MS060A Uncertainty Quantification and Scientific Machine Learning for Predictive Modeling of Complex Systems I
Main Organizer:
Dr.
Danial Faghihi
(
University at Buffalo
, United States
)
Chaired by:
Dr. Georgios Georgalis (GE Aerospace , United States)
Dr. Georgios Georgalis (GE Aerospace , United States)
Scheduled presentations:
-
Quantification and Propagation of Model Form Uncertainties in Simulation-based Digital Twins of Structures
-
Equation Learning for Agent-Based Infectious Disease Models
-
Scientific Machine Learning Methods for Spatial Correlation-Preserving Surrogate Models of HIV and EBV Infection
-
Probabilistic Surrogate Modelling for Spatially Resolved Data
-
Calibrated Uncertainty for Fine-tuned Scientific Foundation Models via Stochastic Attention
-
Adaptive Development and Validation of Large-Scale Multiphysics Simulations via Bayesian Networks
