Direct Prediction of Microstructure Evolution Utilizing Multi-Physics Informed Generative Artificial Intelligence
- 주제어 (키워드) Direct Prediction , Generative artificial intelligence , Physics-informed neural networks , Microstructure evolution , Multi-physics
- 발행기관 서강대학교 일반대학원
- 지도교수 김동철
- 발행년도 2024
- 학위수여년월 2024. 8
- 학위명 박사
- 학과 및 전공 일반대학원 기계공학과
- 실제 URI http://www.dcollection.net/handler/sogang/000000078826
- UCI I804:11029-000000078826
- 본문언어 영어
- 저작권 서강대학교 논문은 저작권 보호를 받습니다.
목차
I. Introduction 1
A. Degradation of Lithium (Li)-metal Batteries Due to Dendrite Growth 4
B. Advancements in Predicting Microstructure Evolution 6
II. Phase Field Modeling of Dendrite Growth in Li-metal Batteries 10
A. Governing Equations 10
Ⅱ.A.1 Dendrite Growth Model 10
Ⅱ.A.2 Dead Li Accumulation Model 14
B. Numerical Methods 18
C. Results and Discussion 22
III. Prediction of Dendrite Growth Utilizing Generative Artificial Intelligence (AI) 39
A. Generative Adversarial Networks (GANs) Model 41
B. Results and Discussion 49
IV. Multiphysics-informed Generative Adversarial Networks (Multi-PIGAN) 53
A. Physics-informed Neural Networks (PINNs) Model 53
B. Multi-PIGAN Model 56
C. Results and Discussion 59
V. Conclusion 66
Bibliography 69
