Yielding physically-interpretable emulators – A Sparse PCA approach

Friday, 18 December 2015: 10:50
3014 (Moscone West)
Stefano Galelli, Singapore University of Technology and Design, Singapore, Singapore, Ahmad Alsahaf, Politecnico di Milano, Milano, Italy, Matteo Giuliani, Polytechnic University of Milan, Milan, Italy and Andrea Castelletti, Politecnico di Milano, Milano, 20133, Italy
Projection-based techniques, such as Principal Orthogonal Decomposition (POD), are a common approach to surrogate high-fidelity process-based models by lower order dynamic emulators. With POD, the dimensionality reduction is achieved by using observations, or ‘snapshots’ – generated with the high-fidelity model –, to project the entire set of input and state variables of this model onto a smaller set of basis functions that account for most of the variability in the data. While reduction efficiency and variance control of POD techniques are usually very high, the resulting emulators are structurally highly complex and can hardly be given a physically meaningful interpretation as each basis is a projection of the entire set of inputs and states. In this work, we propose a novel approach based on Sparse Principal Component Analysis (SPCA) that combines the several assets of POD methods with the potential for ex-post interpretation of the emulator structure. SPCA reduces the number of non-zero coefficients in the basis functions by identifying a sparse matrix of coefficients. While the resulting set of basis functions may retain less variance of the snapshots, the presence of a few non-zero coefficients assists in the interpretation of the underlying physical processes. The SPCA approach is tested on the reduction of a 1D hydro-ecological model (DYRESM-CAEDYM) used to describe the main ecological and hydrodynamic processes in Tono Dam, Japan. An experimental comparison against a standard POD approach shows that SPCA achieves the same accuracy in emulating a given output variable – for the same level of dimensionality reduction – while yielding better insights of the main process dynamics.