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.
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.
intro to financial mathematics; spring 26
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.
Notes on PDEs
Extension of the Black-Scholes framework to path-dependent derivatives (Barrier Options) using stochastic calculus, the reflection principle, and PDE replication.
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