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Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.

Physics Informed Machine Learning Tutorials (Pytorch and Jax)

An intuitive modeling interface for infinite-dimensional optimization problems.

This repo contains CUDA-Q Academic materials, including self-paced Jupyter notebook modules for building and optimizing hybrid quantum-classical algorithms using CUDA-Q.

Hands-On Optimization with Python

Pinneaple is an open-source Physics AI toolkit for Physics-Informed Neural Networks (PINNs), scientific ML, geometry processing, solvers, and reproducible training pipelines.

An Elegant Neural Network User Interface to build drag-and-drop neural networks, train in the browser, visualize during training, and export to Python.

Mathematical Programming in JAX

Hands-on material for a Machine Learning in Chemical Engineering course

Chemical Reaction Neural Network

Aspen HYSYS - Python connection

pyPhi is a python package to perform multivariate analysis using PCA and PLS methods

FOQUS: Framework for Optimization and Quantification of Uncertainty and Surrogates

torchvision-based transforms that provide access to parameterization

Nonlinear and Stochastic Optimization, CBE & ACMS 60499 / 40499 at U. Notre Dame

Examples and tutorials for how to use CUDA-Q.

discovering interpretable conservation laws from differential equations

Implementation of exact and heuristic methods for the minimum number of matches in heat exchanger network design