Scheduled presentations:

• Machine Learning-based Domain Decomposition Approach via the Predefined Lagrange Multipliers
  *Y. Park (Seoul National University), S. Lee (Seoul National University), S. Lee (Samsung Electronics Co.), S. Kang (Sejong University), H. Cho (Jeonbuk National University), S. Shin (Seoul National University)
• Physics-Informed Machine Learning for Predicting Microstructure Evolution Including Grain Growth and Spinodal Decomposition
  *J. Park (KAIST), H. Park (POSTECH), H. Noh (KAIST), K. Kim (POSTECH), S. Lee (KAIST)
• A physics-informed multi-fidelity optimization framework for constrained aerodynamic optimization of high-speed elevator
  *C. He (Yangzhou University), J. Sun (Yangzhou University), K. Izui (Kyoto University), Z. Wang (Zhejiang University), L. Qiu (Zhejiang University; CANNY elevator), S. Zhang (Zhejiang University)
• Hyperparameter Optimization for Multi-Fidelity Surrogate Modeling Considering Physical Information
  *M. Lee (Korea advanced institute of science and techn), I. Lee (Korea advanced institute of science and techn)
• Physics-informed generative framework incorporating data importance for performance enhancement under data scarcity
  *G. Lee (KAIST), M. Lee (KAIST), I. Lee (KAIST)