Uncertainty quantification and probabilistic learning in computational dynamics

Co-organised by:  Christia Soiz1, Eleni Chatzi2Roger Ghanem3Fernando Rochinha4Steve WaiChing Sun5 

1 Université Gustave Eiffel, France, christian.soize@univ-eiffel.fr

2 ETH Zurich, Switzerland, chatzi@ibk.baug.ethz.ch

3 University of Southern California, United States, ghanem@usc.edu

4 COPPE - Universidade Federal do Rio de Janeiro, Brazil, faro@mecanica.coppe.ufrj.br

5 Columbia University, United States, wsun@columbia.edu

 

Abstract 

This mini-symposium focuses on the recent developments in uncertainty quantification and scientific machine learning, with applications in computational dynamics. The applications may concern, among others:

  • Computational solid dynamics.
  • Computational fluid dynamics.
  • Fluid-structure interactions and coupled problems.
  • Soil structure interactions in dynamics.
  • Wave propagation in random media and metamaterials.
  • Crack propagation in dynamics.
  • Structural health monitoring in dynamics and damage.
  • Designs of experiments and autonomous decision making.
  • Digital twins for dynamics problems.
  • Surrogate modeling.
  • Reduced-order modeling.    
  • Multiscale modeling. 
  • Optimization under uncertainties. 
  • Stochastic inverse problems, model updating, and identification methods. 
  • Uncertain multiphysics linear and nonlinear computational dynamics.

 

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