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awesome-neural-ode

A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.

bootcamp

Bootcamp notebooks

camlab-dlsctutorials

climsim

An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators.

computational-science-and-engineering

18.085 Gilbert Strang Computational Science and Engineering

cs224n-win2223

Code and written solutions of the assignments of the Stanford CS224N: Natural Language Processing with Deep Learning course from winter 2022/2023

deepmind-research

This repository contains implementations and illustrative code to accompany DeepMind publications

dlsc-2023

ETH Zürich Deep Learning in Scientific Computing Master's course 2023

dlsc_deepthermal

Project using deep learning to model thermal storage for solar panels

dlsc_projects

This repository contains the machine learning projects completed for the class "Deep Learning in Scientific Computing" taught at ETH jointly by Siddhartha Mishra and Benjamin Moseley in Spring 2024. The description of the tasks can be found in the PDFs.

dlsc_tasks

Repository for the Deep Learning in Scientific Computing course offered in Spring 2022 at ETH Zürich: Deep learning project with the aim of studying the preliminary design of a thermal energy storage.

drlfoam

Deep reinforcement learning with OpenFOAM

gano

geo-fno

Geometry-Aware Fourier Neural Operator (Geo-FNO)

graph-pde

Using graph network to solve PDEs

lips

markov_neural_operator

mlfoam

Thoughts about ML committee for OpenFOAM

modulus

Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods

neural-spdes

Neural Stochastic PDEs: resolution-invariant modelling of continuous spatiotemporal dynamics

neurodiffeq

A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.

pde-surrogate

Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

physics_informed_no

pptale.github.io

MNA_course

probabilistic_ml_fluids

Source code for "Probabilistic neural networks for fluid flow model-order reduction and data recovery"

sciann

Deep learning for Engineers - Physics Informed Deep Learning

sciann-applications

A place to share problems solved with SciANN

scimlbook

Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)

teachingdatascience

Course notes for Data Science related topics, prepared in LaTeX

xpinns

Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations