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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: 3 days ago
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1.1. Data Science
- JuliaData
- Queryverse
- JuliaDatabases
- JuliaData/CSV.jl: Utility library for working with CSV and other delimited files in the Julia programming language
- JuliaData/DataFrames.jl: In-memory tabular data in Julia
- JuliaStats/TimeSeries.jl: Time series toolkit for Julia
Outlier Detection
- OutlierDetectionJL
- rob-med/awesome-TS-anomaly-detection: List of tools & datasets for anomaly detection on time-series data.
- baggepinnen/MatrixProfile.jl: Time-series analysis using the Matrix profile in Julia
- jbytecode/LinRegOutliers: Direct and robust methods for outlier detection in linear regression
- yzhao062/pyod: A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)
- cerlymarco/tsmoothie: A python library for time-series smoothing and outlier detection in a vectorized way.
- DHI/tsod: Anomaly Detection for time series data
2.1. Statistics
- Julia Statistics
- gragusa (Giuseppe Ragusa)
2.2. (Deep Learning based) Time Series Analysis
- microsoft/forecasting: Time Series Forecasting Best Practices & Examples
- JuliaDynamics/ARFIMA.jl: Simulate stochastic timeseries that follow ARFIMA, ARMA, ARIMA, AR, etc. processes
- MaxBenChrist/awesome_time_series_in_python: This curated list contains python packages for time series analysis
- unit8co/darts: A python library for easy manipulation and forecasting of time series.
- jdb78/pytorch-forecasting: Time series forecasting with PyTorch
- AIStream-Peelout/flow-forecast: Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
- timeseriesAI/tsai: Time series Timeseries Deep Learning Machine Learning Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
- tslearn-team/tslearn: A machine learning toolkit dedicated to time-series data
- salesforce/Merlion: Merlion: A Machine Learning Framework for Time Series Intelligence
- ourownstory/neural_prophet: NeuralProphet: A simple forecasting package
- sktime/sktime-dl: sktime companion package for deep learning based on TensorFlow
- IBM/TSML.jl: A package for time series data processing, classification, clustering, and prediction.
- alkaline-ml/pmdarima: A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.
- zhouhaoyi/Informer2020: The GitHub repository for the paper "Informer" accepted by AAAI 2021.
- blue-yonder/tsfresh: Automatic extraction of relevant features from time series:
- TDAmeritrade/stumpy: STUMPY is a powerful and scalable Python library for modern time series analysis
- databrickslabs/tempo: API for manipulating time series on top of Apache Spark: lagged time values, rolling statistics (mean, avg, sum, count, etc), AS OF joins, downsampling, and interpolation
- alan-turing-institute/sktime: A unified framework for machine learning with time series
2.3. Survival Analysis
- Deep Learning for Survival Analysis
- sebp/scikit-survival: Survival analysis built on top of scikit-learn
- havakv/pycox: Survival analysis with PyTorch
- CamDavidsonPilon/lifelines: Survival analysis in Python
- chl8856/DeepHit: DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks
- jaredleekatzman/DeepSurv: DeepSurv is a deep learning approach to survival analysis.
2.4. Data Visualization
- Venn Diagrams
3.1. Differentiation, Quadrature and Tensor computation
- 3.1.1. Auto Differentiation
- 3.1.3. Matrix and Tensor computation
- 3.1.4.Platforms, CPU, GPU and TPU
- 3.1.2. Quadrature
3.2. Optimization
- 3.1.4.Platforms, CPU, GPU and TPU
- 3.2.5. First Order Methods
 - OPTEC
 - kul-optec/CIAOAlgorithms.jl: Coordinate and Incremental Aggregated Optimization Algorithms
- 3.2.1. Metaheuristic
- 3.2.2. Evolution Stragegy
- 3.2.3. Genetic Algorithms
 - d9w/CartesianGeneticProgramming.jl: Cartesian Genetic Programming for Julia
 - WestleyArgentum/GeneticAlgorithms.jl: A lightweight framework for writing genetic algorithms in Julia
- 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
- 3.4.5. Gaussion, non-Gaussion and Kernel
- 3.4.1. MCMC
- 3.4.2. Approximate Bayesian Computation (ABC)
- 3.4.3. Data Assimilation (SMC, particles filter)
- 3.4.4. Variational Inference
- 3.4.6. Bayesian Optimization
- 3.4.7. Information theory
- 3.4.8. Uncertanty
 - uncertainty-toolbox/uncertainty-toolbox: A python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization
 - JuliaPhysics/Measurements.jl: Error propagation calculator and library for physical measurements. It supports real and complex numbers with uncertainty, arbitrary precision calculations, operations with arrays, and numerical integration.
- 3.4.9. Casual
- 3.4.10. Sampling
 - MrUrq/LatinHypercubeSampling.jl: Julia package for the creation of optimised Latin Hypercube Sampling Plans
 - SciML/QuasiMonteCarlo.jl: Lightweight and easy generation of quasi-Monte Carlo sequences with a ton of different methods on one API for easy parameter exploration in scientific machine learning (SciML)
3.5. Machine Learning and Deep Learning
- 3.5.1. Machine Learning
- 3.5.3. Reinforce Learning
- 3.5.2. Deep Learning
- 3.4.10. Sampling
 - ritchieng/the-incredible-pytorch at pythonrepo.com
- 3.5.4. GNN
- 3.5.5. Transformer
 - chengchingwen/Transformers.jl: Julia Implementation of Transformer models
 - huggingface/transformers: 🤗 Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.
- 3.5.6. Transfer Learning
 - jindongwang/transferlearning: Transfer learning / domain adaptation / domain generalization / multi-task learning etc. papers, codes. datasets, applications, tutorials.-迁移学习
- 3.5.7. Neural Tangent
 - google/neural-tangents: Fast and Easy Infinite Neural Networks in Python
- 3.5.8. Visulization
- Semi-supervised Learning
 - TorchSSL/TorchSSL: A PyTorch-based library for semi-supervised learning (NeurIPS'21)
3.6. Probablistic Machine Learning and Deep Learning
- Semi-supervised Learning
- 3.6.3. VAE
- 3.6.4 BNN
- 3.6.1. GAN
 - kwotsin/mimicry: [CVPR 2020 Workshop
- 3.6.2. Normilization Flows
3.7. Differential Equations and Scientific Computation
- 3.6.4 BNN
- 3.7.1. Partial differential equation
- 3.7.2 Fractional Differential and Calculus
3.8. Scientific Machine Learning (Differential Equation and ML)
- 3.8.2. Physical Informed Neural Netwworks
- 3.7.2 Fractional Differential and Calculus
 - Zymrael/awesome-neural-ode: A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.
 - massastrello/awesome-implicit-neural-models
- 3.8.1. Universal Differential Equations. (Neural differential equations)
- 3.8.3. Neural Operator
 - CliMA/OperatorFlux.jl: Operator layers for Flux.jl
 - brekmeuris/DrMZ.jl: Deep renormalized Mori-Zwanzig (DrMZ) Julia package.
3.11. Optimal Transportation
- 3.10.2. Global Sensitivity Anylysis
3.12. Agents, Graph and Networks
- 3.10.2. Global Sensitivity Anylysis
4.1. Symbolic Computation
- 3.10.2. Global Sensitivity Anylysis
4.4 Polynomials
- 4.3.2. Interpolations and Approximations
4.3. Roots, Intepolations
- 4.3.2. Interpolations and Approximations
- 4.3.1. Roots
Smoothing
- viraltux/Smoothers.jl: Collection of basic smoothers and smoothing related applications
- LAMPSPUC/StateSpaceModels.jl: StateSpaceModels.jl is a Julia package for time-series analysis using state-space models.
- miguelraz/StagedFilters.jl
- JuliaDSP/DSP.jl: Filter design, periodograms, window functions, and other digital signal processing functionality
- konkam/FeynmanKacParticleFilters.jl: Particle filtering using the Feynman-Kac formalism
- mschauer/Kalman.jl: Flexible filtering and smoothing in Julia
- JuliaStats/Loess.jl: Local regression, so smooooth!
2.5. GLM
- Venn Diagrams
 - bambinos/bambi: BAyesian Model-Building Interface (Bambi) in Python.
 - The Algorithms
3.10. Model Evaluation
- 3.10.2. Global Sensitivity Anylysis
- 3.10.1. Structure Idendification
 - SciML/StructuralIdentifiability.jl
 - alexeyovchinnikov/SIAN-Julia: Implementation of SIAN in Julia
3.9. Data Driven Methods (Equation Searching Methods)
- 3.9.3. DMD (Dynamic Mode Decomposition)
 - foldfelis/NeuralOperators.jl: learning the solution operator for partial differential equations in pure Julia.
 - mathLab/PyDMD: Python Dynamic Mode Decomposition
- 3.8.3. Neural Operator
 - SciML/DataDrivenDiffEq.jl: Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization
 - nmheim/NeuralArithmetic.jl: Collection of layers that can perform arithmetic operations
- 3.9.1. Symbolic Regression
- 3.9.2. SINDy (Sparse Identification of Nonlinear Dynamics from Data)
 - dynamicslab/pysindy: A package for the sparse identification of nonlinear dynamical systems from data
 - dynamicslab/modified-SINDy: Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data
4.0. Special Functions
- 3.10.2. Global Sensitivity Anylysis
4.2. Bifurcation
- 4.3.2. Interpolations and Approximations
 - rveltz/BifurcationKit.jl: A Julia package to perform Bifurcation Analysis

Programming Languages

Julia 217 Python 97 Jupyter Notebook 26 TeX 3 C++ 3 JavaScript 3 R 3 TypeScript 1 MATLAB 1

Categories

3.1. Differentiation, Quadrature and Tensor computation 81 3.4. Bayesian Inference 80 3.5. Machine Learning and Deep Learning 44 3.2. Optimization 30 3.6. Probablistic Machine Learning and Deep Learning 23 3.7. Differential Equations and Scientific Computation 23 3.3. Optimal Control 19 2.2. (Deep Learning based) Time Series Analysis 18 4.4 Polynomials 18 4.3. Roots, Intepolations 15 2.4. Data Visualization 14 3.8. Scientific Machine Learning (Differential Equation and ML) 14 3.9. Data Driven Methods (Equation Searching Methods) 11 3.12. Agents, Graph and Networks 10 Smoothing 7 Outlier Detection 7 4.1. Symbolic Computation 7 3.10. Model Evaluation 6 4.0. Special Functions 6 1.1. Data Science 6 2.3. Survival Analysis 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 52 3.10.2. Global Sensitivity Anylysis 32 3.2.5. First Order Methods 31 4.3.2. Interpolations and Approximations 28 3.1.4.Platforms, CPU, GPU and TPU 16 3.5.1. Machine Learning 16 3.1.2. Quadrature 14 3.4.5. Gaussion, non-Gaussion and Kernel 14 3.4.1. MCMC 12 3.1.1. Auto Differentiation 12 3.7.1. Partial differential equation 11 3.4.3. Data Assimilation (SMC, particles filter) 10 3.4.2. Approximate Bayesian Computation (ABC) 10 3.6.4 BNN 10 Semi-supervised Learning 10 3.5.2. Deep Learning 8 3.7.2 Fractional Differential and Calculus 7 3.2.1. Metaheuristic 7 3.4.6. Bayesian Optimization 7 Venn Diagrams 6 4.3.1. Roots 6 3.8.1. Universal Differential Equations. (Neural differential equations) 6 3.4.7. Information theory 6 3.6.3. VAE 6 3.5.4. GNN 6 3.2.2. Evolution Stragegy 5 3.9.1. Symbolic Regression 5 3.5.3. Reinforce Learning 5 3.6.2. Normilization Flows 4 3.4.9. Casual 4 3.8.2. Physical Informed Neural Netwworks 4 3.8.3. Neural Operator 4 3.5.8. Visulization 3 3.4.4. Variational Inference 3 3.4.10. Sampling 3 3.2.3. Genetic Algorithms 2 3.4.8. Uncertanty 2 3.10.1. Structure Idendification 2 3.9.2. SINDy (Sparse Identification of Nonlinear Dynamics from Data) 2 3.9.3. DMD (Dynamic Mode Decomposition) 2 3.5.5. Transformer 2 3.5.7. Neural Tangent 1 3.2.4. Nonconvex 1 3.6.1. GAN 1 3.5.6. Transfer Learning 1

Keywords

julia 105 machine-learning 62 python 53 deep-learning 45 optimization 31 bayesian-inference 26 pytorch 24 time-series 23 data-science 22 julia-language 22 scientific-machine-learning 21 sciml 17 differential-equations 16 automatic-differentiation 16 neural-networks 14 math 11 forecasting 10 tensorflow 10 statistics 9 linear-algebra 9 gpu 8 anomaly-detection 8 numerical-integration 8 neural-network 8 pde 8 jax 7 data-analysis 7 scikit-learn 7 julialang 7 time-series-analysis 7 integration 7 partial-differential-equations 7 probabilistic-programming 7 dynamical-systems 7 ode 7 parameter-estimation 7 mcmc 7 timeseries 7 bayesian-statistics 6 variational-inference 6 gaussian-processes 6 bayesian 6 differentialequations 6 automl 6 optimal-control 6 simulation 5 outlier-detection 5 graph-neural-networks 5 nonlinear-optimization 5 numerical-optimization 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="head43">3.5.2. Deep Learning</span>

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

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

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

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

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

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

Semi-supervised Learning

<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>