EAGER: Generative AI for Learning Emergent Complexity in Mechanics-driven Coupled Physics — NSF Award to University of Southern Ca
In this EArly-concept Grant for Exploratory Research (EAGER) project, artificial intelligence (AI) methods that can learn from, and make predictions on, simulations of the physics of materials will be developed. The approach in this project will constitute an extension of the capabilities of recent AI platforms, of whi
| Award title | EAGER: Generative AI for Learning Emergent Complexity in Mechanics-driven Coupled Physics |
|---|---|
| Award ID | 2427856 |
| Awardee | University of Southern California |
| City | LOS ANGELES |
| State | CA |
| Amount obligated | $300,000 |
| Principal investigator | Krishnakumar Garikipati |
| Program | Mechanics of Materials and Str |
| Start date | 09/01/2024 |
| Abstract | In this EArly-concept Grant for Exploratory Research (EAGER) project, artificial intelligence (AI) methods that can learn from, and make predictions on, simulations of the physics of materials will be developed. The approach in this project will constitute an extension of the capabilities of recent AI platforms, of which OpenAI's ChatGPT, Microsoft's Copilot, and Google's Gemini, are among the best known. These AI platforms have caught the public's imagination and are widely used in virtually ev |
| Source | NSF Awards |
$799/mo
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