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awesome-scientific-machine-learning

A curated list of awesome Scientific Machine Learning (SciML) papers, resources and software
https://github.com/MartinuzziFrancesco/awesome-scientific-machine-learning

Last synced: 4 days ago
JSON representation

Papers
- Neural Operators
 - `pub` - 229._
 - `arxiv`
 - `pub` - 229._
 - `pub` - informed-DeepONets) Wang, Sifan, Hanwen Wang, and Paris Perdikaris _Science advances 7, no. 40 (2021): eabi8605._
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 - `arxiv`
- Neural Differential Equations
 - `pub`
 - `arxiv`
 - `pub` - MIT/StiffNeuralODE) Kim, Suyong, Weiqi Ji, Sili Deng, Yingbo Ma, and Christopher Rackauckas _Chaos: An Interdisciplinary Journal of Nonlinear Science 31, no. 9 (2021): 093122_
 - `pub` - nn) Greydanus, Samuel, Misko Dzamba, and Jason Yosinski _Advances in neural information processing systems 32 (2019)._
 - `pub` - neural-odes) Dupont, Emilien, Arnaud Doucet, and Yee Whye Teh _Advances in Neural Information Processing Systems 32 (2019)._
 - `pub`
 - `arxiv` - lstms) Lechner, Mathias, and Ramin Hasani _arXiv preprint arXiv:2006.04418 (2020)._
 - `pub` - kidger/NeuralCDE) Kidger, Patrick, James Morrill, James Foster, and Terry Lyons _Advances in Neural Information Processing Systems 33 (2020): 6696-6707._
 - `arxiv`
- Physics Informed NNs
 - `pub` - 1000._
 - `pub` - A2626._
 - `pub`
 - `pub` - B1132._
 - `pub`
 - `pub` - 707_
 - `pub`
- Model Discovery
 - `pub` - 3937._
 - `pub`
Software
- Julia
  - `code`
  - `code`
  - `code`
  - Scientific Machine Learning (Sciml) - comprehensive list of the packages provided, for a full description please check out their website.
- Python
  - `code`
  - `code`
  - `code`
  - `code` - informed neural networks (PINN).
  - `code`
  - `code`
  - `code`
  - `code`
  - `code` - platform (Pytorch, Jax, Tensorflow) library for scientific machine learning and physics-informed learning.
  - Pytorch
  - Jax
  - Tensorflow
Books
- Python
  - `pub`
  - `site`
Videos
- Channels
  - `profile` - Driven Science and Engineering, and Physics Informed Machine Learning._
  - `profile`
  - `profile`
- Playlists
  - `list`
  - `list`
  - `list`
  - `list`
  - `list`
  - `list`
  - `list`
  - `list`
- Conferences and Workshops
  - `page`
  - `page`
  - `page`
  - `page` - 2019/01/30
Contributions Guidelines
- Papers
  - pub

Programming Languages

Python 7 Julia 3 Jupyter Notebook 1

Categories

Papers 83 Software 16 Videos 15 Books 2 Contributions Guidelines 1

Sub Categories

Neural Operators 65 Python 14 Neural Differential Equations 9 Playlists 8 Physics Informed NNs 7 Julia 4 Conferences and Workshops 4 Channels 3 Model Discovery 2 Papers 1

Keywords

dynamical-systems 5 neural-differential-equations 5 scientific-machine-learning 5 deep-learning 4 differential-equations 4 machine-learning 4 neural-networks 3 differentialequations 3 pytorch 3 neural-network 3 pinn 3 neural-ode 2 pde 2 stochastic-differential-equations 2 sciml 2 nonlinear-dynamics 2 scientific-ml 2 scientific-ai 2 python 2 ode 2 jax 2 physics-informed-learning 2 partial-differential-equations 2 delay-differential-equations 2 ordinary-differential-equations 2 neural-pde 1 neural-sde 1 neural-jump-diffusions 1 neural-dde 1 stochastic-processes 1 spde 1 sde 1 scientific 1 r 1 numerical 1 julia 1 differential-algebraic-equations 1 dde 1 dae 1 tensorflow 1 paddle 1 operator 1 multi-fidelity-data 1 deeponet 1 system-identification 1 sparse-regression 1 model-discovery 1 equinox 1 physics-informed-neural-networks 1 pde-solver 1