Quantifying Uncertainty in E3SM via Functional Tensor Network Approximations


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 which makes both forward and inverse uncertainty quantification studies difficult. 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 approximate the components of the large scale model with functional forms tailored to the model behavior. We will then cast the training of the functional tensor network model in a Bayesian framework and use a Stein 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.

Apr 12, 2022 12:00 AM — 12:00 AM
Atlanta, GA

I am contributing to the following presentations at this conference:

  • Arun S. Hegde, Elan Weiss, Wolfgang Windl, Habib N. Najm, Cosmin Safta, Bayesian Calibration of Interatomic Potential Models for Binary Alloys
  • Luke Boll, Katherine M. Johnston, Khachik Sargsyan, Cosmin Safta, Bert J. Debusschere, The UQTk C++/Python Toolkit for Uncertainty Quantification: Overview and Applications
  • Ari Frankel, Cosmin Safta, Reese Jones, Graph Convolutional Neural Networks for Microstructure Homogenization with Quantified Uncertainty
  • Daniel Ricciuto, Khachik Sargsyan, Cosmin Safta, Spatio-Temporal Land Model Calibration Using Karhunen-Loeve Expansions
Cosmin Safta
Cosmin Safta
Distinguished Member of Technical Staff

My research interests include uncertainty quantification, machine learning, and statistics.