Scheduled presentations:

• Kernel Functions and Loss-embedded Closure Models for Discrepancy Modeling in Finite Strain Elastodynamics
  *A. Masud (University of Illinois Urbana-Champaign), I. Wijaya (University of Illinois Urbana-Champaign), J. Hickman (University of Illinois Urbana-Champaign)
• Topological Deep Learning for PDE Simulations on Meshes
  *L. Tesán (Universidad de Zaragoza, I3A), D. González (Universidad de Zaragoza, I3A), P. Martins (Universidad de Zaragoza, I3A), E. Cueto (Universidad de Zaragoza, I3A)
• Uncertainty Quantification of Foundation Models for Scientific Machine Learning
  *M. ZAKI (JOHNS HOPKINS UNIVERSITY), P. Thiagarajan (JOHNS HOPKINS UNIVERSITY), S. Chakraborty (INDIAN INSTITUTE OF TECHNOLOGY DELHI), M. Shields (JOHNS HOPKINS UNIVERSITY)
• Deep Operator Gaussian Process for Uncertainty Quantification of Operator Learning
  *J. Lee (Chung-Ang University), Y. Shin (North Carolina State University)
• Learning to Choose Optimizers
  *M. van der Schelling (Delft University of Technology), D. Toshniwal (Delft University of Technology), M. Bessa (Brown University)
• Development of a low-order model of Li-Ion cell using machine learning techniques
  *Z. Buliński (Silesian University of Technology), W. Adamczyk (Silesian University of Technology), T. Krysiński (Silesian University of Technology), A. Schydlo (TRATON R&D Germany GmbH)