136 citations · 140 across the 4 of their papers we have counts for
4 papers · 1 filter
Transfer learning for improved generalizability in causal physics-informed neural networks for beam simulations
Taniya Kapoor, Hongrui Wang, Alfredo Nunez +1
This paper introduces a novel methodology for simulating the dynamics of beams on elastic foundations. Specifically, Euler-Bernoulli and Timoshenko beam models on the Winkler found…
Neural oscillators for generalization of physics-informed machine learning
Taniya Kapoor, Abhishek Chandra, Daniel M. Tartakovsky +3
A primary challenge of physics-informed machine learning (PIML) is its generalization beyond the training domain, especially when dealing with complex physical problems represented…
Physics-informed machine learning for moving load problems
Taniya Kapoor, Hongrui Wang, Alfredo Núñez +1
This paper presents a new approach to simulate forward and inverse problems of moving loads using physics-informed machine learning (PIML). Physics-informed neural networks (PINNs)…
Physics-informed neural networks for solving forward and inverse problems in complex beam systems
Taniya Kapoor, Hongrui Wang, Alfredo Nunez +1
This paper proposes a new framework using physics-informed neural networks (PINNs) to simulate complex structural systems that consist of single and double beams based on Euler-Ber…