deepsysid: System Identification Toolkit for Multistep Prediction using Deep Learning (doi:10.18419/darus-3455)

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deepsysid: System Identification Toolkit for Multistep Prediction using Deep Learning

doi:10.18419/darus-3455

DaRUS

2023-05-15

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Baier, Alexandra; Frank, Daniel, 2023, "deepsysid: System Identification Toolkit for Multistep Prediction using Deep Learning", https://doi.org/10.18419/darus-3455, DaRUS, V1

deepsysid: System Identification Toolkit for Multistep Prediction using Deep Learning

doi:10.18419/darus-3455

Baier, Alexandra (Universität Stuttgart)

Frank, Daniel (Universität Stuttgart)

EXC 2075 - 390740016

DaRUS

Baier, Alexandra

Baier, Alexandra

2023-05-09

https://doi.org/10.18419/darus-3455

Computer and Information Science, Engineering, System Identification, Nonlinear System, Dynamical System

<p>deepsysid is a system identification toolkit for multistep prediction using deep learning and hybrid methods.</p> <p>The toolkit is easy to use. After you follow the instructions in the <a href="https://darus.uni-stuttgart.de/file.xhtml?fileId=203227">README</a>, you will be able to download a dataset, run hyperparameter optimization and identify your best-performing multistep prediction models with just three commands: <br> <code> deepsysid download 4dof-sim-ship<br> deepsysid session --enable-cuda progress.json NEW<br> deepsysid session --enable-cuda --reportin=progress.json progress.json TEST_BEST </code> <br></p> The most current version of this software is available on <a href="https://github.com/AlexandraBaier/deepsysid">GitHub</a>.

Baier, Alexandra, Boukhers, Zeyd, & Staab, Steffen (2021). Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction. ArXiv, abs/2103.06727.

abs/2103.06727

Baier, Alexandra, Boukhers, Zeyd, & Staab, Steffen (2021). Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction. ArXiv, abs/2103.06727.

Frank, Daniel, Latif, Decky Aspandi, Muehlebach, Michael, & Staab, Steffen (2022). Robust Recurrent Neural Network to Identify Ship Motion in Open Water with Performance Guarantees - Technical Report. ArXiv, abs/2212.05781.

abs/2212.05781

Frank, Daniel, Latif, Decky Aspandi, Muehlebach, Michael, & Staab, Steffen (2022). Robust Recurrent Neural Network to Identify Ship Motion in Open Water with Performance Guarantees - Technical Report. ArXiv, abs/2212.05781.

Alexandra Baier, Decky Aspandi and Steffen Staab, "ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks", Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI-23), 2023.

Alexandra Baier, Decky Aspandi and Steffen Staab, "ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks", Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI-23), 2023.

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