PROJECTION-BASED MODEL REDUCTION ALGORITHMS FOR INVERSE PROBLEMS

Abstract. This paper briefly describes the formulation and implementation of projection-based model reduction for the solution of deterministic and statistical inverse problems. The projection framework is introduced. We highlight the difficulty associated with identifying an efficient basis in the inverse problem setting. The additional challenge of understanding how reduced-order models treat uncertainty in the statistical setting is also presented