awesome-sciml

Awesome-spatial-temporal-scientific-machine-learning-data-mining-packages. Julia and Python resources on spatial and temporal data mining. Mathematical epidemiology as an application. Most about package information. Data Sources Links and Epidemic Repos are also included.
https://github.com/Mathepia/awesome-sciml

Last synced: 7 days ago
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4.4 Polynomials

4.3.2. Interpolations and Approximations

3.1. Differentiation, Quadrature and Tensor computation

3.1.1. Auto Differentiation

3.1.2. Quadrature

3.1.3. Matrix and Tensor computation

3.1.4.Platforms, CPU, GPU and TPU

3.2. Optimization

3.2.5. First Order Methods

3.1.4.Platforms, CPU, GPU and TPU

3.2.1. Metaheuristic

3.2.2. Evolution Stragegy

3.2.3. Genetic Algorithms

3.2.4. Nonconvex

JuliaNonconvex/Nonconvex.jl: Toolbox for non-convex constrained optimization.

3.3. Optimal Control

3.2.5. First Order Methods

3.4. Bayesian Inference

3.2.5. First Order Methods

3.4.1. MCMC

ecosyste.ms

Data

Tools

Indexes

Applications

Experiments

Awesome

awesome-sciml

4.4 Polynomials

<span id="head72">4.3.2. Interpolations and Approximations</span>

<span id="head11">3.1. Differentiation, Quadrature and Tensor computation</span>

<span id="head12">3.1.1. Auto Differentiation</span>

<span id="head14">3.1.2. Quadrature</span>

<span id="head17">3.1.3. Matrix and Tensor computation</span>

<span id="head23">3.1.4.Platforms, CPU, GPU and TPU</span>

<span id="head24">3.2. Optimization</span>

3.2.5. First Order Methods

<span id="head23">3.1.4.Platforms, CPU, GPU and TPU</span>

<span id="head25">3.2.1. Metaheuristic</span>

<span id="head26">3.2.2. Evolution Stragegy</span>

<span id="head27">3.2.3. Genetic Algorithms</span>

<span id="head28">3.2.4. Nonconvex</span>

<span id="head29">3.3. Optimal Control</span>

3.2.5. First Order Methods

<span id="head30">3.4. Bayesian Inference</span>

3.2.5. First Order Methods

<span id="head31">3.4.1. MCMC</span>