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September 10, 2025

Nonlinear Dynamic Modeling of Duffing Oscillators Using Fourier Neural Operators

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Embedding physical knowledge enhances the solution space of duffing oscillator models, indicating improved predictive capability.
The study shows how two-stage training can boost the robustness and generalization of neural operators in complex dynamical systems.
Results indicate that physics-informed approaches may better handle chaotic responses compared to standard models.
Assessment focuses on evaluating the impact of physical constraints in the context of nonlinear dynamics modeling.

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Analysis explores physics-informed Fourier neural operators in modeling the chaotic dynamics of Duffing oscillators.

A 2025 study studied this question.

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