Scientific Software
Updated 6 months ago

PySINDy — Peer-reviewed • Rank 21.2 • Science 95%

PySINDy: A comprehensive Python package for robust sparse system identification - Published in JOSS (2022)

Economics Materials Science (40%)
Scientific Software · Peer-reviewed
Scientific Software
Updated 6 months ago

SysIdentPy — Peer-reviewed • Rank 15.8 • Science 93%

SysIdentPy: A Python package for System Identification using NARMAX models - Published in JOSS (2020)

Updated 6 months ago

autokoopman • Rank 10.2 • Science 64%

AutoKoopman - automated Koopman operator methods for data-driven dynamical systems analysis and control.

Updated 6 months ago

ceem • Rank 7.9 • Science 20%

Certainty-Equivalent Expectation Maximization: a scalable algorithm for system identification of partially observed systems

Updated 6 months ago

https://github.com/biaslab/acc2022-vmpnarmax • Rank 1.4 • Science 26%

Experiments and derivations for ACC2022 paper on variational message passing for online NARMAX identification.

Updated 6 months ago

https://github.com/biaslab/iwai2020-onlinesysid • Rank 0.0 • Science 26%

Code and experiments for IWAI 2020 submission on online system identification

Updated 6 months ago

https://github.com/biaslab/ccta2024-bidconvection • Rank 0.0 • Science 13%

Experiments for CCTA 2024 submission on fast Bayesian gray-box identification of convection in heat transfer dynamics.

Updated 6 months ago

https://github.com/baggepinnen/ltvmodels.jl • Science 10%

Tools to estimate Linear Time-Varying models in Julia

Updated 6 months ago

deepsysid • Science 67%

System identification toolkit for multistep prediction using deep learning and hybrid methods.

Updated 6 months ago

https://github.com/biaslab/cdc-2022 • Science 26%

Experiments and derivations for CDC2022 paper on message passing-based inference for NARMAX system identification.

Updated 6 months ago

https://github.com/equinor/timeseriesanalysis • Science 26%

Library that combines control engineering, dynamic simulation and machine learning on time-series. Developed to describe industrial processes and -automation. Lightweight, robust and fast for use in advanced analytics. Built on .NET to run anywhere.