IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.

Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation

ACID: Action-Conditional Implicit Visual Dynamics for Deformable Object Manipulation

AI4Science: Python/Matlab implementation of online and window dynamic mode decomposition (Online DMD and Window DMD)

Python-based object-oriented discrete-event simulation tool for complex, data-driven modeling

AI4Science: Efficient data-driven Online Model Learning (OML) / system identification and control

Source code for the paper "Data-driven reduced-order models via regularised Operator Inference for a single-injector combustion process" by S. A. McQuarrie, C. Huang, and K. E. Willcox.

a little library to help me with things involving Koopman operators

Sparse Identification of Truncation Errors (SITE) for Data-Driven Discovery of Modified Differential Equations

Constructing linearizing transformations for reduced-order modeling of nonlinear dynamical systems

Non-intrusive reduced-order modeling with geometry-informed snapshots. Current based registration is applied to compute the diffeomorphism between snapshots.

Deep neural networks have garnered tremendous excitement in recent years thanks to their superior learning capacity in the presence of abundant data resources. However, collecting an exhaustive dataset covering all possible scenarios is often slow, expensive, and even impractical. The goal of this project is to devise a new learning framework th…

Five-point stencil Convolutional Neural Networks (FCNNs)

A novel neural network for effective learning of highly impulsive/oscillatory dynamic systems by jointly utilizing low-order derivatives

cNN-DP: Composite neural network with differential propagation for impulsive nonlinear dynamics.

This repository contains the complied GUI and backend codebase to enable the full-functionalities of Project Varuna.

A framework for data-driven modeling and analysis of granular materials in the strongly nonlinear regime using the modern Koopman theory

A novel neural network for effective learning of highly impulsive/oscillatory dynamic systems by jointly utilizing low-order derivatives

Final projects for 401-4656-21L AI in Sciences and Engineering @ ETHz. Includes implementation of Fourier Neural Operator (FNO) with time dependency, data-driven symbolic regression with PDE-Find and foundation model based on FNO for phase-field dynamics