
IS062A Uncertainty quantification and Data-driven approaches for Multi-Fidelity, Multi-physics, and Multi-Scale problems I
Main Organizer:
Prof.
Daniele Schiavazzi
(
University of Notre Dame
, United States
)
Scheduled presentations:
-
Multifidelity Bayesian Optimization for Steady-State Predictions using Gyrokinetic Simulations of Plasma Turbulence
-
Stochastic Finite Element Analysis of a Multiphysics Model for Heated Concrete
-
NeurAM: nonlinear dimensionality reduction for uncertainty quantification through neural active manifolds
-
UQ and Data-Based Learning employing Multi-Fidelity and Multi Physics Approaches targeting complex Problems in Biomechanics