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

Data-driven reduced order modeling for nonlinear dynamical systems

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

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.

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.

Supplementary materials to the lecture data driven audio signal processing

Matlab implementation of online and window dynamic mode decomposition algorithms

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

Fourier Feature Character Animation Controller Demo

This approach is used to solve data-driven optimal control problems by providing a Koopman operator based convex formulation

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

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