We describe a general Bayesian approach for using computer codes for a complex physical system to assist in forecasting actual system outcomes. Our approach is based on expert judgements and experiments on fast versions of the computer code. These are combined to construct models for the relationships between the code's inputs and outputs, respecting the natural space/time features of the physical system. The resulting beliefs are systematically updated as we make evaluations of the code for varying input sets and calibrate the input space against past data on the system. The updated beliefs are then used to construct forecasts for future system outcomes. While the approach is quite general, it has been developed particularly to handle problems with high-dimensional input and output spaces, for which each run of the computer code is expensive. The methodology will be applied to problems in uncertainty analysis for hydrocarbon reservoirs.

We consider a process of one-dimensional fluid flow through a soil packed tube in which a contaminant is initially distributed. The contaminant concentration, as a function of location in the tube and time after flushing begins, is classically modeled as the solution of a linear second order partial differential equation. Here, we consider the related issues of how contaminant concentration measured at some location-time combinations can be used to approximate concentration at other locations and times (ie., exprimental design). The method is demonstrated for the case in which initial concentrations are approximated based on data collected only at the downstream end of the tube. Finally, the effect of misspecifying one of the model parameters is discussed, and alternative designs are developed for instances in which that parameter must be estimated from the data.

Dynamic singularities are ubiquitous. They arise in mathematical models of phenomena as grand as star formation or as familiar as the breakup of a thread of honey as it is being added to tea. Drop breakup allows one to study dynamics close to a singularity in a simple context which is also accessible to experiments. Recent works have revealed that a viscous liquid drop close to breakup looks self-similar---the drop profile looks the same if the length scales are rescaled appropriately. A new numerical strategy is developed to capture the drop breakup dynamics and show good agreement with experimental measurements. Surprisingly, the presence of even small amounts of viscous dissipation in the surrounding can dramatically alter the self-similar profile. In particular, when no exterior viscous dissipation is present, the thread profile is symmetric about the point of pinch-off. When small amounts of exterior viscous dissipation are present, the thread profile becomes severely asymmetric. An understanding of the final breakup process is crucial in elucidating the mechanisms underlying the formation of satellite drops, an issue relevant to the development of ink-jet printing technologies and emulsification processes.

A limit of a suitably-chosen string theory compactified on an elliptic Calabi-Yau threefold is believed to be equivalent to a Yang-Mills field theory with gravity in six dimensions. The Lie group associated to this Yang-Mills theory is encoded in the geometry of the Calabi-Yau space --- the Cartan subalgebra is generated by ruled surfaces and the weights of representations of fields appear as rational curves living inside the threefold. Cancellation of anomalies in the six-dimensional field theory predicts rather peculiar constraints on the configurations of such ruled surfaces and rational curves.

I will explain some basic concepts for fluid flow in porous media, go through some details about the 3D streamline simulator and SGSIM (Sequential Gaussian SIMulation) for generating random permeability fields under multiGaussian assumption.