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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: about 6 hours ago
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Programming Languages

Julia 217 Python 95 Jupyter Notebook 26 TeX 3 C++ 3 JavaScript 3 R 3 TypeScript 1 MATLAB 1

Categories

3.1. Differentiation, Quadrature and Tensor computation 86 3.4. Bayesian Inference 81 3.5. Machine Learning and Deep Learning 44 3.2. Optimization 31 3.6. Probablistic Machine Learning and Deep Learning 24 3.7. Differential Equations and Scientific Computation 23 3.3. Optimal Control 19 4.4 Polynomials 18 2.2. (Deep Learning based) Time Series Analysis 17 4.3. Roots, Intepolations 16 3.8. Scientific Machine Learning (Differential Equation and ML) 15 2.4. Data Visualization 14 3.9. Data Driven Methods (Equation Searching Methods) 13 3.12. Agents, Graph and Networks 10 Smoothing 7 Outlier Detection 7 4.1. Symbolic Computation 7 2.3. Survival Analysis 7 3.10. Model Evaluation 6 4.0. Special Functions 6 1.1. Data Science 6 3.11. Optimal Transportation 5 2.1. Statistics 2 2.5. GLM 2 4.2. Bifurcation 1

Sub Categories

3.1.3. Matrix and Tensor computation 56 3.10.2. Global Sensitivity Anylysis 32 3.2.5. First Order Methods 31 4.3.2. Interpolations and Approximations 29 3.1.4.Platforms, CPU, GPU and TPU 17 3.5.1. Machine Learning 16 3.1.2. Quadrature 15 3.4.5. Gaussion, non-Gaussion and Kernel 14 3.1.1. Auto Differentiation 12 3.4.1. MCMC 12 3.7.1. Partial differential equation 11 3.6.4 BNN 10 3.4.2. Approximate Bayesian Computation (ABC) 10 3.4.3. Data Assimilation (SMC, particles filter) 10 Semi-supervised Learning 10 3.5.2. Deep Learning 8 3.4.6. Bayesian Optimization 7 3.8.1. Universal Differential Equations. (Neural differential equations) 7 3.6.3. VAE 7 3.2.1. Metaheuristic 7 3.7.2 Fractional Differential and Calculus 7 3.5.4. GNN 6 3.4.7. Information theory 6 4.3.1. Roots 6 Venn Diagrams 6 3.2.2. Evolution Stragegy 5 3.9.1. Symbolic Regression 5 3.5.3. Reinforce Learning 5 3.8.2. Physical Informed Neural Netwworks 4 3.9.3. DMD (Dynamic Mode Decomposition) 4 3.6.2. Normilization Flows 4 3.8.3. Neural Operator 4 3.4.9. Casual 4 3.4.4. Variational Inference 4 3.4.10. Sampling 3 3.5.8. Visulization 3 3.9.2. SINDy (Sparse Identification of Nonlinear Dynamics from Data) 2 3.10.1. Structure Idendification 2 3.5.5. Transformer 2 3.4.8. Uncertanty 2 3.2.3. Genetic Algorithms 2 3.5.6. Transfer Learning 1 3.2.4. Nonconvex 1 3.5.7. Neural Tangent 1 3.6.1. GAN 1

Keywords

julia 105 machine-learning 61 python 53 deep-learning 45 optimization 31 bayesian-inference 26 pytorch 24 time-series 22 julia-language 22 scientific-machine-learning 21 data-science 20 sciml 17 automatic-differentiation 16 differential-equations 16 neural-networks 13 math 11 tensorflow 11 linear-algebra 9 statistics 9 forecasting 8 pde 8 anomaly-detection 8 data-analysis 8 neural-network 8 numerical-integration 8 ode 7 parameter-estimation 7 partial-differential-equations 7 probabilistic-programming 7 dynamical-systems 7 variational-inference 7 julialang 7 integration 7 mcmc 7 gpu 7 scikit-learn 6 bayesian 6 jax 6 differentialequations 6 bayesian-statistics 6 automl 6 time-series-analysis 6 timeseries 6 gaussian-processes 6 optimal-control 6 simulation 5 classification 5 transformer 5 neural-differential-equations 5 graph-neural-networks 5

Ecosyste.ms: Awesome

awesome-sciml

<span id="head3">1.1. Data Science</span>

<span id="head75"> Outlier Detection</span>

<span id="head6">2.1. Statistics</span>

<span id="head7">2.2. (Deep Learning based) Time Series Analysis</span>

<span id="head8">2.3. Survival Analysis</span>

<span id="head9">2.4. Data Visualization</span>

Venn Diagrams

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

<span id="head12">3.1.1. Auto Differentiation</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="head14">3.1.2. Quadrature</span>

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

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

3.2.5. First Order Methods

<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="head35">3.4.5. Gaussion, non-Gaussion and Kernel</span>

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

<span id="head32">3.4.2. Approximate Bayesian Computation (ABC)</span>

<span id="head33">3.4.3. Data Assimilation (SMC, particles filter)</span>

<span id="head34">3.4.4. Variational Inference</span>

<span id="head36">3.4.6. Bayesian Optimization</span>

<span id="head37">3.4.7. Information theory</span>

<span id="head38">3.4.8. Uncertanty</span>

<span id="head39">3.4.9. Casual</span>

<span id="head40">3.4.10. Sampling</span>

<span id="head41">3.5. Machine Learning and Deep Learning</span>

<span id="head42">3.5.1. Machine Learning</span>

<span id="head44">3.5.3. Reinforce Learning</span>

<span id="head40">3.4.10. Sampling</span>

<span id="head43">3.5.2. Deep Learning</span>

<span id="head45">3.5.4. GNN</span>

<span id="head46">3.5.5. Transformer</span>

<span id="head48">3.5.7. Neural Tangent</span>

<span id="head49">3.5.8. Visulization</span>

Semi-supervised Learning

<span id="head47">3.5.6. Transfer Learning</span>

<span id="head50">3.6. Probablistic Machine Learning and Deep Learning</span>

Semi-supervised Learning

<span id="head53">3.6.3. VAE</span>

3.6.4 BNN

<span id="head51">3.6.1. GAN</span>

<span id="head52">3.6.2. Normilization Flows</span>

<span id="head54">3.7. Differential Equations and Scientific Computation</span>

3.6.4 BNN

<span id="head55">3.7.1. Partial differential equation</span>

3.7.2 Fractional Differential and Calculus

<span id="head56">3.8. Scientific Machine Learning (Differential Equation and ML)</span>

<span id="head58">3.8.2. Physical Informed Neural Netwworks</span>

3.7.2 Fractional Differential and Calculus

<span id="head57">3.8.1. Universal Differential Equations. (Neural differential equations)</span>

<span id="head59">3.8.3. Neural Operator</span>

<span id="head77">3.11. Optimal Transportation</span>

<span id="head66">3.10.2. Global Sensitivity Anylysis</span>

<span id="head81">3.12. Agents, Graph and Networks</span>

<span id="head66">3.10.2. Global Sensitivity Anylysis</span>

<span id="head69">4.1. Symbolic Computation</span>

<span id="head66">3.10.2. Global Sensitivity Anylysis</span>

4.4 Polynomials

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

<span id="head70">4.3. Roots, Intepolations</span>

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

<span id="head71">4.3.1. Roots</span>

<span id="head4"> Smoothing</span>

<span id="head76">2.5. GLM</span>

Venn Diagrams

<span id="head64">3.10. Model Evaluation</span>

<span id="head66">3.10.2. Global Sensitivity Anylysis</span>

<span id="head65">3.10.1. Structure Idendification</span>

<span id="head60">3.9. Data Driven Methods (Equation Searching Methods)</span>

<span id="head63">3.9.3. DMD (Dynamic Mode Decomposition)</span>