrank(U) == 0
println("0 plaq_t = $plaq_t")
end
poly = calculate_Polyakov_loop(U,temp1,temp2)
if get_myrank(U) == 0
println("0 polyakov loop = $(real(poly)) $(imag(poly))")
end
gauge_action = GaugeAction(U)
plaqloop = make_loops_fromname("plaquette")
append!(plaqloop,plaqloop')
β = β/2
push!(gauge_action,β,plaqloop)
#show(gauge_action)
p = initialize_TA_Gaugefields(U) #This is a traceless-antihermitian gauge fields. This has NC^2-1 real coefficients.
Uold = similar(U)
substitute_U!(Uold,U)
MDsteps = 100
temp1 = similar(U[1])
temp2 = similar(U[1])
comb = 6
factor = 1/(comb*U[1].NV*U[1].NC)
numaccepted = 0
numtrj = 100
for itrj = 1:numtrj
t = @timed begin
accepted = MDstep!(gauge_action,U,p,MDsteps,Dim,Uold,temp1,temp2)
end
if get_myrank(U) == 0
println("elapsed time for MDsteps: $(t.time) [s]")
end
numaccepted += ifelse(accepted,1,0)
#plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
#println("$itrj plaq_t = $plaq_t")
if itrj % 10 == 0
plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
if get_myrank(U) == 0
println("$itrj plaq_t = $plaq_t")
end
poly = calculate_Polyakov_loop(U,temp1,temp2)
if get_myrank(U) == 0
println("$itrj polyakov loop = $(real(poly)) $(imag(poly))")
println("acceptance ratio ",numaccepted/itrj)
end
end
end
return plaq_t,numaccepted/numtrj
end
function main()
β = 5.7
NX = 8
NY = 8
NZ = 8
NT = 8
NC = 3
HMC_test_4D(NX,NY,NZ,NT,NC,β)
end
main()
```
Also we can implement higher-form gauge fields.
# Utilities
## Data structure
We can access the gauge field defined on the bond between two neigbohr points.
In 4D system, the gauge field is like ```u[ic,jc,ix,iy,iz,it]```.
There are four directions in 4D system. Gaugefields.jl uses the array like:
```julia
NX = 4
NY = 4
NZ = 4
NT = 4
NC = 3
Nwing = 1
Dim = 4
U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "cold")
```
In the later exaples, we use, ``mu=1`` and ``u=U[mu]`` as an example.
## Hermitian conjugate (Adjoint operator)
If you want to get the hermitian conjugate of the gauge fields, you can do like
```julia
u'
```
This is evaluated with the lazy evaluation.
So there is no memory copy.
This returms $U_\mu^\dagger$ for all sites.
## Shift operator
If you want to shift the gauge fields, you can do like
```julia
shifted_u = shift_U(u, shift)
```
This is also evaluated with the lazy evaluation.
Here ``shift`` is ``shift=(1,0,0,0)`` for example.
## Evaluate Wilson links
Here the example to evaluate the Wilson links.
```julia
using Gaugefields
using Wilsonloop
function main()
NX = 4
NY = 4
NZ = 4
NT = 4
Nwing = 0
NC = 3
U1 = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "hot")
temps = typeof(U1[1])[]
for i=1:10
push!(temps,similar(U1[1]))
end
loop = [(1,+1),(2,+1),(1,-1),(2,-1)]
println(loop)
w = Wilsonline(loop)
println("P: ")
show(w)
Uloop = similar(U1[1])
Gaugefields.evaluate_gaugelinks!(Uloop, w, U1, temps)
display(Uloop[:,:,1,1,1,1])
end
main()
```
## matrix-field matrix-field product
If you want to calculate the matrix-matrix multiplicaetion on each lattice site, you can do like
As a mathematical expression, for matrix-valued fields ``A(n), B(n)``,
we define "matrix-field matrix-field product" as,
```math
[A(n)B(n)]_{ij} = \sum_k [A(n)]_{ik} [B(n)]_{kj}
```
for all site index n.
In our package, this is expressed as,
```julia
mul!(C,A,B)
```
which means ```C = A*B``` on each lattice site.
Here ``A, B, C`` are same type of ``u``.
## Trace operation
If you want to calculate the trace of the gauge field, you can do like
```julia
tr(A)
```
It is useful to evaluation actions.
This trace operation summing up all indecis, spacetime and color.
# Applications
This package and Wilsonloop.jl enable you to perform several calcurations.
Here we demonstrate them.
Some of them will be simplified in LatticeQCD.jl.
## Wilson loops
We develop [Wilsonloop.jl](https://github.com/akio-tomiya/Wilsonloop.jl.git), which is useful to calculate Wilson loops.
If you want to use this, please install like
```
add Wilsonloop.jl
```
For example, if you want to calculate the following quantity:
```math
U_{1}(n)U_{2}(n+\hat{1}) U^{\dagger}_{1}(n+\hat{2}) U^{\dagger}_2(n)
```
or
```math
U_{1}(n)U_{2}(n+\hat{1}) U^{\dagger}_{1}(n+\hat{2}) U^{\dagger}_2(n) e^{-2\pi B_{12}(n) / N} ,
```
which is Z(Nc) 1-form gauge invariant [[arXiv:2303.10977 [hep-lat]](https://arxiv.org/abs/2303.10977)].
You can use Wilsonloop.jl as follows
```julia
using Wilsonloop
loop = [(1,1),(2,1),(1,-1),(2,-1)]
w = Wilsonline(loop)
```
The output is ```L"$U_{1}(n)U_{2}(n+e_{1})U^{\dagger}_{1}(n+e_{2})U^{\dagger}_{2}(n)$"```.
Then, you can evaluate this loop with the use of the Gaugefields.jl like:
```julia
using LinearAlgebra
NX = 4
NY = 4
NZ = 4
NT = 4
NC = 3
Nwing = 1
Dim = 4
U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "cold")
temp1 = similar(U[1])
V = similar(U[1])
evaluate_gaugelinks!(V,w,U,[temp1])
println(tr(V))
```
For example, if you want to calculate the clover operators, you can define like:
```julia
function make_cloverloop(μ,ν,Dim)
loops = Wilsonline{Dim}[]
loop_righttop = Wilsonline([(μ,1),(ν,1),(μ,-1),(ν,-1)],Dim = Dim) # Pmunu
push!(loops,loop_righttop)
loop_rightbottom = Wilsonline([(ν,-1),(μ,1),(ν,1),(μ,-1)],Dim = Dim) # Qmunu
push!(loops,loop_rightbottom)
loop_leftbottom= Wilsonline([(μ,-1),(ν,-1),(μ,1),(ν,1)],Dim = Dim) # Rmunu
push!(loops,loop_leftbottom)
loop_lefttop = Wilsonline([(ν,1),(μ,-1),(ν,-1),(μ,1)],Dim = Dim) # Smunu
push!(loops,loop_lefttop)
return loops
end
```
The energy density defined in the paper (Ramos and Sint, [Eur. Phys. J. C (2016) 76:15](https://link.springer.com/article/10.1140%2Fepjc%2Fs10052-015-3831-9)) can be calculated as follows. Note: the coefficient in the equation (3.40) in the preprint version is wrong.
```julia
function make_clover(G,U,temps,Dim)
temp1 = temps[1]
temp2 = temps[2]
temp3 = temps[3]
for μ=1:Dim
for ν=1:Dim
if μ == ν
continue
end
loops = make_cloverloop(μ,ν,Dim)
evaluate_gaugelinks!(temp3,loops,U,[temp1,temp2])
Traceless_antihermitian!(G[μ,ν],temp3)
end
end
end
function calc_energydensity(G,U,temps,Dim)
temp1 = temps[1]
s = 0
for μ=1:Dim
for ν=1:Dim
if μ == ν
continue
end
mul!(temp1,G[μ,ν],G[μ,ν])
s += -real(tr(temp1))/2
end
end
return s/(4^2*U[1].NV)
end
```
Then, we can calculate the energy density:
```julia
function test(NX,NY,NZ,NT,β,NC)
Dim = 4
Nwing = 1
U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "cold")
filename = "./conf_00000010.txt"
L = [NX,NY,NZ,NT]
load_BridgeText!(filename,U,L,NC) # We load a configuration from a file.
temp1 = similar(U[1])
temp2 = similar(U[1])
temp3 = similar(U[1])
println("Make clover operator")
G = Array{typeof(u1),2}(undef,Dim,Dim)
for μ=1:Dim
for ν=1:Dim
G[μ,ν] = similar(U[1])
end
end
comb = 6
factor = 1/(comb*U[1].NV*U[1].NC)
@time plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
println("plaq_t = $plaq_t")
g = Gradientflow(U,eps = 0.01)
for itrj=1:100
flow!(U,g)
make_clover(G,U,[temp1,temp2,temp3],Dim)
E = calc_energydensity(G,U,[temp1,temp2,temp3],Dim)
plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
println("$itrj $(itrj*0.01) plaq_t = $plaq_t , E = $E")
end
end
NX = 8
NY = 8
NZ = 8
NT = 8
β = 5.7
NC = 3
test(NX,NY,NZ,NT,β,NC)
```
## Calculating actions
We can calculate actions from this packages with fixed gauge fields U.
We introduce the concenpt "Scalar-valued neural network", which is S(U) -> V, where U and V are gauge fields.
```julia
using Gaugefields
using LinearAlgebra
function test1()
NX = 4
NY = 4
NZ = 4
NT = 4
Nwing = 1
Dim = 4
NC = 3
U =Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "cold")
gauge_action = GaugeAction(U) #empty network
plaqloop = make_loops_fromname("plaquette") #This is a plaquette loops.
append!(plaqloop,plaqloop') #We need hermitian conjugate loops for making the action real.
β = 1 #This is a coefficient.
push!(gauge_action,β,plaqloop)
show(gauge_action)
Uout = evaluate_Gaugeaction_untraced(gauge_action,U)
println(tr(Uout))
end
test1()
```
The output is
```
----------------------------------------------
Structure of the actions for Gaugefields
num. of terms: 1
-------------------------------
1-st term:
coefficient: 1.0
-------------------------
1-st loop
L"$U_{1}(n)U_{2}(n+e_{1})U^{\dagger}_{1}(n+e_{2})U^{\dagger}_{2}(n)$"
2-nd loop
L"$U_{1}(n)U_{3}(n+e_{1})U^{\dagger}_{1}(n+e_{3})U^{\dagger}_{3}(n)$"
3-rd loop
L"$U_{1}(n)U_{4}(n+e_{1})U^{\dagger}_{1}(n+e_{4})U^{\dagger}_{4}(n)$"
4-th loop
L"$U_{2}(n)U_{3}(n+e_{2})U^{\dagger}_{2}(n+e_{3})U^{\dagger}_{3}(n)$"
5-th loop
L"$U_{2}(n)U_{4}(n+e_{2})U^{\dagger}_{2}(n+e_{4})U^{\dagger}_{4}(n)$"
6-th loop
L"$U_{3}(n)U_{4}(n+e_{3})U^{\dagger}_{3}(n+e_{4})U^{\dagger}_{4}(n)$"
7-th loop
L"$U_{2}(n)U_{1}(n+e_{2})U^{\dagger}_{2}(n+e_{1})U^{\dagger}_{1}(n)$"
8-th loop
L"$U_{3}(n)U_{1}(n+e_{3})U^{\dagger}_{3}(n+e_{1})U^{\dagger}_{1}(n)$"
9-th loop
L"$U_{4}(n)U_{1}(n+e_{4})U^{\dagger}_{4}(n+e_{1})U^{\dagger}_{1}(n)$"
10-th loop
L"$U_{3}(n)U_{2}(n+e_{3})U^{\dagger}_{3}(n+e_{2})U^{\dagger}_{2}(n)$"
11-th loop
L"$U_{4}(n)U_{2}(n+e_{4})U^{\dagger}_{4}(n+e_{2})U^{\dagger}_{2}(n)$"
12-th loop
L"$U_{4}(n)U_{3}(n+e_{4})U^{\dagger}_{4}(n+e_{3})U^{\dagger}_{3}(n)$"
-------------------------
----------------------------------------------
9216.0 + 0.0im
```
## Fractional topological charge
```julia
function calculate_topological_charge_plaq(U::Array{T,1}, B::Array{T,2}, temp_UμνTA, temps) where {T}
UμνTA = temp_UμνTA
numofloops = calc_UμνTA!(UμνTA, "plaq", U, B, temps)
Q = calc_Q(UμνTA, numofloops, U)
return Q
end
function calculate_topological_charge_clover(U::Array{T,1}, B::Array{T,2}, temp_UμνTA, temps) where {T}
UμνTA = temp_UμνTA
numofloops = calc_UμνTA!(UμνTA, "clover", U, B, temps)
Q = calc_Q(UμνTA, numofloops, U)
return Q
end
function calculate_topological_charge_improved(
U::Array{T,1},
B::Array{T,2},
temp_UμνTA,
Qclover,
temps,
) where {T}
UμνTA = temp_UμνTA
numofloops = calc_UμνTA!(UμνTA, "rect", U, B, temps)
Qrect = 2 * calc_Q(UμνTA, numofloops, U)
c1 = -1 / 12
c0 = 5 / 3
Q = c0 * Qclover + c1 * Qrect
return Q
end
function calc_UμνTA!(
temp_UμνTA,
name::String,
U::Array{T,1},
B::Array{T,2},
temps,
) where {NC,Dim,T<:AbstractGaugefields{NC,Dim}}
loops_μν, numofloops = calc_loopset_μν_name(name, Dim)
calc_UμνTA!(temp_UμνTA, loops_μν, U, B, temps)
return numofloops
end
function calc_UμνTA!(
temp_UμνTA,
loops_μν,
U::Array{T,1},
B::Array{T,2},
temps,
) where {NC,Dim,T<:AbstractGaugefields{NC,Dim}}
UμνTA = temp_UμνTA
for μ = 1:Dim
for ν = 1:Dim
if ν == μ
continue
end
evaluate_gaugelinks!(temps[1], loops_μν[μ, ν], U, B, temps[2:6])
Traceless_antihermitian!(UμνTA[μ, ν], temps[1])
end
end
return
end
#=
implementation of topological charge is based on
https://arxiv.org/abs/1509.04259
=#
function calc_Q(UμνTA, numofloops, U::Array{<:AbstractGaugefields{NC,Dim},1}) where {NC,Dim}
Q = 0.0
if Dim == 4
ε(μ, ν, ρ, σ) = epsilon_tensor(μ, ν, ρ, σ)
else
error("Dimension $Dim is not supported")
end
for μ = 1:Dim
for ν = 1:Dim
if ν == μ
continue
end
Uμν = UμνTA[μ, ν]
for ρ = 1:Dim
for σ = 1:Dim
if ρ == σ
continue
end
Uρσ = UμνTA[ρ, σ]
s = tr(Uμν, Uρσ)
Q += ε(μ, ν, ρ, σ) * s / numofloops^2
end
end
end
end
return -Q / (32 * (π^2))
end
#topological charge
function epsilon_tensor(mu::Int, nu::Int, rho::Int, sigma::Int)
sign = 1 # (3) 1710.09474 extended epsilon tensor
if mu < 0
sign *= -1
mu = -mu
end
if nu < 0
sign *= -1
nu = -nu
end
if rho < 0
sign *= -1
rh = -rho
end
if sigma < 0
sign *= -1
sigma = -sigma
end
epsilon = zeros(Int, 4, 4, 4, 4)
epsilon[1, 2, 3, 4] = 1
epsilon[1, 2, 4, 3] = -1
epsilon[1, 3, 2, 4] = -1
epsilon[1, 3, 4, 2] = 1
epsilon[1, 4, 2, 3] = 1
epsilon[1, 4, 3, 2] = -1
epsilon[2, 1, 3, 4] = -1
epsilon[2, 1, 4, 3] = 1
epsilon[2, 3, 1, 4] = 1
epsilon[2, 3, 4, 1] = -1
epsilon[2, 4, 1, 3] = -1
epsilon[2, 4, 3, 1] = 1
epsilon[3, 1, 2, 4] = 1
epsilon[3, 1, 4, 2] = -1
epsilon[3, 2, 1, 4] = -1
epsilon[3, 2, 4, 1] = 1
epsilon[3, 4, 1, 2] = 1
epsilon[3, 4, 2, 1] = -1
epsilon[4, 1, 2, 3] = -1
epsilon[4, 1, 3, 2] = 1
epsilon[4, 2, 1, 3] = 1
epsilon[4, 2, 3, 1] = -1
epsilon[4, 3, 1, 2] = -1
epsilon[4, 3, 2, 1] = 1
return epsilon[mu, nu, rho, sigma] * sign
end
function calc_loopset_μν_name(name, Dim)
loops_μν = Array{Vector{Wilsonline{Dim}},2}(undef, Dim, Dim)
if name == "plaq"
numofloops = 1
for μ = 1:Dim
for ν = 1:Dim
loops_μν[μ, ν] = Wilsonline{Dim}[]
if ν == μ
continue
end
plaq = make_plaq(μ, ν, Dim = Dim)
push!(loops_μν[μ, ν], plaq)
end
end
elseif name == "clover"
numofloops = 4
for μ = 1:Dim
for ν = 1:Dim
loops_μν[μ, ν] = Wilsonline{Dim}[]
if ν == μ
continue
end
loops_μν[μ, ν] = make_cloverloops_topo(μ, ν, Dim = Dim)
end
end
elseif name == "rect"
numofloops = 8
for μ = 1:4
for ν = 1:4
if ν == μ
continue
end
loops = Wilsonline{Dim}[]
loop_righttop = Wilsonline([(μ, 2), (ν, 1), (μ, -2), (ν, -1)])
loop_lefttop = Wilsonline([(ν, 1), (μ, -2), (ν, -1), (μ, 2)])
loop_rightbottom = Wilsonline([(ν, -1), (μ, 2), (ν, 1), (μ, -2)])
loop_leftbottom = Wilsonline([(μ, -2), (ν, -1), (μ, 2), (ν, 1)])
push!(loops, loop_righttop)
push!(loops, loop_lefttop)
push!(loops, loop_rightbottom)
push!(loops, loop_leftbottom)
loop_righttop = Wilsonline([(μ, 1), (ν, 2), (μ, -1), (ν, -2)])
loop_lefttop = Wilsonline([(ν, 2), (μ, -1), (ν, -2), (μ, 1)])
loop_rightbottom = Wilsonline([(ν, -2), (μ, 1), (ν, 2), (μ, -1)])
loop_leftbottom = Wilsonline([(μ, -1), (ν, -2), (μ, 1), (ν, 2)])
push!(loops, loop_righttop)
push!(loops, loop_lefttop)
push!(loops, loop_rightbottom)
push!(loops, loop_leftbottom)
loops_μν[μ, ν] = loops
end
end
else
error("$name is not supported")
end
return loops_μν, numofloops
end
function make_cloverloops_topo(μ, ν; Dim = 4)
loops = Wilsonline{Dim}[]
loop_righttop = Wilsonline([(μ, 1), (ν, 1), (μ, -1), (ν, -1)])
loop_lefttop = Wilsonline([(ν, 1), (μ, -1), (ν, -1), (μ, 1)])
loop_rightbottom = Wilsonline([(ν, -1), (μ, 1), (ν, 1), (μ, -1)])
loop_leftbottom = Wilsonline([(μ, -1), (ν, -1), (μ, 1), (ν, 1)])
push!(loops, loop_righttop)
push!(loops, loop_lefttop)
push!(loops, loop_rightbottom)
push!(loops, loop_leftbottom)
return loops
end
```
We can calculate the topological charge as
```Qplaq = calculate_topological_charge_plaq(U,B,temp_UμνTA,temps[1:6])```,
```Qclover = calculate_topological_charge_clover(U,B,temp_UμνTA,temps[1:6])```,
```Qimproved= calculate_topological_charge_improved(U,B,temp_UμνTA,Qclover,temps[1:6])```.
# How to calculate derivatives
We can easily calculate the matrix derivative of the actions. The matrix derivative is defined as
```math
[\frac{\partial S}{\partial U_{\mu}(n)}]_{ij} = \frac{\partial S}{\partial U_{\mu,ji}(n)}
```
We can calculate this like
```julia
dSdUμ = calc_dSdUμ(gauge_action,μ,U)
```
or
```julia
calc_dSdUμ!(dSdUμ,gauge_action,μ,U)
```
## Hybrid Monte Carlo
With the use of the matrix derivative, we can do the Hybrid Monte Carlo method.
The simple code is as follows.
```julia
using Gaugefields
using LinearAlgebra
function MDtest!(gauge_action,U,Dim)
p = initialize_TA_Gaugefields(U) #This is a traceless-antihermitian gauge fields. This has NC^2-1 real coefficients.
Uold = similar(U)
substitute_U!(Uold,U)
MDsteps = 100
temp1 = similar(U[1])
temp2 = similar(U[1])
comb = 6
factor = 1/(comb*U[1].NV*U[1].NC)
numaccepted = 0
numtrj = 100
for itrj = 1:numtrj
accepted = MDstep!(gauge_action,U,p,MDsteps,Dim,Uold)
numaccepted += ifelse(accepted,1,0)
plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
println("$itrj plaq_t = $plaq_t")
println("acceptance ratio ",numaccepted/itrj)
end
end
```
We define the functions as
```julia
function calc_action(gauge_action,U,p)
NC = U[1].NC
Sg = -evaluate_GaugeAction(gauge_action,U)/NC #evaluate_GaugeAction(gauge_action,U) = tr(evaluate_Gaugeaction_untraced(gauge_action,U))
Sp = p*p/2
S = Sp + Sg
return real(S)
end
function MDstep!(gauge_action,U,p,MDsteps,Dim,Uold)
Δτ = 1/MDsteps
gauss_distribution!(p)
Sold = calc_action(gauge_action,U,p)
substitute_U!(Uold,U)
for itrj=1:MDsteps
U_update!(U,p,0.5,Δτ,Dim,gauge_action)
P_update!(U,p,1.0,Δτ,Dim,gauge_action)
U_update!(U,p,0.5,Δτ,Dim,gauge_action)
end
Snew = calc_action(gauge_action,U,p)
println("Sold = $Sold, Snew = $Snew")
println("Snew - Sold = $(Snew-Sold)")
ratio = min(1,exp(-Snew+Sold))
if rand() > ratio
substitute_U!(U,Uold)
return false
else
return true
end
end
function U_update!(U,p,ϵ,Δτ,Dim,gauge_action)
temps = get_temporary_gaugefields(gauge_action)
temp1 = temps[1]
temp2 = temps[2]
expU = temps[3]
W = temps[4]
for μ=1:Dim
exptU!(expU,ϵ*Δτ,p[μ],[temp1,temp2])
mul!(W,expU,U[μ])
substitute_U!(U[μ],W)
end
end
function P_update!(U,p,ϵ,Δτ,Dim,gauge_action) # p -> p +factor*U*dSdUμ
NC = U[1].NC
temps = get_temporary_gaugefields(gauge_action)
dSdUμ = temps[end]
factor = -ϵ*Δτ/(NC)
for μ=1:Dim
calc_dSdUμ!(dSdUμ,gauge_action,μ,U)
mul!(temps[1],U[μ],dSdUμ) # U*dSdUμ
Traceless_antihermitian_add!(p[μ],factor,temps[1])
end
end
```
Then, we can do the HMC:
```julia
function test1()
NX = 4
NY = 4
NZ = 4
NT = 4
Nwing = 1
Dim = 4
NC = 3
U =Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "cold")
gauge_action = GaugeAction(U)
plaqloop = make_loops_fromname("plaquette")
append!(plaqloop,plaqloop') # add hermitian conjugate
β = 5.7/2 # real part; re[p] = (p+p')/2
push!(gauge_action,β,plaqloop)
show(gauge_action)
MDtest!(gauge_action,U,Dim)
end
test1()
```
## Stout smearing
We can use stout smearing.
```math
U_{\rm fat} = {\cal F}(U)
```
The smearing is regarded as gauge covariant neural networks [Tomiya and Nagai, arXiv:2103.11965](https://arxiv.org/abs/2103.11965).
The network is constructed as follows.
```julia
NX = 4
NY = 4
NZ = 4
NT = 4
L = [NX,NY,NZ,NT]
nn = CovNeuralnet()
ρ = [0.1]
layername = ["plaquette"]
st = STOUT_Layer(layername,ρ,L)
push!(nn,st)
show(nn)
```
The output is
```
num. of layers: 1
- 1-st layer: STOUT
num. of terms: 1
-------------------------------
1-st term:
coefficient: 0.1
-------------------------
1-st loop
L"$U_{1}(n)U_{2}(n+e_{1})U^{\dagger}_{1}(n+e_{2})U^{\dagger}_{2}(n)$"
2-nd loop
L"$U_{1}(n)U_{3}(n+e_{1})U^{\dagger}_{1}(n+e_{3})U^{\dagger}_{3}(n)$"
3-rd loop
L"$U_{1}(n)U_{4}(n+e_{1})U^{\dagger}_{1}(n+e_{4})U^{\dagger}_{4}(n)$"
4-th loop
L"$U_{2}(n)U_{3}(n+e_{2})U^{\dagger}_{2}(n+e_{3})U^{\dagger}_{3}(n)$"
5-th loop
L"$U_{2}(n)U_{4}(n+e_{2})U^{\dagger}_{2}(n+e_{4})U^{\dagger}_{4}(n)$"
6-th loop
L"$U_{3}(n)U_{4}(n+e_{3})U^{\dagger}_{3}(n+e_{4})U^{\dagger}_{4}(n)$"
-------------------------
```
Since we ragard the smearing as the neural networks, we can calculate the derivative with the use of the back propergation techques.
```math
\frac{\partial S}{\partial U} = G \left( \frac{dS}{dU_{\rm fat}},U \right)
```
For example,
```julia
using Gaugefields
using Wilsonloop
function stoutsmearing(NX,NY,NZ,NT,NC)
Nwing = 1
Dim = 4
U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "hot")
L = [NX,NY,NZ,NT]
comb = 6
factor = 1/(comb*U[1].NV*U[1].NC)
temp1 = similar(U[1])
temp2 = similar(U[1])
plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
println(" plaq_t = $plaq_t")
nn = CovNeuralnet()
ρ = [0.1]
layername = ["plaquette"]
st = STOUT_Layer(layername,ρ,L)
push!(nn,st)
show(nn)
@time Uout,Uout_multi,_ = calc_smearedU(U,nn)
plaq_t = calculate_Plaquette(Uout,temp1,temp2)*factor
println("plaq_t = $plaq_t")
gauge_action = GaugeAction(U)
plaqloop = make_loops_fromname("plaquette")
append!(plaqloop,plaqloop')# add hermitian conjugate
β = 5.7/2 # real part; re[p] = (p+p')/2
push!(gauge_action,β,plaqloop)
μ = 1
dSdUμ = similar(U)
for μ=1:Dim
dSdUμ[μ] = calc_dSdUμ(gauge_action,μ,U)
end
@time dSdUbareμ = back_prop(dSdUμ,nn,Uout_multi,U)
end
NX = 4
NY = 4
NZ = 4
NT = 4
NC = 3
stoutsmearing(NX,NY,NZ,NT,NC)
```
# HMC with stout smearing
With the use of the derivatives, we can do the HMC with the stout smearing.
The code is shown as follows
```julia
using Gaugefields
using LinearAlgebra
function MDtest!(gauge_action,U,Dim,nn)
p = initialize_TA_Gaugefields(U) #This is a traceless-antihermitian gauge fields. This has NC^2-1 real coefficients.
Uold = similar(U)
dSdU = similar(U)
substitute_U!(Uold,U)
MDsteps = 100
temp1 = similar(U[1])
temp2 = similar(U[1])
comb = 6
factor = 1/(comb*U[1].NV*U[1].NC)
numaccepted = 0
numtrj = 100
for itrj = 1:numtrj
accepted = MDstep!(gauge_action,U,p,MDsteps,Dim,Uold,nn,dSdU)
numaccepted += ifelse(accepted,1,0)
plaq_t = calculate_Plaquette(U,temp1,temp2)*factor
println("$itrj plaq_t = $plaq_t")
println("acceptance ratio ",numaccepted/itrj)
end
end
function calc_action(gauge_action,U,p)
NC = U[1].NC
Sg = -evaluate_GaugeAction(gauge_action,U)/NC #evaluate_GaugeAction(gauge_action,U) = tr(evaluate_GaugeAction_untraced(gauge_action,U))
Sp = p*p/2
S = Sp + Sg
return real(S)
end
function MDstep!(gauge_action,U,p,MDsteps,Dim,Uold,nn,dSdU)
Δτ = 1/MDsteps
gauss_distribution!(p)
Uout,Uout_multi,_ = calc_smearedU(U,nn)
Sold = calc_action(gauge_action,Uout,p)
substitute_U!(Uold,U)
for itrj=1:MDsteps
U_update!(U,p,0.5,Δτ,Dim,gauge_action)
P_update!(U,p,1.0,Δτ,Dim,gauge_action,dSdU,nn)
U_update!(U,p,0.5,Δτ,Dim,gauge_action)
end
Uout,Uout_multi,_ = calc_smearedU(U,nn)
Snew = calc_action(gauge_action,Uout,p)
println("Sold = $Sold, Snew = $Snew")
println("Snew - Sold = $(Snew-Sold)")
accept = exp(Sold - Snew) >= rand()
if accept != true #rand() > ratio
substitute_U!(U,Uold)
return false
else
return true
end
end
function U_update!(U,p,ϵ,Δτ,Dim,gauge_action)
temps = get_temporary_gaugefields(gauge_action)
temp1 = temps[1]
temp2 = temps[2]
expU = temps[3]
W = temps[4]
for μ=1:Dim
exptU!(expU,ϵ*Δτ,p[μ],[temp1,temp2])
mul!(W,expU,U[μ])
substitute_U!(U[μ],W)
end
end
function P_update!(U,p,ϵ,Δτ,Dim,gauge_action,dSdU,nn) # p -> p +factor*U*dSdUμ
NC = U[1].NC
factor = -ϵ*Δτ/(NC)
temps = get_temporary_gaugefields(gauge_action)
Uout,Uout_multi,_ = calc_smearedU(U,nn)
for μ=1:Dim
calc_dSdUμ!(dSdU[μ],gauge_action,μ,Uout)
end
dSdUbare = back_prop(dSdU,nn,Uout_multi,U)
for μ=1:Dim
mul!(temps[1],U[μ],dSdUbare[μ]) # U*dSdUμ
Traceless_antihermitian_add!(p[μ],factor,temps[1])
end
end
function test1()
NX = 4
NY = 4
NZ = 4
NT = 4
Nwing = 1
Dim = 4
NC = 3
U =Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "hot")
gauge_action = GaugeAction(U)
plaqloop = make_loops_fromname("plaquette")
append!(plaqloop,plaqloop')
β = 5.7/2
push!(gauge_action,β,plaqloop)
show(gauge_action)
L = [NX,NY,NZ,NT]
nn = CovNeuralnet()
ρ = [0.1]
layername = ["plaquette"]
st = STOUT_Layer(layername,ρ,L)
push!(nn,st)
MDtest!(gauge_action,U,Dim,nn)
end
test1()
```
# Acknowledgment
If you write a paper using this package, please refer this code.
BibTeX citation is following
```
@article{Nagai:2024yaf,
author = "Nagai, Yuki and Tomiya, Akio",
title = "{JuliaQCD: Portable lattice QCD package in Julia language}",
eprint = "2409.03030",
archivePrefix = "arXiv",
primaryClass = "hep-lat",
month = "9",
year = "2024"
}
```
and the paper is [arXiv:2409.03030](https://arxiv.org/abs/2409.03030).