Aug 29, 2020 · 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.

Computer model calibration is a formal statistical procedure to infer input parameters by combining information from model runs and observational data. The ...

COMPUTER MODEL CALIBRATION WITH TIME SERIES DATA. 1. USING DEEP LEARNING AND QUANTILE REGRESSION∗. 2. SAUMYA BHATNAGAR† , WON CHANG ‡ , SEONJIN KIM§ , AND ...

This work proposes a new calibration framework based on a deep neural network (DNN) with long-short term memory layers that directly emulates the inverse ...

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 ...

Oct 22, 2024 · Adopting the 'learning with noise' idea we train our DNN model to filter out the effects from data model discrepancy on input parameter ...

Jan 5, 2022 · We also formulate a new way to construct interval predictions for DNN using quantile regression to quantify the uncertainty in input parameter ...

Sep 8, 2020 · Our focus is on calibration using time series data, which are one of the most common form of computer model output (Bayarri et al., 2007; Higdon ...

Nov 12, 2024 · Computer Model Calibration with Time Series Data Using Deep Learning and Quantile Regression. Editors. Year, 2022-03. Journal, SIAM/ASA Journal ...

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