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The Julia module for Multilevel Monte Carlo methods
Lightweight and easy generation of quasi-Monte Carlo sequences with a ton of different methods on one API for easy parameter exploration in scientific machine learning (SciML)
Deep BSDE implementation in TensorFlow with XLA support. Examples for pricing European options are provided
This repository contains Python scripts that implement backward stochastic differential equations (BSDEs) solutions to several partial differential equations in high dimension using Pytorch.
This repository contains the code for the paper: Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation
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
A visual labeling system implemented in Jupyter widgets.
Randomized algorithms for numerical linear algebra in Julia
Code for paper https://arxiv.org/abs/2306.07961
Research package for automatic differentiation of programs containing discrete randomness.