Energy-Consistent Neural Networks with Fenchel–Young Loss for Physics-Guided Energy Prediction in Sheet Metal Forming Under Small-Data Conditions
Seong-Su Jhang, Jae-Young Kwon, Won-Hee Lee, Hong-Gyu Park · 2026
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Description
This study addresses energy-response prediction in sheet metal forming under small-data conditions, where conventional simulation-based approaches are computationally expensive and data acquisition is limited. We propose an Energy-Informed Neural Network (EINN) framework that integrates energy consistency constraints and a Fenchel–Young duality-based loss to enforce physically consistent learning without relying on explicit governing equations.
Catalog record
| CREATOR | Seong-Su Jhang, Jae-Young Kwon, Won-Hee Lee, Hong-Gyu Park |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 44 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |