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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Veröffentlicht in:	Archives of computational methods in engineering Jg. 28; H. 3; S. 1017 - 1037
Hauptverfasser:	Peng, Grace C. Y., Alber, Mark, Buganza Tepole, Adrian, Cannon, William R., De, Suvranu, Dura-Bernal, Savador, Garikipati, Krishna, Karniadakis, George, Lytton, William W., Perdikaris, Paris, Petzold, Linda, Kuhl, Ellen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.05.2021 Springer Nature B.V
Schlagworte:	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 Biomedicine Physics-based simulation Multiscale modeling Machine learning physics-based simulation biomedicine multiscale modeling
ISSN:	1134-3060, 1886-1784, 1886-1784
Online-Zugang:	Volltext
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