Scheduled presentations:

• Physics-informed machine learning for solving inverse problems
  *H. Son (Konkuk University)
• Hybrid AI: Homotopic Physics Relaxation for Sparse Parameter Discovery in Misspecified Models
  *D. WU (CNRS@CREATE), A. Ammar (CNRS@CREATE), F. Chinesta (CNRS@CREATE)
• Neural PDE Surrogates for American Options: Real-Time Prices, Greeks, and Implied Volatilities
  *S. Stoykov (FactSet Research Systems Inc.)
• Data Driven vs. Physics-Informed Neural Solvers for Aerodynamic Analysis and Design
  *S. Choi (Gwangju Institute of Science and Technology)
• Physics-Guided Machine Learning for Solving Transient Nonlinear Partial Differential Equations with Sparse Measurements and Optimal Sensor Placement
  *J. Lim (Kyung Hee University), J. Park (Jeonbuk National University), H. Noh (Korea Advanced Institute of Science and Techn)