data-driven-model

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

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.

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.

Supplementary materials to the lecture data driven audio signal processing

[CVPR 2025] Pytorch implementation of the paper "Hearing Anywhere in Any Environment"

Matlab implementation of online and window dynamic mode decomposition algorithms

a little library to help me with things involving Koopman operators

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

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

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

Fourier Feature Character Animation Controller Demo

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