all AI news
Learning two-phase microstructure evolution using neural operators and autoencoder architectures. (arXiv:2204.07230v1 [cond-mat.mtrl-sci])
April 18, 2022, 1:11 a.m. | Vivek Oommen, Khemraj Shukla, Somdatta Goswami, Remi Dingreville, George Em Karniadakis
cs.LG updates on arXiv.org arxiv.org
Phase-field modeling is an effective mesoscale method for capturing the
evolution dynamics of materials, e.g., in spinodal decomposition of a two-phase
mixture. However, the accuracy of high-fidelity phase field models comes at a
substantial computational cost. Hence, fast and generalizable surrogate models
are needed to alleviate the cost in computationally taxing processes such as in
optimization and design of materials. The intrinsic discontinuous nature of the
physical phenomena incurred by the presence of sharp phase boundaries makes the
training of …
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Senior AI Engineer, EdTech (Remote)
@ Lightci | Toronto, Ontario
Data Scientist for Salesforce Applications
@ ManTech | 781G - Customer Site,San Antonio,TX
AI Research Scientist
@ Gridmatic | Cupertino, CA
Data Engineer
@ Global Atlantic Financial Group | Boston, Massachusetts, United States
Machine Learning Engineer - Conversation AI
@ DoorDash | Sunnyvale, CA; San Francisco, CA; Seattle, WA; Los Angeles, CA