NSF AI INSTITUTE IN DYNAMIC SYSTEMS

For automatically converting algorithms into live ROS graphs.…continue

An RL-Gym for Challenge Problems in Data-Driven Modeling and Control of Fluid Dynamics.…continue

For calculating observability along trajectories…continue

PyDMD is a Python package designed for Dynamic Mode Decomposition (DMD), a data-driven method used for analyzing and extracting spatiotemporal coherent structures from time-varying datasets. It provides a comprehensive and user-friendly interface for performing DMD analysis, making it a valuable tool for researchers, engineers, and data scientists working in various fields…continue

PyKoopman is a Python package for computing data-driven approximations to the Koopman operator…continue

Recently upgraded, including new methods, PyNumDiff is a Python package that implements various methods for computing numerical derivatives of noisy data, which can be a critical step in developing dynamic models or designing control. There are four different families of methods implemented in this repository: smoothing followed by finite difference calculation, local approximation with linear models, Kalman filtering based methods and total variation regularization methods. Most of these methods have multiple parameters involved to tune. We take a principled approach and propose a multi-objective optimization framework for choosing parameters that minimize a loss function to balance the faithfulness and smoothness of the derivative estimate…continue

PySensors is a Scikit-learn style Python package for the sparse placement of sensors, either for reconstruction or classification tasks. Version 2.0 adds constrained optimal sensor placement, reconstruction uncertainty heatmaps, and a greedy two-point optimization scheme, with example notebooks for nuclear digital twin applications…continue

PySINDy is a sparse regression package with several implementations for the Sparse Identification of Nonlinear Dynamical systems (SINDy) method introduced in Brunton et al. (2016a), including the unified optimization approach of Champion et al. (2019), SINDy with control from Brunton et al. (2016b), Trapping SINDy from Kaptanoglu et al. (2021), SINDy-PI from Kaheman et al. (2020), PDE-FIND from Rudy et al. (2017), and so on. A comprehensive literature review is given in de Silva et al. (2020) and Kaptanoglu, de Silva et al. (2021)…continue

RLE-Bench is a benchmark developed by Institute Co-PI Na Li and collaborators for evaluating AI agents on robot learning engineering tasks. Learn more about the benchmark and GitHub repo.

A dataset of 1,680 controlled rover mobility trials conducted at the Mines Lunar Surface Simulator. Includes synchronized telemetry (IMU, wheel encoders), motion-capture ground truth, and LiDAR terrain scans…continue