Scheduled presentations:

• Keynote Smoother or rougher: how to generate data to learn operators?
  *Y. Yu (Lehigh University), J. Wang (Huazhong University of Science and Technology), F. Lu (Johns Hopkins University)
• Operator Learning for Inelastic Homogenization
  K. Bhattacharya (California Institute of Technology), *L. Cao (California Institute of Technology), G. Stepaniants (California Institute of Technology), A. Stuart (California Institute of Technology), M. Trautner (California Institute of Technology)
• Hyperbolic State-Space Models for Hierarchical Representation Learning
  S. Patil (DS/NJIT), A. Pandey (DS/NJIT), I. Koutis (CS/NJIT), *M. Xu (DS/NJIT)
• Automatic discovery of optimal meta-solvers via multi-objective optimization
  Y. Lee (Brown University), S. Liu (Brown University), *J. Darbon (Brown University), G. Karniadakis (Brown University)
• State-dependent temperature control in Langevin diffusions using numerical exploratory Hamiltonian-Jacobi-Bellman equations
  T. Wang (Worcester Polytechnic Institute), X. Li (Hongkong Polytechnic University), G. Wang (Worcester Polytechnic Institute), *Z. Zhang (Worcester Polytechnic Institute)