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SIAM/ASA Journal on Uncertainty Quantification
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Statistical Inference for Conservation Law McKean–Vlasov SDEs via Deep Neural Networks

Statistical inference for conservation law McKean-Vlasov SDEs via deep neural networks
descriptionPublicationkeyboard_double_arrow_right Article 17 Apr 2025 English Publisher:Society for Industrial & Applied Mathematics (SIAM)Journal:SIAM/ASA Journal on Uncertainty Quantification, volume 13, pages 425-448 (eissn: 2166-2525, Copyright policy )
Authors: Denis Belomestny; Tatiana Orlova;

doi: 10.1137/24m1668949

Statistical Inference for Conservation Law McKean–Vlasov SDEs via Deep Neural Networks

Abstract

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Keywords

flux, McKean-Vlasov SDEs, deep neural networks, approximation complexity, rectified quadratic unit activations, Rate of convergence, degree of approximation, Artificial neural networks and deep learning, statistical inference

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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