arXiv:2606.14708v1 Announce Type: cross Abstract: Data-driven machine learning approaches have become increasingly attractive for nonlinear system identification, but standard models often fail to preserve the underlying physical structure and remain difficult to interpret, especially when no analytical model is available. In this context, port-Hamiltonian (pH) models provide a natural physics-informed representation. However, when these models are parameterized with standard multilayer perceptrons (MLPs), the learned constitutive components often remain poorly interpretable. In this paper, we
The increasing complexity of AI models and the desire for more interpretable and physics-informed systems are driving innovation in neural network architectures.
This development could lead to more robust, reliable, and deployable AI systems, particularly in critical applications where physical integrity and interpretability are paramount.
AI models for system identification may become significantly more interpretable and inherently stable by leveraging physics-informed architectures like Port-Hamiltonian networks.
- · Industrial automation
- · Robotics
- · Control systems engineering
- · AI safety researchers
- · Black-box AI models in critical applications
- · MLPs in physics-constrained environments
Improved performance and interpretability of AI systems in identifying and controlling nonlinear physical systems.
Accelerated adoption of AI in sectors requiring high reliability and physical accuracy, such as aerospace and advanced manufacturing.
Potential for new regulatory frameworks around 'physically-assured' AI systems, creating a competitive advantage for technologies adhering to such principles.
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Read at arXiv cs.AI