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.

Notes on PDEs

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

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.