https://github.com/SciML/Surrogates.jl

Surrogate modeling and optimization for scientific machine learning (SciML)
https://github.com/SciML/Surrogates.jl

automatic-differentiation differential-equations high-performance-computing julia optimization scientific-machine-learning sciml surrogate surrogate-based-optimization surrogate-models surrogates

Last synced: 3 months ago
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Surrogate modeling and optimization for scientific machine learning (SciML)

• Host: GitHub
• URL: https://github.com/SciML/Surrogates.jl
• Owner: SciML
• License: other
• Created: 2019-05-08T18:27:16.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2024-04-08T03:36:23.000Z (7 months ago)
• Last Synced: 2024-04-14T01:46:59.575Z (7 months ago)
• Topics: automatic-differentiation, differential-equations, high-performance-computing, julia, optimization, scientific-machine-learning, sciml, surrogate, surrogate-based-optimization, surrogate-models, surrogates
• Language: Julia
• Homepage: https://docs.sciml.ai/Surrogates/stable/
• Size: 263 MB
• Stars: 312
• Watchers: 13
• Forks: 69
• Open Issues: 36
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE.md

Awesome Lists containing this project

• awesome-sciml - SciML/Surrogates.jl: Surrogate modeling and optimization for scientific machine learning (SciML)

README

        
## Surrogates.jl

[![Join the chat at https://julialang.zulipchat.com #sciml-bridged](https://img.shields.io/static/v1?label=Zulip&message=chat&color=9558b2&labelColor=389826)](https://julialang.zulipchat.com/#narrow/stream/279055-sciml-bridged)

[![Global Docs](https://img.shields.io/badge/docs-SciML-blue.svg)](https://docs.sciml.ai/Surrogates/stable/)

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[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.12571718.svg)](https://doi.org/10.5281/zenodo.12571718)

A surrogate model is an approximation method that mimics the behavior of a computationally

expensive simulation. In more mathematical terms: suppose we are attempting to optimize a function

`f(p)`, but each calculation of `f` is very expensive. It may be the case we need to solve a PDE for each point or use advanced numerical linear algebra machinery, which is usually costly. The idea is then to develop a surrogate model `g` which approximates `f` by training on previous data collected from evaluations of `f`.

The construction of a surrogate model can be seen as a three-step process:

 1. Sample selection

 2. Construction of the surrogate model

 3. Surrogate optimization

Sampling can be done through [QuasiMonteCarlo.jl](https://github.com/SciML/QuasiMonteCarlo.jl), all the functions available there can be used in Surrogates.jl.

## ALL the currently available surrogate models:

  - Kriging

  - Kriging using Stheno

  - Radial Basis

  - Wendland

  - Linear

  - Second Order Polynomial

  - Support Vector Machines (Wait for LIBSVM resolution)

  - Neural Networks

  - Random Forests

  - Lobachevsky

  - Inverse-distance

  - Polynomial expansions

  - Variable fidelity

  - Mixture of experts (Waiting GaussianMixtures package to work on v1.5)

  - Earth

  - Gradient Enhanced Kriging

## ALL the currently available optimization methods:

  - SRBF

  - LCBS

  - DYCORS

  - EI

  - SOP

  - Multi-optimization: SMB and RTEA

## Installing Surrogates package

```julia

using Pkg

Pkg.add("Surrogates")

```