A physics-informed multi-fidelity approach for the estimation of differential equations parameters i...
A physics-informed multi-fidelity approach for the estimation of differential equations parameters in low-data or large-noise regimes
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English
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In this paper, we propose a multi-fidelity approach for parameter estimation problems based on Physics-Informed Neural Networks (PINNs). The proposed methods apply to models expressed by linear or nonlinear differential equations, whose parameters need to be estimated starting from (possibly partial and noisy) measurements of the model’s solution....
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A physics-informed multi-fidelity approach for the estimation of differential equations parameters in low-data or large-noise regimes
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TN_cdi_crossref_citationtrail_10_4171_rlm_943
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_crossref_citationtrail_10_4171_rlm_943
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ISSN
1120-6330
E-ISSN
1720-0768
DOI
10.4171/rlm/943
