Gajendra Yadav

Structural parameter identification with hybrid physics informed neural network

Nikhil Mahar, Gajendra Yadav, Kajal Thakur, Subhamoy Sen, Laurent Mevel

TL;DR: An input-robust hybrid physics informed neural network (rHPINN) framework is proposed that integrates physics-based system dynamics with the temporal learning capability of HPINN, allowing accurate estimation of system states and spatial health parameters without input force measurements.

System identification (SI) is critical for ensuring the reliability of structural and mechanical components across engineering applications. Traditional model-based SI methods often struggle with complex dynamics and the scarcity of accurate physical models, while purely data-dri…Read more