Wednesday, July 15

Quantifying Uncertainty and Predictability in Mathematical Models

2:00 PM-4:00 PM
Room: Sidney Smith 1084

Quantifying the uncertainties in mathematical models is essential for making reliable predictions of complex phenomena. Well-informed decisions based on simulations require that we can identify the significance of the inherent variability of the physical system, the impact of the approximations made in formulating the model problem, the consequences of simulation errors when solving the approximate model, the sensitivity of the prediction to our limited knowledge of the state of the system and the probabilistic implications of the inherent stochastic effects that exist in most physical systems. We will discuss the importance of quantifying these uncertainties and describe new approaches for estimating their impact and sensitivity on the model predictions.

The minisymposium will be useful for anyone interested in quantifying the uncertainty in mathematical models predicting complex physical phenomena.

Organizer: James M. Hyman
Los Alamos National Laboratory
2:00 Quantifying Uncertainty in the Numerical Solution of Partial Differential Equations
James M. Hyman, Organizer
2:30 Optimal Prediction of Underresolved Dynamics
Raz Kupferman, Alexandre J. Chorin, and Anton P. Kast, Lawrence Berkeley National Laboratory
3:00 Model Reduction for Nonlinear Dynamical Systems: What Constitutes a Good Reduced Model?
Linda R. Petzold, University of California, Santa Barbara
3:30 Predictability of Complex Phenomena
James G. Glimm, State University of New York, Stony Brook
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LMH Created: 3/20/98 Updated: 6/22/98