research
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07/24/2023
Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions
Conventional neural network elastoplasticity models are often perceived ...
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12/18/2021
Manifold embedding data-driven mechanics
This article introduces a new data-driven approach that leverages a mani...
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07/24/2021
Training multi-objective/multi-task collocation physics-informed neural network with student/teachers transfer learnings
This paper presents a PINN training framework that employs (1) pre-train...
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05/20/2021
Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation
This paper presents a computational framework that generates ensemble pr...
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04/12/2021
Equivariant geometric learning for digital rock physics: estimating formation factor and effective permeability tensors from Morse graph
We present a SE(3)-equivariant graph neural network (GNN) approach that ...
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11/30/2020