Collaborative Research: SHF: Medium: OASIS: An Open-Source AI-Driven EDA Tool for Real-Tim — NSF Award to OHIO STATE UNIVERSITY, T
Physics-Informed Neural Networks (PINNs) are an emerging class of Artificial Intelligence (AI) models that incorporate physical laws directly into their architecture, enabling fast and accurate simulations even with limited or noisy data. They show significant promise for electromagnetic (EM) simulations, particularly
| Award title | Collaborative Research: SHF: Medium: OASIS: An Open-Source AI-Driven EDA Tool for Real-Tim |
|---|---|
| Award ID | 2504340 |
| Awardee | OHIO STATE UNIVERSITY, THE |
| City | COLUMBUS |
| State | OH |
| Amount obligated | $300,000 |
| Principal investigator | Asimina Kiourti |
| Program | Software & Hardware Foundation |
| Start date | 10/01/2025 |
| Abstract | Physics-Informed Neural Networks (PINNs) are an emerging class of Artificial Intelligence (AI) models that incorporate physical laws directly into their architecture, enabling fast and accurate simulations even with limited or noisy data. They show significant promise for electromagnetic (EM) simulations, particularly in managing parameter variations in real time. However, ensuring both accuracy and stability in PINN training remains a major challenge, often requiring large datasets and exhibiti |
| Source | NSF Awards |
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