Multiscale Modeling Meets Machine Learning: What Can We Learn?
Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology,...
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| Vydané v: | Archives of computational methods in engineering Ročník 28; číslo 3; s. 1017 - 1037 |
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| Hlavní autori: | , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Dordrecht
Springer Netherlands
01.05.2021
Springer Nature B.V |
| Predmet: | |
| ISSN: | 1134-3060, 1886-1784, 1886-1784 |
| On-line prístup: | Získať plný text |
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| Abstract | Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems. |
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| AbstractList | Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems. Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems. |
| Author | Garikipati, Krishna Dura-Bernal, Savador Lytton, William W. Karniadakis, George Kuhl, Ellen De, Suvranu Cannon, William R. Petzold, Linda Peng, Grace C. Y. Buganza Tepole, Adrian Alber, Mark Perdikaris, Paris |
| Author_xml | – sequence: 1 givenname: Grace C. Y. surname: Peng fullname: Peng, Grace C. Y. organization: National Institutes of Health – sequence: 2 givenname: Mark surname: Alber fullname: Alber, Mark organization: University of California – sequence: 3 givenname: Adrian surname: Buganza Tepole fullname: Buganza Tepole, Adrian organization: Purdue University – sequence: 4 givenname: William R. surname: Cannon fullname: Cannon, William R. organization: Pacific Northwest National Laboratory – sequence: 5 givenname: Suvranu surname: De fullname: De, Suvranu organization: Rensselaer Polytechnic Institute – sequence: 6 givenname: Savador surname: Dura-Bernal fullname: Dura-Bernal, Savador organization: State University of New York – sequence: 7 givenname: Krishna surname: Garikipati fullname: Garikipati, Krishna organization: University of Michigan – sequence: 8 givenname: George surname: Karniadakis fullname: Karniadakis, George organization: Brown University – sequence: 9 givenname: William W. surname: Lytton fullname: Lytton, William W. organization: State University of New York – sequence: 10 givenname: Paris surname: Perdikaris fullname: Perdikaris, Paris organization: University of Pennsylvania – sequence: 11 givenname: Linda surname: Petzold fullname: Petzold, Linda organization: University of California – sequence: 12 givenname: Ellen orcidid: 0000-0002-6283-935X surname: Kuhl fullname: Kuhl, Ellen email: ekuhl@stanford.edu organization: Stanford University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34093005$$D View this record in MEDLINE/PubMed |
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| Keywords | Biomedicine Physics-based simulation Multiscale modeling Machine learning physics-based simulation biomedicine multiscale modeling |
| Language | English |
| License | Terms of use and reuse: academic research for non-commercial purposes, see here for full terms. https://www.springer.com/aam-terms-v1 |
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| SubjectTerms | Artificial intelligence Biological models (mathematics) Boundary conditions Electrophysiology Engineering Machine learning Mathematical and Computational Engineering Object recognition Original Paper Parameter identification Parameter sensitivity Radiology Robustness (mathematics) System dynamics |
| Title | Multiscale Modeling Meets Machine Learning: What Can We Learn? |
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