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Learning Latent Hardening (LLH): Enhancing Deep Learning with Domain Knowledge for Material Inverse Problems

1. 연구의 배경 및 필요성 데이터 부족 문제 : 재료 역문제는 실험 비용·시간이 많이 소요돼 대규모 데이터셋 확보가 어려움. 기존 딥러닝 모델은 대량 데이터에 의존하는 경향이 있어, 데이터가 제한된 상황에서 과적합·일반화 오류가 발생한다. 도메인 지식 활용 : 재료 과학에서는 응력‑변형률 곡선이 미세구조와 직접 연결되는 물리적 관계가 잘 알려져 있다. 이를 모델에 명시적으로 주입하면 학습 효율을 크게 높일 수 있다는 가설이 제시된다. 2. 제안된 LLH 프레임워크 | 단계 | 핵심 아이디어 | 구현 방법 | 기대 효과 | | |

Learning Latent Hardening (LLH): Enhancing Deep Learning with Domain Knowledge for Material Inverse Problems

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