MathExLab Internal Seminar

Internal Seminar Series — MathExLab

Interpretable AI for Nonlinear Structural Dynamics and a Benchmark for Nonlinear Mode Interaction

In this internal seminar, I presented my ongoing research on interpretable AI for nonlinear structural dynamics and benchmark design for nonlinear mode interaction.

Seminar Details

Abstract

I introduced a large controlled dataset for detecting nonlinearities in vibrating structures directly from raw time-series signals, together with a post-hoc interpretability pipeline based on Integrated Gradients, DeepLIFT, GradientSHAP, and DeepSHAP, as well as new quantitative metrics for testing attribution fidelity.

I also presented a large-scale benchmark for nonlinear mode interaction in two-degree-of-freedom oscillators, designed to support reproducible study of resonance, detuning, damping, nonlinear strength, forcing, and early interaction detection in complex physics-based systems.

For the mathematical foundations behind this work — covering how linear modes decouple, where nonlinear cross terms arise, and why internal resonance drives energy exchange — see the companion post: From Decoupled Linear Modes to Nonlinear Mode Interaction.

This seminar is part of the MathExLab Internal Seminar Series.