End-to-end Phase Field Model Discovery Combining Experimentation, Crowdsourcing, Simulation and Learning

• URL: http://arxiv.org/abs/2311.12801v1
• Date: Wed, 13 Sep 2023 22:44:04 GMT
• Title: End-to-end Phase Field Model Discovery Combining Experimentation, Crowdsourcing, Simulation and Learning
• Authors: Md Nasim, Anter El-Azab, Xinghang Zhang, Yexiang Xue
• Abstract summary: We present Phase-Field-Lab platform for end-to-end phase field model discovery. Phase-Field-Lab combines (i) a streamlined annotation tool which reduces the annotation time; (ii) an end-to-end neural model which automatically learns phase field models from data; and (iii) novel interfaces and visualizations. Our platform is deployed in the analysis of nano-structure evolution in materials under extreme conditions.
• Score: 9.763339269757227
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The availability of tera-byte scale experiment data calls for AI driven approaches which automatically discover scientific models from data. Nonetheless, significant challenges present in AI-driven scientific discovery: (i) The annotation of large scale datasets requires fundamental re-thinking in developing scalable crowdsourcing tools. (ii) The learning of scientific models from data calls for innovations beyond black-box neural nets. (iii) Novel visualization and diagnosis tools are needed for the collaboration of experimental and theoretical physicists, and computer scientists. We present Phase-Field-Lab platform for end-to-end phase field model discovery, which automatically discovers phase field physics models from experiment data, integrating experimentation, crowdsourcing, simulation and learning. Phase-Field-Lab combines (i) a streamlined annotation tool which reduces the annotation time (by ~50-75%), while increasing annotation accuracy compared to baseline; (ii) an end-to-end neural model which automatically learns phase field models from data by embedding phase field simulation and existing domain knowledge into learning; and (iii) novel interfaces and visualizations to integrate our platform into the scientific discovery cycle of domain scientists. Our platform is deployed in the analysis of nano-structure evolution in materials under extreme conditions (high temperature and irradiation). Our approach reveals new properties of nano-void defects, which otherwise cannot be detected via manual analysis.

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