A0301
Title: Feature calibration for computer models
Authors: Wenzhe Xu - Beijing University of Posts and Telecommunications (China) [presenting]
Abstract: Computer model calibration involves using partial and imperfect observations of the real world to learn which values of a models input parameters lead to outputs that are consistent with real-world observations. When calibrating models with high dimensional output (e.g. a spatial field), it is common to represent the output as a linear combination of a small set of basis vectors. Often, when trying to calibrate to such output, what is important to the credibility of the model is that key emergent physical phenomena are represented, even if not faithfully or in the right place. In these cases, a comparison of model output and data in a linear subspace is inappropriate and will usually lead to poor model calibration. To overcome this, kernel-based history matching (KHM) is presented, generalizing the meaning of the technique sufficiently to be able to project model outputs and observations into a higher-dimensional feature space, where patterns can be compared without their location necessarily being fixed. The technical methodology is developed, presenting an expert-driven kernel selection algorithm, and then the techniques are applied to the calibration of boundary layer clouds for the French climate model IPSL-CM.
