Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems-Reference-Cited by-同舟云学术

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

Published:2023-05-31 Issue: Volume: Page:
ISSN:
Container-title:2023 American Control Conference (ACC)
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Author:

Nghiem Truong X.¹,Drgoňa Ján²,Jones Colin³,Nagy Zoltan⁴,Schwan Roland³,Dey Biswadip⁵,Chakrabarty Ankush⁶,Di Cairano Stefano⁶,Paulson Joel A.⁷,Carron Andrea⁸,Zeilinger Melanie N.⁸,Shaw Cortez Wenceslao²,Vrabie Draguna L.²

Affiliation:

1. Northern Arizona University,School of Informatics, Computing, and Cyber Systems,Flagstaff,AZ,USA,86011

2. Pacific Northwest National Laboratory,Richland,WA,USA

3. EPFL,Switzerland

4. The University of Texas at Austin,USA

5. Siemens Corporation, Technology,Princeton,NJ,USA,08540

6. Mitsubishi Electric Research Laboratories,Cambridge,MA,USA,02139

7. The Ohio State University,Columbus,OH,USA,43210

8. ETH Zurich,Switzerland

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10155646/10155787/10155901.pdf?arnumber=10155901

Reference216 articles.

1. Physics-constrained deep learning of multi-zone building thermal dynamics

2. Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance?

3. Constrained Block Nonlinear Neural Dynamical Models

4. Success Weighted by Completion Time: A Dynamics-Aware Evaluation Criteria for Embodied Navigation

5. Neural Lander: Stable Drone Landing Control Using Learned Dynamics

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