
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:
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Multifidelity Bayesian Optimization for Steady-State Predictions using Gyrokinetic Simulations of Plasma Turbulence
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Stochastic Finite Element Analysis of a Multiphysics Model for Heated Concrete
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NeurAM: nonlinear dimensionality reduction for uncertainty quantification through neural active manifolds
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UQ and Data-Based Learning employing Multi-Fidelity and Multi Physics Approaches targeting complex Problems in Biomechanics