A Hybrid Physics-Augmented Neural Network for Dynamic System Modeling with Partially Known Dynamics
Name
vehicles-08-00172-v2.pdf
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1.12 MB
Format
Adobe PDF
Checksum (MD5)
b94af8316101c07547a4924f1cff0487
Author(s)
Ludmann, Laurin
Choi, Jaeyoun
Neubeck, Jens
Wagner, Andreas
Fan, Chuchu
Date Issued
July 27, 2026
Journal
Vehicles
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Ludmann, L.; Choi, J.; Neubeck, J.; Wagner, A.; Fan, C. A Hybrid Physics-Augmented Neural Network for Dynamic System Modeling with Partially Known Dynamics. Vehicles 2026, 8, 172.
Version
Final published version
Abstract
This paper introduces Hybrid Physics-Augmented Neural Network (HyPA-Net), a hybrid modeling framework that integrates physics-based linear time-invariant models with artificial neural networks (ANNs) to address dynamic system modeling when only partial physical knowledge is available. The approach leverages the interpretability and robustness of established physical models while using ANNs—such as long short-term memory architectures—to capture unknown or nonlinear system behaviors. The methodology normalizes state and input variables for compatibility with ANN training and expands traditional recursive state-space equations for efficient backpropagation over sequences. Vehicle dynamics, specifically using a rear-wheel steering test case, validate the proposed framework. Various HyPA-Net configurations are benchmarked against pure physics-based and pure data-driven models, demonstrating improved prediction accuracy and model flexibility. The experimental results in this application confirm that hybrid models yield superior performance over strict physical approaches and can implicitly approximate submodel dynamics within a unified, yet modular, architecture, opening avenues for applications in domains where partial physics-based knowledge is available but insufficient on its own.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/vehicles8080172
