지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
이용수9
2023
Table of contents iList of Figures ivList of Tables xviABSTRACT xixChapter 1. Introduction 11.1 Research background 11.2 Research trends 41.3 Research objectives 8Chapter 2. Physics-informed neural networks for heat conduction and elasticity problems 92.1 Heat conduction problems 92.1.1 Governing equation 92.1.2 Physics-informed neural network 122.1.3 Self-adaptive physics-informed neural network 152.2 Elasticity problems 182.2.1 Governing equation 182.2.2 Physics-informed neural network 202.2.3 Self-adaptive physics-informed neural network 22Chapter 3. PINN-based surrogate model for a virtual sensor with real-time simulation 243.1 Heat conduction problems 243.2 Elasticity problems 27Chapter 4. Numerical examples 294.1 Heat conduction problems 304.1.1 PINN as a solver 304.1.1.1 One-dimensional bar with a given boundary temperature 304.1.1.2 Two-dimensional plate with a given boundary heat flux 344.1.2 PINN-based surrogate model 394.1.2.1 One-dimensional bar with a given boundary heat flux 394.1.2.2 Two-dimensional plate with a given boundary heat flux 554.1.2.3 Two-dimensional plate with unknown boundary heat flux 684.2 Elasticity problems 854.2.1 PINN as a solver 854.2.1.1 Two-dimensional infinite plate with a hole under uniaxial tension 854.2.2 PINN-based surrogate model 944.2.1.1 Two-dimensional plate with a hole under unknown biaxial tension 94Chapter 5. Conclusion 107References 109국 문 초 록 122
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