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: 1 day 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)
- cscherrer/MeasureTheory.jl: "Distributions" that might not add to one.
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.
- 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
- jdb78/pytorch-forecasting: Time series forecasting with PyTorch
- 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.
- square/pysurvival: Open source package for Survival Analysis modeling
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
- 3.4.10. Sampling
 - ritchieng/the-incredible-pytorch at pythonrepo.com
- 3.5.2. Deep Learning
- 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.
- 3.5.7. 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)
- 3.5.6. Transfer Learning
 - jindongwang/transferlearning: Transfer learning / domain adaptation / domain generalization / multi-task learning etc. papers, codes. datasets, applications, tutorials.-迁移学习
3.6. 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
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!
4.3. Roots, Intepolations
- 4.3.2. Interpolations and Approximations
- 4.3.1. Roots
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)
- 3.8.3. 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
- 3.9.3. DMD (Dynamic Mode Decomposition)
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
 - rveltz/BifurcationKit.jl: A Julia package to perform Bifurcation Analysis

Programming Languages

Julia 242 Python 96 Jupyter Notebook 28 TeX 3 C++ 3 JavaScript 3 R 3 TypeScript 1 MATLAB 1

Categories

3.1. Differentiation, Quadrature and Tensor computation 91 3.4. Bayesian Inference 82 3.5. Machine Learning and Deep Learning 46 3.2. Optimization 32 4.4 Polynomials 28 3.6. Probablistic Machine Learning and Deep Learning 25 3.7. Differential Equations and Scientific Computation 23 3.3. Optimal Control 20 2.2. (Deep Learning based) Time Series Analysis 18 4.3. Roots, Intepolations 18 2.4. Data Visualization 14 3.8. Scientific Machine Learning (Differential Equation and ML) 14 3.9. Data Driven Methods (Equation Searching Methods) 13 3.12. Agents, Graph and Networks 10 3.10. Model Evaluation 7 Smoothing 7 2.3. Survival Analysis 7 Outlier Detection 7 4.1. Symbolic Computation 7 1.1. Data Science 6 4.0. Special Functions 6 3.11. Optimal Transportation 5 2.1. Statistics 3 4.2. Bifurcation 2 2.5. GLM 2

Sub Categories

3.1.3. Matrix and Tensor computation 61 4.3.2. Interpolations and Approximations 42 3.10.2. Global Sensitivity Anylysis 33 3.2.5. First Order Methods 32 3.1.4.Platforms, CPU, GPU and TPU 18 3.5.1. Machine Learning 17 3.1.2. Quadrature 15 3.4.5. Gaussion, non-Gaussion and Kernel 14 3.4.1. MCMC 13 3.1.1. Auto Differentiation 12 Semi-supervised Learning 11 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 3.5.2. Deep Learning 8 3.2.1. Metaheuristic 7 3.7.2 Fractional Differential and Calculus 7 3.6.3. VAE 7 3.5.4. GNN 7 Venn Diagrams 6 3.4.6. Bayesian Optimization 6 3.8.1. Universal Differential Equations. (Neural differential equations) 6 3.4.7. Information theory 6 4.3.1. Roots 6 3.4.9. Casual 5 3.2.2. Evolution Stragegy 5 3.9.1. Symbolic Regression 5 3.5.3. Reinforce Learning 5 3.9.3. DMD (Dynamic Mode Decomposition) 4 3.6.2. Normilization Flows 4 3.8.3. Neural Operator 4 3.4.4. Variational Inference 4 3.8.2. Physical Informed Neural Netwworks 4 3.5.8. Visulization 3 3.4.10. Sampling 3 3.2.3. Genetic Algorithms 2 3.10.1. Structure Idendification 2 3.9.2. SINDy (Sparse Identification of Nonlinear Dynamics from Data) 2 3.5.5. Transformer 2 3.4.8. Uncertanty 2 3.6.1. GAN 1 3.2.4. Nonconvex 1 3.5.6. Transfer Learning 1 3.5.7. Neural Tangent 1

Keywords

julia 119 machine-learning 69 python 54 deep-learning 48 optimization 32 bayesian-inference 30 pytorch 26 julia-language 23 data-science 22 scientific-machine-learning 22 time-series 22 sciml 18 automatic-differentiation 17 differential-equations 17 neural-networks 14 statistics 11 tensorflow 11 math 11 linear-algebra 10 probabilistic-programming 9 variational-inference 9 numerical-integration 9 pde 8 ode 8 julialang 8 data-analysis 8 anomaly-detection 8 neural-network 8 forecasting 8 parameter-estimation 7 dynamical-systems 7 gpu 7 mcmc 7 partial-differential-equations 7 gaussian-processes 7 integration 7 graph-neural-networks 6 bayesian 6 simulation 6 automl 6 bayesian-methods 6 time-series-analysis 6 optimal-control 6 timeseries 6 differentialequations 6 scikit-learn 6 bayesian-statistics 6 nonlinear-optimization 6 jax 6 classification 6

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="head4"> Smoothing</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="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="head59">3.8.3. Neural Operator</span>

<span id="head61">3.9.1. Symbolic Regression</span>