A modular physics-informed neural network for nonlinear vibration isolators

AFT Neural Function Approximators for 1D Nonlinear Force Laws

The approach points toward a reusable library of nonlinear-element surrogates that can be combined in arbitrary number and location within a mechanical system by bypassing the iterative force evaluation in time domain.

Hybrid Physics-Guided Neural Network for Vibration Sensor Nonlinearity Correction

Accelerometers built on micro-electromechanical systems (MEMS) play a critical role in structural monitoring and machinery diagnostics; however, their accuracy suffers from intrinsic nonlinearities—dead zones, hysteresis, saturation, and colored noise. Conventional physics-based correction methods are interpretable yet...

Neural topology optimization of ship structures under propulsion machinery vibrations

Ship structural vibrations contribute to noise, fatigue, and equipment damage, while dynamic-compliance topology optimization can produce pathological designs near resonance. This study extends neural-reparameterized topology optimization using a convolutional Kolmogorov-Arnold network (KATO) to forced-vibration design...

Dual-Output Physics-Informed Neural Networks for Real-Time Modeling of Low-Frequency Piezoelectric Energy Harvesters

An Explainable Physics-Informed Neural Frequency-Response Framework for Shunt-Parameter Identification in Semi-Active Piezoelectric Tuned Mass Dampers

This paper proposes a Physics-Informed Neural Frequency Response Framework for learning and interpreting the frequency-domain behavior of semi-active shunted piezoelectric tuned mass dampers. The motivation is that the behavior of such systems is most naturally expressed through frequency response functions, while the...

Dynamics Response and Dual-Function Validation of a Novel NES-GMM Unit Cell for Linear Structure