PDE-Net: Learning PDEs from Data

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

Creating animation sequences between sparse key frames using motion phase features.

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

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

Data-driven reduced order modeling for nonlinear dynamical systems

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

Awesome-spatial-temporal-data-mining-packages. Julia and Python resources on spatial and temporal data mining. Mathematical epidemiology as an application. Most about package information. Data Sources Links and Epidemic Repos are also included. Keep updating.

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.

Supplementary materials to the lecture data driven audio signal processing

a little library to help me with things involving Koopman operators

Matlab implementation of online and window dynamic mode decomposition algorithms

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

Fourier Feature Character Animation Controller Demo

PILOTS (ProgrammIng Language for spatiO-Temporal data Streaming applications)

Delay Embedded Regressive Reduced Order Model

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…