Matches in SemOpenAlex for { <https://semopenalex.org/work/W4287235514> ?p ?o ?g. }
Showing items 1 to 67 of
67
with 100 items per page.
- W4287235514 abstract "The unprecedented amount of data generated from experiments, field observations, and large-scale numerical simulations at a wide range of spatio-temporal scales have enabled the rapid advancement of data-driven and especially deep learning models in the field of fluid mechanics. Although these methods are proven successful for many applications, there is a grand challenge of improving their generalizability. This is particularly essential when data-driven models are employed within outer-loop applications like optimization. In this work, we put forth a physics-guided machine learning (PGML) framework that leverages the interpretable physics-based model with a deep learning model. The PGML framework is capable of enhancing the generalizability of data-driven models and effectively protect against or inform about the inaccurate predictions resulting from extrapolation. We apply the PGML framework as a novel model fusion approach combining the physics-based Galerkin projection model and long-short term memory (LSTM) network for parametric model order reduction of fluid flows. We demonstrate the improved generalizability of the PGML framework against a purely data-driven approach through the injection of physics features into intermediate LSTM layers. Our quantitative analysis shows that the overall model uncertainty can be reduced through the PGML approach especially for test data coming from a distribution different than the training data. Moreover, we demonstrate that our approach can be used as an inverse diagnostic tool providing a confidence score associated with models and observations. The proposed framework also allows for multi-fidelity computing by making use of low-fidelity models in the online deployment of quantified data-driven models." @default.
- W4287235514 created "2022-07-25" @default.
- W4287235514 creator A5028149900 @default.
- W4287235514 creator A5032407979 @default.
- W4287235514 creator A5037019234 @default.
- W4287235514 creator A5072774082 @default.
- W4287235514 creator A5085671233 @default.
- W4287235514 date "2021-04-09" @default.
- W4287235514 modified "2023-09-28" @default.
- W4287235514 title "Model fusion with physics-guided machine learning" @default.
- W4287235514 doi "https://doi.org/10.48550/arxiv.2104.04574" @default.
- W4287235514 hasPublicationYear "2021" @default.
- W4287235514 type Work @default.
- W4287235514 citedByCount "0" @default.
- W4287235514 crossrefType "posted-content" @default.
- W4287235514 hasAuthorship W4287235514A5028149900 @default.
- W4287235514 hasAuthorship W4287235514A5032407979 @default.
- W4287235514 hasAuthorship W4287235514A5037019234 @default.
- W4287235514 hasAuthorship W4287235514A5072774082 @default.
- W4287235514 hasAuthorship W4287235514A5085671233 @default.
- W4287235514 hasBestOaLocation W42872355141 @default.
- W4287235514 hasConcept C105795698 @default.
- W4287235514 hasConcept C108583219 @default.
- W4287235514 hasConcept C117251300 @default.
- W4287235514 hasConcept C119857082 @default.
- W4287235514 hasConcept C132459708 @default.
- W4287235514 hasConcept C134306372 @default.
- W4287235514 hasConcept C154945302 @default.
- W4287235514 hasConcept C202444582 @default.
- W4287235514 hasConcept C27158222 @default.
- W4287235514 hasConcept C2776459999 @default.
- W4287235514 hasConcept C33923547 @default.
- W4287235514 hasConcept C41008148 @default.
- W4287235514 hasConcept C50644808 @default.
- W4287235514 hasConcept C76155785 @default.
- W4287235514 hasConcept C9652623 @default.
- W4287235514 hasConceptScore W4287235514C105795698 @default.
- W4287235514 hasConceptScore W4287235514C108583219 @default.
- W4287235514 hasConceptScore W4287235514C117251300 @default.
- W4287235514 hasConceptScore W4287235514C119857082 @default.
- W4287235514 hasConceptScore W4287235514C132459708 @default.
- W4287235514 hasConceptScore W4287235514C134306372 @default.
- W4287235514 hasConceptScore W4287235514C154945302 @default.
- W4287235514 hasConceptScore W4287235514C202444582 @default.
- W4287235514 hasConceptScore W4287235514C27158222 @default.
- W4287235514 hasConceptScore W4287235514C2776459999 @default.
- W4287235514 hasConceptScore W4287235514C33923547 @default.
- W4287235514 hasConceptScore W4287235514C41008148 @default.
- W4287235514 hasConceptScore W4287235514C50644808 @default.
- W4287235514 hasConceptScore W4287235514C76155785 @default.
- W4287235514 hasConceptScore W4287235514C9652623 @default.
- W4287235514 hasLocation W42872355141 @default.
- W4287235514 hasOpenAccess W4287235514 @default.
- W4287235514 hasPrimaryLocation W42872355141 @default.
- W4287235514 hasRelatedWork W2169667994 @default.
- W4287235514 hasRelatedWork W3014300295 @default.
- W4287235514 hasRelatedWork W4223943233 @default.
- W4287235514 hasRelatedWork W4225161397 @default.
- W4287235514 hasRelatedWork W4309045103 @default.
- W4287235514 hasRelatedWork W4312200629 @default.
- W4287235514 hasRelatedWork W4312831135 @default.
- W4287235514 hasRelatedWork W4360585206 @default.
- W4287235514 hasRelatedWork W4364306694 @default.
- W4287235514 hasRelatedWork W4375852175 @default.
- W4287235514 isParatext "false" @default.
- W4287235514 isRetracted "false" @default.
- W4287235514 workType "article" @default.