905 Stochastic Modeling and Uncertainty Quantification
Recent developments in computational and sensing resources provide us the ability to infer about physical phenomena with increasingly detailed resolutions and to better characterize the interplay between experimentally observed cause and effect. In many problems of interest, this interplay is best described in a non-deterministic framework, permitting the description of experimental errors and inaccuracies, modeling errors and inadequacies, as well as numerical approximations.
These uncertainties conspire, with interpretation and analysis tools, to affect the predictive power of accumulated knowledge.
This minisymposium will bring together current research efforts attempting to characterize and manage uncertainties in various stages of the prediction process and also, to allow the visualization of uncertainties and the parameter sensibility in order to help the decision-making process. In particular, research in the following areas will be highlighted:
1. Experimental data representation.
2. Data assimilation and inverse analysis.
3. Uncertainty propagation.
4. Non-deterministic computational modeling.
5. Optimization and design under uncertainty.
6. Visualization of uncertainties.
7. Stochastic modeling.
8. Surrogate models.
9. Model-order reductions.
10. Small probability events.
11. Application examples and case studies.
12. Bayesian inference.