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Divergence-free neural operators for stress field modeling in polycrystalline materials
Whereas PgFNO training is based solely on these data, that of the PiFNO and PeFNO is in addition constrained by the requirement that stress fields satisfy mechanical equilibrium, i. e., be divergence-free.
Computational Discovery of Energy-Efficient Heat Treatment for Microstructure Design using Deep Reinforcement Learning
In particular, the agent is capable of controlling the temperature to reach the desired microstructure starting from a variety of initial conditions.
Accelerating phase-field-based simulation via machine learning
Training input for this network is obtained from the results of the numerical solution of initial-boundary-value problems (IBVPs) based on the Fan-Chen model for grain microstructure evolution.
Lossless Multi-Scale Constitutive Elastic Relations with Artificial Intelligence
To demonstrate the accuracy and the efficiency of the trained CNN model, a Finite Element Method (FEM) based result of an elastically deformed nanoporous beam equipped with the CNN as constitutive law is compared with that by a full atomistic simulation.
Machine-learning-enhanced time-of-flight mass spectrometry analysis
Mass spectrometry is a widespread approach to work out what are the constituents of a material.
