14 citations · 14 across the 2 of their papers we have counts for
3 papers
cs.LG2021★ 14 cited
Training multi-objective/multi-task collocation physics-informed neural network with student/teachers transfer learnings
Bahador Bahmani, WaiChing Sun
This paper presents a PINN training framework that employs (1) pre-training steps that accelerates and improve the robustness of the training of physics-informed neural network wit…
cs.LG2021
Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation
Xiao Sun, Bahador Bahmani, Nikolaos N. Vlassis +2
This paper presents a computational framework that generates ensemble predictive mechanics models with uncertainty quantification (UQ). We first develop a causal discovery algorith…
cs.LG2020
An accelerated hybrid data-driven/model-based approach for poroelasticity problems with multi-fidelity multi-physics data
Bahador Bahmani, WaiChing Sun
We present a hybrid model/model-free data-driven approach to solve poroelasticity problems. Extending the data-driven modeling framework originated from Kirchdoerfer and Ortiz (201…