Uncertainty Quantification

Computer models (also known as process models, mechanistic models, simulation models etc.) are used widely throughout science and engineering for making predictions, and for conducting ‘virtual experiments’ when physical experiments would be too costly or impractical. There will almost always be uncertainty in any model prediction, caused by uncertainty about what input values to use, and/or uncertainty about how well the model represents reality. We cannot trust a computer model prediction until we have quantified the uncertainty properly.

My interest in this topic began with my PhD, which was on propagating input uncertainty through computationally expensive models, using Gaussian process emulators. I continue to work on work on methods for dealing with input uncertainty, though I think the most important problems now are to do with how we quantify uncertainty about model discrepancy: the difference between a model prediction and reality.

Papers on Uncertainty Quantification

PhD Thesis