An abstracted framework for training AI surrogates of physical systems using physics-based symbolic loss functions

Project demonstrates the power and simplicity of NVIDIA NIM (NVIDIA Inference Model), a suite of optimized cloud-native microservices, by setting up and running a Retrieval-Augmented Generation (RA…

Build a RAG (Retrieval Augmented Generation) pipeline from scratch and have it all run locally.

Keras Hyperopt: A very simple wrapper for convenient hyperparameter optimization

Importance Sampled Mixture of Experts Code

AutoML library for deep learning

Hands-On GPU Programming with Python and CUDA, published by Packt

Tutorial on history matching with ensembles

GPU accelerated multigrid library for Python

Deep Learning Examples

A C class for Mutiple Point Simulation algorithms

GEOSX Simulation Framework

Neural networks designed for operator-valued functional learning problems.

Machine learning framework for reservoir simulation

Deep learning library for solving differential equations and more

Suite of utilities aiming to simplify the workflow required to build models using Physics Informed Neural Networks and, eventually, Physics ML more broadly. This includes facilities for project ma…

PyTriton is a Flask/FastAPI-like interface that simplifies Triton's deployment in Python environments.

Transformer related optimization, including BERT, GPT

Repo for demo files and training materials

Code generation framework for automated finite difference computation

MH-MDGM for Bayesian inverse problems

Use Fourier transform to learn operators in differential equations.

PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations

Open source code base to showcase interoperability of CUDA-X AI software stack in multi-GPU environments and thus provide researchers a reference framework to build new projects on.

We well know GANs for success in the realistic image generation. However, they can be applied in tabular data generation. We will review and examine some recent papers about tabular GANs in action.