Physics-Informed Data-Driven Surrogate Modeling for Full-Field 3D Microstructure and Micromechanical Field Evolution of Polycrystalline Materials

We have developed a machine learning-based crystal plasticity surrogate model (CP-SM) that can directly learn highly nonlinear material behavior during plastic deformation. CP-SM provides fast inference of spatially resolved three-dimensional (3D) microstructure and micromechanical fields and their...

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Veröffentlicht in:	JOM (1989) 2021-11, Vol.73 (11), p.3371-3382
Hauptverfasser:	Pokharel, Reeju, Pandey, Anup, Scheinker, Alexander
Format:	Artikel
Sprache:	eng
Schlagworte:	Chemistry/Food Science Crystal structure Datasets Deformation Direct numerical simulation Earth Sciences Engineering Environment Evolution Experiments Fourier transforms Informatics-Enabled Design of Structural Materials Inverse problems Machine learning Mathematical functions Mathematical models Microstructure Neural networks Partial differential equations Physics Plastic deformation Quaternions Simulation Strain Tensors
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