Scheduled presentations:

• Optimizing the spectral bias in Fourier feature physics-informed neural networks
  *F. Sahli Costabal (Pontificia Universidad Católica de Chile), M. Petrache (Pontificia Universidad Católica de Chile), M. Courdurier (Pontificia Universidad Católica de Chile), J. Molina (Pontificia Universidad Católica de Chile)
• High-Frequency PINN Solutions of the Helmholtz Equation via Joint Architectural and Optimization Design
  *M. Abedi (University of the Basque Country (EHU)), D. Pardo (University of the Basque Country (EHU)), T. Alkhalifah (KAUST)
• From Vanishing Viscosity Method to Neural Network Solvers for Eikonal Equations
  E. Kim (Samsung Electronics Co., Ltd.), Y. Park (University of California in Los Angeles), J. Han (University at Albany, State University of NY), M. Choi (Pohang University of Science and Technology), *J. Hahn (Czech Technical University in Prague)
• Exploring Architectural and Physics-Based Enhancements to Improve PINN learning of Adsorption CO2 Capture Processes
  S. Holm (SINTEF Energy Research), *S. Blakseth (SINTEF Energy Research), S. Subraveti (SINTEF Energy Research)
• Molecular Dynamics Analysis of Twin Boundary Migration in Mn-Cu-Ni-Fe Damping Alloys Using Neural Network Potential
  T. Furukawa (Fukuoka Institute of Technology), *A. Tomoda (Fukuoka Institute of Technology)
• Approximating the History Term in the Maxey-Riley-Gatignol Equations by Universal Differential Equations
  *F. Sommer (Hamburg University of Technology), V. Rathi (Hamburg University of Technology), S. Götschel (Hamburg University of Technology), D. Ruprecht (Hamburg University of Technology)