A modular physics-informed neural network for nonlinear vibration isolators

AFT Neural Function Approximators for 1D Nonlinear Force Laws

Nonlinear contacts and friction strongly influence the vibration response of assembled structures, but their accurate numerical treatment is computationally demanding. The harmonic balance method is widely used to compute periodic steady-state responses, yet the required alternating frequency-time scheme becomes costly...

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