2 citations · 2 across the 2 of their papers we have counts for
3 papers
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)…