Chavelli, F., Khoo, ZY., Wu, D., Low, J.S.C., Bressan, S. (2025). Physics-Informed Discovery of State Variables in Second-Order and Hamiltonian Systems. In: Nguyen, N.T., et al. Intelligent Information and Database Systems. ACIIDS 2025. Lecture Notes in Computer Science(), vol 15683. Springer, Singapore. https://doi.org/10.1007/978-981-96-6008-7_1
Abstract:
The modeling of dynamical systems is a pervasive concern for not only describing but also predicting and controlling natural phenomena and engineered systems. Current data-driven approaches often assume prior knowledge of the relevant state variables or result in overparameterized state spaces. Boyuan Chen and his co-authors proposed a neural network model that estimates the degrees of freedom and attempts to discover the state variables of a dynamical system. Despite its innovative approach, this baseline model lacks a connection to the physical principles governing the systems it analyzes, leading to unreliable state variables.
This research proposes a method that leverages the physical characteristics of second-order Hamiltonian systems to constrain the baseline model. The proposed model outperforms the baseline model in identifying a minimal set of non-redundant and interpretable state variables.
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Publisher Copyright
Funding Info:
This research is part of the outputs from Dr Khoo Zi-Yu's PhD, which was supported by the A*STAR Graduate Scholarship (AGS).
This research / project is supported by the Ministry of Education - Academic Research Fund Tier 2
Grant Reference no. : MOE T2EP50120-0019
This research / project is supported by the National Research Foundation - Campus for Research Excellence and Technological Enterprise (CREATE) program
Grant Reference no. :
Description:
This is a post-peer-review, pre-copyedit version of an article published in Asian Conference on Intelligent Information and Database Systems. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-981-96-6008-7_1