Scheduled presentations:

• Multilinear Surrogates Modeling Wave-Type Phenomena
  *A. Gruber (Sandia National Laboratories), E. Cyr (Sandia National Laboratories), A. Vijaywargia (University of Texas at Austin)
• Self-Adaptive Model Order Reduction for Shock-dominated Problems
  A. Mohaghegh (University of Kansas), *C. Huang (University of Kansas)
• Structure-preserving Lift & Learn: Scientific machine learning for nonlinear conservative partial differential equations
  H. Sharma (University of Wisconsin Madison), J. Draxl Giannoni (Technische Universität München), B. Krämer (University of California, San Diego)
• Registration in bounded domains for model reduction of parametric conservation laws
  *T. Taddei (Sapienza University of Rome)
• Reduced-Order State Estimation of Fluid Flows via Diffusion-Augmented Particle Filtering
  *J. Tran (University of California, Los Angeles), J. Eldredge (University of California, Los Angeles), K. Taira (University of California, Los Angeles)
• Interpretable Physics-Informed Reduced Order Modeling: Closing the Simulation-to-Data Gap
  *H. Salehipour (Autodesk Research), N. Kutz (Autodesk Research), A. Butscher (Autodesk Research)