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08/30/2023
Neural network-based multiscale modeling of finite strain magneto-elasticity with relaxed convexity criteria
We present a framework for the multiscale modeling of finite strain magn...
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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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06/07/2023
Synthesizing realistic sand assemblies with denoising diffusion in latent space
The shapes and morphological features of grains in sand assemblies have ...
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02/24/2023
Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties
In this paper, we introduce a denoising diffusion algorithm to discover ...
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09/27/2022
Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter
Experimental data is costly to obtain, which makes it difficult to calib...
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07/30/2022
Geometric deep learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticity
The history-dependent behaviors of classical plasticity models are often...
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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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11/29/2021
MD-inferred neural network monoclinic finite-strain hyperelasticity models for β-HMX: Sobolev training and validation against physical constraints
We present a machine learning framework to train and validate neural net...
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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
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 poroe...
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08/11/2020
An immersed phase field fracture model for fluid-infiltrating porous media with evolving Beavers-Joseph-Saffman condition
This study presents a phase field model for brittle fracture in fluid-in...
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04/13/2020
A non-cooperative meta-modeling game for automated third-party calibrating, validating, and falsifying constitutive laws with parallelized adversarial attacks
The evaluation of constitutive models, especially for high-risk and high...
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01/08/2020
ILS-MPM: an implicit level-set-based material point method for frictional particulate contact mechanics of deformable particles
Finite element simulations of frictional multi-body contact problems via...
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01/08/2020
Geometric deep learning for computational mechanics Part I: Anisotropic Hyperelasticity
This paper is the first attempt to use geometric deep learning and Sobol...
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01/04/2020
A phase field model for cohesive fracture in micropolar continua
While crack nucleation and propagation in the brittle or quasi-brittle r...
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03/08/2019
A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation
We introduce a multi-agent meta-modeling game to generate data, knowledg...
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10/24/2018
