Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior-Reference-Cited by-同舟云学术

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Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior

Published:2022-12-06 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 61st Conference on Decision and Control (CDC)
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Author:

Beckers Thomas¹,Seidman Jacob²,Perdikaris Paris³,Pappas George J.¹

Affiliation:

1. University of Pennsylvania,Department of Electrical and Systems Engineering,Philadelphia,PA,USA,19104

2. University of Pennsylvania,graduate program in Applied Mathematics and Computational Science,Philadelphia,PA,USA,19104

3. University of Pennsylvania,Department of Mechanical Engineering and Applied Mechanics,Philadelphia,PA,USA,19104

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9992315/9992317/09992733.pdf?arnumber=9992733

Reference28 articles.

1. Port-Hamiltonian Systems in Adaptive and Learning Control: A Survey

2. Learning ODE Models with Qualitative Structure Using Gaussian Processes

3. Symplectic Gaussian process regression of maps in Hamiltonian systems

4. Port-Hamiltonian neural networks for learning explicit time-dependent dynamical systems

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3. Torque-Induced-Overshoot Reduction Inspired Compensator for PMSMs Using Motor-Physics Embedded Gaussian Process Regression;IEEE/ASME Transactions on Mechatronics;2024

4. Port-Hamiltonian Neural ODE Networks on Lie Groups for Robot Dynamics Learning and Control;IEEE Transactions on Robotics;2024

5. Data-Driven Momentum Observers With Physically Consistent Gaussian Processes;IEEE Transactions on Robotics;2024

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