Scheduled presentations:

• Using data-consistent inversion to build population-informed priors for Bayesian inference
  *R. White (Sandia National Laboratories), J. Jakeman (Sandia National Laboratories), T. Wildey (Sandia National Laboratories), T. Butler (University of Colorado Denver)
• An AI-based integrated framework for solving inversion problems in computational science
  *C. Heaney (Imperial College London), D. Guo (Imperial College London), B. Chen (Imperial College London), C. Pain (Imperial College London)
• Goal-Oriented Projection Based Model Order Reduction for Data Assimilation
  *P. Mollo (Technical University Eindhoven), K. Veroy-Grepl (Technical University Eindhoven)
• Distributed Computing for Physics-based Data-driven Reduced Modeling at Scale
  *I. Farcas (Virginia Tech), R. Gundevia (Jacobs Engineering Group, Inc.), R. Munipalli (Air Force Research Laboratory), K. Willcox (The University of Texas at Austin)
• Student Parameter Estimation in a Nonlinear Mechanical System Using an Adaptive Cubature Kalman Filter
  *L. Moyne (Polytechnique Montréal), E. Ghorbani (Polytechnique Montréal), O. Tuysuz (Polytechnique Montréal), F. Gosselin (Polytechnique Montréal)