Informing missing Physics with Model Form Error and Model Selection

Despite continuing advances in statistical inversion and modeling, model inadequacy due to model form error remains a concern in all areas of mathematical modeling. Much like physical models, calibrating a discrepancy model requires careful consideration regarding formulation, parameter estimation, and uncertainty quantification, each of which is often problem-specific. At the intersection of model form error quantification and model selection lies a systematic methodology for identifying and characterizing model form error as a means of identifying missing physics or unknown phenomena, which has the potential to inform model development and experimental design. One such methodology is presented.