Picture for Bahador Bahmani

Bahador Bahmani

A Resolution Independent Neural Operator

Add code
Jul 17, 2024
Viaarxiv icon

A review on data-driven constitutive laws for solids

Add code
May 06, 2024
Figure 1 for A review on data-driven constitutive laws for solids
Figure 2 for A review on data-driven constitutive laws for solids
Figure 3 for A review on data-driven constitutive laws for solids
Figure 4 for A review on data-driven constitutive laws for solids
Viaarxiv icon

Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions

Add code
Jul 24, 2023
Viaarxiv icon

Manifold embedding data-driven mechanics

Add code
Dec 18, 2021
Figure 1 for Manifold embedding data-driven mechanics
Figure 2 for Manifold embedding data-driven mechanics
Figure 3 for Manifold embedding data-driven mechanics
Figure 4 for Manifold embedding data-driven mechanics
Viaarxiv icon

Training multi-objective/multi-task collocation physics-informed neural network with student/teachers transfer learnings

Add code
Jul 24, 2021
Figure 1 for Training multi-objective/multi-task collocation physics-informed neural network with student/teachers transfer learnings
Figure 2 for Training multi-objective/multi-task collocation physics-informed neural network with student/teachers transfer learnings
Figure 3 for Training multi-objective/multi-task collocation physics-informed neural network with student/teachers transfer learnings
Figure 4 for Training multi-objective/multi-task collocation physics-informed neural network with student/teachers transfer learnings
Viaarxiv icon

Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation

Add code
May 20, 2021
Figure 1 for Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation
Figure 2 for Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation
Figure 3 for Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation
Figure 4 for Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation
Viaarxiv icon

Equivariant geometric learning for digital rock physics: estimating formation factor and effective permeability tensors from Morse graph

Add code
Apr 12, 2021
Figure 1 for Equivariant geometric learning for digital rock physics: estimating formation factor and effective permeability tensors from Morse graph
Figure 2 for Equivariant geometric learning for digital rock physics: estimating formation factor and effective permeability tensors from Morse graph
Figure 3 for Equivariant geometric learning for digital rock physics: estimating formation factor and effective permeability tensors from Morse graph
Figure 4 for Equivariant geometric learning for digital rock physics: estimating formation factor and effective permeability tensors from Morse graph
Viaarxiv icon

An accelerated hybrid data-driven/model-based approach for poroelasticity problems with multi-fidelity multi-physics data

Add code
Nov 30, 2020
Figure 1 for An accelerated hybrid data-driven/model-based approach for poroelasticity problems with multi-fidelity multi-physics data
Figure 2 for An accelerated hybrid data-driven/model-based approach for poroelasticity problems with multi-fidelity multi-physics data
Figure 3 for An accelerated hybrid data-driven/model-based approach for poroelasticity problems with multi-fidelity multi-physics data
Figure 4 for An accelerated hybrid data-driven/model-based approach for poroelasticity problems with multi-fidelity multi-physics data
Viaarxiv icon