A Julia package for Deep Backwards Stochastic Differential Equation (Deep BSDE) and Feynman-Kac methods to solve high-dimensional PDEs without the curse of dimensionality

This repository introduces Partial Differential Equation Solver using neural network that can learn resolution-invariant solution operators on Navier-Stokes equation. Solving PDE is the core subject of numerical simulation and is widely used in science and engineering, from molecular dynamics to flight simulation, and even weather forecasting.

C++ code for pricing options under Feller-Levy models using the Finite Element Method

Notes on PDEs

Full-stack web platform for exploring Feynman-Kac PINNs - solving PDEs via random walk Monte Carlo representations. Features 10D Black-Scholes and high-dimensional Schrödinger simulations.