BGCL:Learning Constitutive Laws for System Identification

Abhishek Patkar, Kamal Youcef-Toumi
Proceedings of The 8th Annual Learning for Dynamics and Control Conference, PMLR 331:397-411, 2026.

Abstract

Nonlinear system identification of dynamical systems is a challenging problem. Recently, learning based approaches have made attempts to embed physical priors in the learning model to improve model identification of dynamical systems. In this paper, we propose the Bond Graph based Con stitutive Law learning (BGCL) framework to learn analytical expressions for constitutive laws and thus identify models for physical dynamical systems. Simulation studies conducted on a spring mass system and synchronous three phase motor are used to validate the proposed framework.

Cite this Paper

BibTeX
@InProceedings{pmlr-v331-patkar26a, title = {BGCL:Learning Constitutive Laws for System Identification}, author = {Patkar, Abhishek and Youcef-Toumi, Kamal}, booktitle = {Proceedings of The 8th Annual Learning for Dynamics and Control Conference}, pages = {397--411}, year = {2026}, editor = {Sukhatme, Gaurav and Lindemann, Lars and Tu, Stephen and Wierman, Adam and Atanasov, Nikolay}, volume = {331}, series = {Proceedings of Machine Learning Research}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v331/main/assets/patkar26a/patkar26a.pdf}, url = {https://proceedings.mlr.press/v331/patkar26a.html}, abstract = {Nonlinear system identification of dynamical systems is a challenging problem. Recently, learning based approaches have made attempts to embed physical priors in the learning model to improve model identification of dynamical systems. In this paper, we propose the Bond Graph based Con stitutive Law learning (BGCL) framework to learn analytical expressions for constitutive laws and thus identify models for physical dynamical systems. Simulation studies conducted on a spring mass system and synchronous three phase motor are used to validate the proposed framework.} }
Endnote
%0 Conference Paper %T BGCL:Learning Constitutive Laws for System Identification %A Abhishek Patkar %A Kamal Youcef-Toumi %B Proceedings of The 8th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2026 %E Gaurav Sukhatme %E Lars Lindemann %E Stephen Tu %E Adam Wierman %E Nikolay Atanasov %F pmlr-v331-patkar26a %I PMLR %P 397--411 %U https://proceedings.mlr.press/v331/patkar26a.html %V 331 %X Nonlinear system identification of dynamical systems is a challenging problem. Recently, learning based approaches have made attempts to embed physical priors in the learning model to improve model identification of dynamical systems. In this paper, we propose the Bond Graph based Con stitutive Law learning (BGCL) framework to learn analytical expressions for constitutive laws and thus identify models for physical dynamical systems. Simulation studies conducted on a spring mass system and synchronous three phase motor are used to validate the proposed framework.
APA
Patkar, A. & Youcef-Toumi, K.. (2026). BGCL:Learning Constitutive Laws for System Identification. Proceedings of The 8th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 331:397-411 Available from https://proceedings.mlr.press/v331/patkar26a.html.

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