4 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 2 cited
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…
cs.LG2023
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)…
cs.LG2022★ 4 cited
GraTO: Graph Neural Network Framework Tackling Over-smoothing with Neural Architecture Search
Xinshun Feng, Herun Wan, Shangbin Feng +4
Current Graph Neural Networks (GNNs) suffer from the over-smoothing problem, which results in indistinguishable node representations and low model performance with more GNN layers.…