42 citations · 155 across the 16 of their papers we have counts for
4 papers · 1 filter
Manifold embedding data-driven mechanics
Bahador Bahmani, WaiChing Sun
This article introduces a new data-driven approach that leverages a manifold embedding generated by the invertible neural network to improve the robustness, efficiency, and accurac…
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…
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…
Equivariant geometric learning for digital rock physics: estimating formation factor and effective permeability tensors from Morse graph
Chen Cai, Nikolaos Vlassis, Lucas Magee +6
We present a SE(3)-equivariant graph neural network (GNN) approach that directly predicting the formation factor and effective permeability from micro-CT images. FFT solvers are es…