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Found 19 papers, 3 papers with code
Towards scientific machine learning for granular material simulations -- challenges and opportunities
Attended by researchers from both the granular materials (GM) and machine learning (ML) communities, a recent Lorentz Center Workshop on "Machine Learning for Discrete Granular Media" brought the ML community up to date with GM challenges.
A review on data-driven constitutive laws for solids
This review article highlights state-of-the-art data-driven techniques to discover, encode, surrogate, or emulate constitutive laws that describe the path-independent and path-dependent response of solids.
Physics-Informed Diffusion Models
Generative models such as denoising diffusion models are quickly advancing their ability to approximate highly complex data distributions.
Prediction of Effective Elastic Moduli of Rocks using Graph Neural Networks
This study presents a Graph Neural Networks (GNNs)-based approach for predicting the effective elastic moduli of rocks from their digital CT-scan images.
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.
Synthesizing realistic sand assemblies with denoising diffusion in latent space
In this paper, we introduce a denoising diffusion algorithm that uses a set of point clouds collected from the surface of individual sand grains to generate grains in the latent space.
Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties
The results of this study indicate that the denoising diffusion process is capable of creating microstructures of fine-tuned nonlinear material properties within the latent space of the training data.
Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter
This new data leads to a Bayesian update of the parameters by the KF, which is used to enhance the state representation.
Geometric deep learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticity
The history-dependent behaviors of classical plasticity models are often driven by internal variables evolved according to phenomenological laws.
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 networks to predict the anisotropic elastic response of the monoclinic organic molecular crystal $\beta$-HMX in the geometrical nonlinear regime.
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.
Sobolev training of thermodynamic-informed neural networks for smoothed elasto-plasticity models with level set hardening
We introduce a deep learning framework designed to train smoothed elastoplasticity models with interpretable components, such as a smoothed stored elastic energy function, a yield surface, and a plastic flow that are evolved based on a set of deep neural network predictions.
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-regret engineering applications, requires efficient and rigorous third-party calibration, validation and falsification.
Geometric deep learning for computational mechanics Part I: Anisotropic Hyperelasticity
To ensure smoothness and prevent non-convexity of the trained stored energy functional, we introduce a Sobolev training technique for neural networks such that stress measure is obtained implicitly from taking directional derivatives of the trained energy functional.
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, knowledge, and models that make predictions on constitutive responses of elasto-plastic materials.
Meta-modeling game for deriving theoretical-consistent, micro-structural-based traction-separation laws via deep reinforcement learning
This paper presents a new meta-modeling framework to employ deep reinforcement learning (DRL) to generate mechanical constitutive models for interfaces.
