Artificial Intelligence Exploration (AIE) DARPA-PA-18-02-02 Artificial Intelligence Research Associate (AIRA)
Abstract
We propose a Bayesian framework to develop new machine learning and operator inference methods to aid the discovery of physical phenomena and the prediction of material properties and responses. We specifically target the challenges in material physics associated with systematic attempts to (a) abstract complexity from a hierarchy of scales into predictive model forms and (b) delineate mechanisms of coupled materials physics. Our project develops the following tasks that unite artificial intelligence (AI) with the discovery of emergent physics: (I) Scale bridging from quantum mechanics to continuum PDEs. (2) Physics discovery via system identification and operator inference. (3) Bayesian inference and uncertainty quantification for learning from data and quantifying predictive quality. (4) Optimal experimental design for intelligent data acquisition and management to achieve efficient high-level learning. The first two tasks address important application-specific problems and the next two tasks provide the AI interface.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 20, 2020
- Accession Number
- AD1121192
Entities
People
- Alex Gorodetsky
- Emmanuelle A. Marquis
- Karthik Duraisamy
- Krishna Garikipati
- Vikram Gavini
- Xun Huan
Organizations
- University of Michigan