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Activity Number: 318 - Robust Regression Methods: From Independent Observations to Spatial Dependence
Type: Topic Contributed
Date/Time: Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
Sponsor: International Indian Statistical Association
Abstract #322989
Title: Computer Model Calibration with Time Series Data Using Deep Learning and Quantile Regression
Author(s): Seonjin Kim* and Saumya Bhatnagar and Won Chang and Jiali Wang
Companies: Miami Univeristy and University of Cincinnati and University of Cincinnati and Argonne National Laboratory
Keywords: Computer Model Calibration; Deep Learning; Long short-term Memory Network; Data-Model Discrepancy
Abstract:

Computer models play a key role in many scientific and engineering problems. One major source of uncertainty in computer model experiments is input parameter uncertainty. Computer model calibration is a formal statistical procedure to infer input parameters by combining information from model runs and observational data. The existing standard calibration framework suffers from inferential issues when the model output and observational data are high-dimensional dependent data, such as large time series due to the difficulty in building an emulator and the non-identifiability between effects from input parameters and data-model discrepancy. To overcome these challenges, we propose a new calibration framework based on a deep neural network (DNN) with Long short-term memory layers that directly emulates the inverse relationship between the model output and input parameters. Adopting the ‘learning with noise’ idea, we train our DNN model to filter out the effects from data model discrepancy on input parameter inference. We also formulate a new way to construct interval predictions for DNN using quantile regression to quantify the uncertainty in input paramter estimates.


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