Dynamical Systems

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Period ending 2026-09-21

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Dynamical Systems

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System Identification

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Nonlinear Dynamics

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Qualitative Dynamics

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SOLIS: Physics-Informed Learning of Interpretable Neural Surrogates for Nonlinear Systems
Apr 16, 2026Murat Furkan Mansur, Tufan KumbasarConventional Physics-Informed Neural Networks System Identification
Learning Without Adversarial Training: A Physics-Informed Neural Network for Secure Power System State Estimation under False Data Injection Attacks
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Achieving $\widetilde{O}(1/ε)$ Sample Complexity for Bilinear Systems Identification under Bounded Noises
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Generating consensus and dissent on massive discussion platforms with a semantic-vector model
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Minimum distance classification for nonlinear dynamical systems
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Network Dynamics-Based Framework for Understanding Deep Neural Networks
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From system models to class models: An in-context learning paradigm
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Diffusion learning reveals viable parameter manifolds and compensation geometry in biological dynamical systems
Date pendingRuilin Zhang, Louis Tao, Zhuo-Cheng XiaoQualitative Dynamics Discrete Diffusion Model

Apr 16, 2026 · cs.LGJ/K move · Enter open · S save

SOLIS: Physics-Informed Learning of Interpretable Neural Surrogates for Nonlinear Systems

Murat Furkan Mansur, Tufan Kumbasar

AI and Intelligent Systems Laboratory, Istanbul Technical University, 34469, Istanbul, Türkiye

Nonlinear system identification must balance physical interpretability with model flexibility. Classical methods yield structured, control-relevant models but rely on rigid parametric forms that often miss complex nonlinearities, whereas Neural ODEs are expressive yet largely black-box. Physics-Informed Neural Networks (PINNs) sit between these extremes, but inverse PINNs typically assume a known governing equation with fixed coefficients, leading to identifiability failures when the true dynamics are unknown or state-dependent. We propose \textbf{SOLIS}, which models unknown dynamics via a \emph{state-conditioned second-order surrogate model} and recasts identification as learning a Quasi-Linear Parameter-Varying (Quasi-LPV) representation, recovering interpretable natural frequency, damping, and gain without presupposing a global equation. SOLIS decouples trajectory reconstruction from parameter estimation and stabilizes training with a cyclic curriculum and \textbf{Local Physics Hints} windowed ridge-regression anchors that mitigate optimization collapse. Experiments on benchmarks show accurate parameter-manifold recovery and coherent physical rollouts from sparse data, including regimes where standard inverse methods fail.

Conventional Physics-Informed Neural Networks System Identification Neural Ordinary Differential Equations Surrogate Models Robotwin

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