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

Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.

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

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

FreeFEM source code

Next generation FEniCS problem solving environment

Grid-based approximation of partial differential equations in Julia

PDEBench: An Extensive Benchmark for Scientific Machine Learning

Julia package for function approximation

FiPy is a Finite Volume PDE solver written in Python

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

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

18.S096 - Applications of Scientific Machine Learning

Graph Neural PDEs

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

PDE-Net: Learning PDEs from Data