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dc.contributor.authorAlber, Mark
dc.contributor.authorBuganza Tepole, Adrian
dc.contributor.authorCannon, William R
dc.contributor.authorDe, Suvranu
dc.contributor.authorDura-Bernal, Salvador
dc.contributor.authorGarikipati, Krishna
dc.contributor.authorKarniadakis, George
dc.contributor.authorLytton, William W
dc.contributor.authorPerdikaris, Paris
dc.contributor.authorPetzold, Linda
dc.contributor.authorKuhl, Ellen
dc.date.accessioned2023-04-10T16:03:07Z
dc.date.available2023-04-10T16:03:07Z
dc.date.issued2019-11-25
dc.identifier.citationAlber M, Buganza Tepole A, Cannon WR, De S, Dura-Bernal S, Garikipati K, Karniadakis G, Lytton WW, Perdikaris P, Petzold L, Kuhl E. Integrating machine learning and multiscale modeling-perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. NPJ Digit Med. 2019 Nov 25;2:115. doi: 10.1038/s41746-019-0193-y. PMID: 31799423; PMCID: PMC6877584.en_US
dc.identifier.eissn2398-6352
dc.identifier.doi10.1038/s41746-019-0193-y
dc.identifier.pmid31799423
dc.identifier.urihttp://hdl.handle.net/20.500.12648/8566
dc.description.abstractFueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance human health. The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunity in this regard. However, machine learning alone ignores the fundamental laws of physics and can result in ill-posed problems or non-physical solutions. Multiscale modeling is a successful strategy to integrate multiscale, multiphysics data and uncover mechanisms that explain the emergence of function. However, multiscale modeling alone often fails to efficiently combine large datasets from different sources and different levels of resolution. Here we demonstrate that machine learning and multiscale modeling can naturally complement each other to create robust predictive models that integrate the underlying physics to manage ill-posed problems and explore massive design spaces. We review the current literature, highlight applications and opportunities, address open questions, and discuss potential challenges and limitations in four overarching topical areas: ordinary differential equations, partial differential equations, data-driven approaches, and theory-driven approaches. Towards these goals, we leverage expertise in applied mathematics, computer science, computational biology, biophysics, biomechanics, engineering mechanics, experimentation, and medicine. Our multidisciplinary perspective suggests that integrating machine learning and multiscale modeling can provide new insights into disease mechanisms, help identify new targets and treatment strategies, and inform decision making for the benefit of human health.
dc.language.isoenen_US
dc.relation.urlhttps://www.nature.com/articles/s41746-019-0193-yen_US
dc.rights© The Author(s) 2019.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectComputational biophysicsen_US
dc.subjectComputational scienceen_US
dc.titleIntegrating machine learning and multiscale modeling-perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences.en_US
dc.typeArticle/Reviewen_US
dc.source.journaltitleNPJ digital medicineen_US
dc.source.volume2
dc.source.beginpage115
dc.source.endpage
dc.source.countryUnited States
dc.source.countryUnited States
dc.source.countryUnited States
dc.source.countryUnited States
dc.source.countryUnited States
dc.source.countryUnited States
dc.source.countryUnited States
dc.source.countryUnited States
dc.source.countryUnited States
dc.source.countryUnited States
dc.source.countryEngland
dc.description.versionVoRen_US
refterms.dateFOA2023-04-10T16:03:07Z
html.description.abstractFueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance human health. The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunity in this regard. However, machine learning alone ignores the fundamental laws of physics and can result in ill-posed problems or non-physical solutions. Multiscale modeling is a successful strategy to integrate multiscale, multiphysics data and uncover mechanisms that explain the emergence of function. However, multiscale modeling alone often fails to efficiently combine large datasets from different sources and different levels of resolution. Here we demonstrate that machine learning and multiscale modeling can naturally complement each other to create robust predictive models that integrate the underlying physics to manage ill-posed problems and explore massive design spaces. We review the current literature, highlight applications and opportunities, address open questions, and discuss potential challenges and limitations in four overarching topical areas: ordinary differential equations, partial differential equations, data-driven approaches, and theory-driven approaches. Towards these goals, we leverage expertise in applied mathematics, computer science, computational biology, biophysics, biomechanics, engineering mechanics, experimentation, and medicine. Our multidisciplinary perspective suggests that integrating machine learning and multiscale modeling can provide new insights into disease mechanisms, help identify new targets and treatment strategies, and inform decision making for the benefit of human health.
dc.description.institutionSUNY Downstateen_US
dc.description.departmentNathan Kline Institute for Psychiatric Researchen_US
dc.description.departmentPhysiology and Pharmacologyen_US
dc.description.degreelevelN/Aen_US
dc.identifier.journalNPJ digital medicine


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