Accelerating the design of compositionally complex materials via physics-informed artificial intelligence
The chemical space for designing materials is practically infinite. This makes disruptive
progress by traditional physics-based modeling alone challenging. Yet, training data for …
progress by traditional physics-based modeling alone challenging. Yet, training data for …
A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: Comparison with finite element …
Physics informed neural networks (PINNs) are capable of finding the solution for a given
boundary value problem. Here, the training of the network is equivalent to the minimization …
boundary value problem. Here, the training of the network is equivalent to the minimization …
Modeling and simulation of microstructure in metallic systems based on multi-physics approaches
The complex interplay between chemistry, microstructure, and behavior of many
engineering materials has been investigated predominantly by experimental methods …
engineering materials has been investigated predominantly by experimental methods …
[HTML][HTML] Deep CNNs as universal predictors of elasticity tensors in homogenization
B Eidel - Computer Methods in Applied Mechanics and …, 2023 - Elsevier
In the present work, 3D convolutional neural networks (CNNs) are trained to link random
heterogeneous, multiphase materials to their elastic macroscale stiffness thus replacing …
heterogeneous, multiphase materials to their elastic macroscale stiffness thus replacing …
An artificial neural network for surrogate modeling of stress fields in viscoplastic polycrystalline materials
The purpose of this work is the development of a trained artificial neural network for
surrogate modeling of the mechanical response of elasto-viscoplastic grain microstructures …
surrogate modeling of the mechanical response of elasto-viscoplastic grain microstructures …
Mixed formulation of physics‐informed neural networks for thermo‐mechanically coupled systems and heterogeneous domains
Physics‐informed neural networks (PINNs) are a new tool for solving boundary value
problems by defining loss functions of neural networks based on governing equations …
problems by defining loss functions of neural networks based on governing equations …
Machine-learning-based surrogate modeling of microstructure evolution using phase-field
Phase-field-based models have become common in material science, mechanics, physics,
biology, chemistry, and engineering for the simulation of microstructure evolution. Yet, they …
biology, chemistry, and engineering for the simulation of microstructure evolution. Yet, they …
FE2 Computations with Deep Neural Networks: Algorithmic Structure, Data Generation, and Implementation
Multiscale FE 2 computations enable the consideration of the micro-mechanical material
structure in macroscopical simulations. However, these computations are very time …
structure in macroscopical simulations. However, these computations are very time …
Emerging Directions for Carbon Capture Technologies: A Synergy of High-Throughput Theoretical Calculations and Machine Learning
As the world grapples with the challenges of energy transition and industrial
decarbonization, the development of carbon capture technologies presents a promising …
decarbonization, the development of carbon capture technologies presents a promising …
Data-driven multiscale simulation of solid-state batteries via machine learning
A Asheri, M Fathidoost, V Glavas, S Rezaei… - Computational Materials …, 2023 - Elsevier
The battery cell performance is determined by electro-chemo-mechanical mechanisms on
different length scales. Though there exist multi-field multiscale simulation frameworks, the …
different length scales. Though there exist multi-field multiscale simulation frameworks, the …