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.
Independent numerical study of Kolmogorov PDEs using SDE simulation, Monte Carlo, finite differences, and neural Feynman–Kac regression.
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.
Notes on PDEs
C++ code for pricing options under Feller-Levy models using the Finite Element Method
intro to financial mathematics; spring 26
Extension of the Black-Scholes framework to path-dependent derivatives (Barrier Options) using stochastic calculus, the reflection principle, and PDE replication.
A scientifically audited research prototype for finite-difference & Feynman-Kac computation of linear parabolic PDEs. Establishes a rigorous, noise-controlled no-go theorem for reference-free error source identifiability. 61+ tests green.
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