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Found 5 papers, 1 papers with code
Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions
A post-processing step is then used to re-interpret the set of single-variable neural network mapping functions into mathematical form through symbolic regression.
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-training steps that accelerates and improve the robustness of the training of physics-informed neural network with auxiliary data stored in point clouds, (2) a net-to-net knowledge transfer algorithm that improves the weight initialization of the neural network and (3) a multi-objective optimization algorithm that may improve the performance of a physical-informed neural network with competing constraints.
Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation
This paper presents a computational framework that generates ensemble predictive mechanics models with uncertainty quantification (UQ).
Equivariant geometric learning for digital rock physics: estimating formation factor and effective permeability tensors from Morse graph
Comparisons among predictions inferred from training the CNN and those from graph convolutional neural networks (GNN) with and without the equivariant constraint indicate that the equivariant graph neural network seems to perform better than the CNN and GNN without enforcing equivariant constraints.
An accelerated hybrid data-driven/model-based approach for poroelasticity problems with multi-fidelity multi-physics data
We present a hybrid model/model-free data-driven approach to solve poroelasticity problems.
