Apr 22, 2022 · We propose error-in-variables (EiV) models for two operator regression methods, MOR-Physics and DeepONet, and demonstrate that these new models ...
The objective of this work is to explore the bias in models learned from noisy independent variables and to propose a correction, applicable to a wide variety ...
Abstract. Deep operator learning has emerged as a promising tool for reduced order modeling and PDE model discovery. Leveraging the expressive power of deep ...
Apr 22, 2022 · We propose error-in-variables (EiV) models for two operator regression methods, MOR-Physics and DeepONet, and demonstrate that these new models ...
Error-in-variables modelling for operator learning. Ravi G. Patel1 , Indu ... EiV model for learning the Burgers operator – MOR-. Physics. 11. Recover ...
[PDF] Error-in-variables modelling for operator learning benchmarks
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ABSTRACT. Operator learning has emerged as a promising technique for PDE and reduced-order modeling. However, least squares (LS) estimates inferred from ...
In statistics, errors-in-variables models or measurement error models are regression models that account for measurement errors in the independent variables.
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Jul 19, 2022 · The Compensated Matrix Uncertainty selector. 2. Page 3. ERROR-IN-VARIABLES MODELLING FOR OPERATOR LEARNING method (Rosenbaum and Tsybakov, 2013) ...
Apr 3, 2023 · In this study, we develop a novel algorithm for measurement error modelling, which could in principle take any regression model fitted by ...
Missing: operator | Show results with:operator
Jan 16, 2024 · A parameter estimation method called the two-stage error-in-variable model was introduced to the models that have both dependent and ...