Collection of notebooks about quantitative finance, with interactive python code.

Learning in infinite dimension with neural operators.

Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

FreeFEM source code

Next generation FEniCS problem solving environment

PDEBench: An Extensive Benchmark for Scientific Machine Learning

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

Grid-based approximation of partial differential equations in Julia

Julia package for function approximation

Simulation and Parameter Estimation in Geophysics - A python package for simulation and gradient based parameter estimation in the context of geophysical applications.

FiPy is a Finite Volume PDE solver written in Python

Python package for numerical derivatives and partial differential equations in any number of dimensions.

Python package for solving partial differential equations using finite differences.

Finite element toolbox for Julia

Graph Neural PDEs

The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems

Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

PDE-Net: Learning PDEs from Data

18.S096 - Applications of Scientific Machine Learning