A Generative Machine Learning Model for Material Microstructure 3D Reconstruction and Performance Evaluation

arXiv:2402.15815v1 Announce Type: new
Abstract: The reconstruction of 3D microstructures from 2D slices is considered to hold significant value in predicting the spatial structure and physical properties of materials.The dimensional extension from 2D to 3D is viewed as a highly challenging inverse problem from the current technological perspective.Recently,methods based on generative adversarial networks have garnered widespread attention.However,they are still hampered by numerous limitations,including oversimplified models,a requirement for a substantial number of training samples,and difficulties in achieving model convergence during training.In …

3d reconstruction abstract arxiv cond-mat.mtrl-sci cs.cv cs.lg current evaluation extension generative machine machine learning machine learning model material materials performance spatial type value