Scheduled presentations:

• Accelerating Ultrasonic Wavefield Simulations Using Physics-informed Neural Networks
  N. Munir (GIST), *H. Oh (GIST)
• Physics-Informed Deep Operator Network for Predicting Temperature Field of a Lithium-Ion Battery Under Variable Operating Conditions
  *Y. Oh (KAIST), J. Kim (KAIST), B. Park (KAIST), J. Park (KAIST), B. Lee (KAIST), S. Lee (KAIST)
• 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)
• 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 Neural Network-Based Discovery of Hyperelastic Constitutive Models from Extremely Scarce Data
  *H. Moon (Korea Advanced Institute of Science and Techn)
• Nonparametric Identification of Multimaterial Structure via Physics-Informed Neural Networks
  *S. Lee (UNIST), H. Chung (UNIST)