Functional Tensor Network Approximations for E3SM Land Model

Abstract

The predictive performance of the Energy Exascale Earth System Model (E3SM) is challenged by the modeling choices for a large ensemble of physical processes. This results in a large number of uncertain parameters and computationally expensive numerical simulations. To overcome these challenges, we will focus on constructing surrogate models that exploit the model structure via low-rank functional tensor networks approximations. We will employ a variational inference approach to construct a probabilistic model that approximates the discrepancy between the surrogate and the original model predictions. We will focus on the land model component of E3SM and present results pertaining to global sensitivity analysis and model calibration at a regional scale.