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Editorial: Special issue on uncertainty quantification, machine learning, and data-driven modeling of biological systems. (English) Zbl 1433.00040

From the text: This special issue on ‘Uncertainty Quantification, Machine Learning, and Data-Driven Modeling of Biological Systems’ provides a perspective of the U.S. Association for Computational Mechanics Technical Thrust Area ‘Biological Systems’ and seeks to place this theme within the broader field of computational mechanics.

MSC:

00B15 Collections of articles of miscellaneous specific interest
92-06 Proceedings, conferences, collections, etc. pertaining to biology
68-06 Proceedings, conferences, collections, etc. pertaining to computer science

Software:

MFclass
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References:

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[15] Zohdi, T. I., Rapid simulation-based uncertainty quantification of flash-type time-of-flight and Lidar-based body-scanning processes, Comput. Methods Appl. Mech. Engrg., 358, Article 112386 pp. (2020)
[16] Zhang, W.; Capilnasiu, A.; Sommer, G.; Holzapfel, G. A.; Nordsletten, D. A., An efficient and accurate method for modeling nonlinear fractional viscoelastic biomaterials, Comput. Methods Appl. Mech. Engrg. (2020)
[17] Grytz, R.; Krishnan, K.; Whitley, R.; Libertiaux, V.; Sigal, I. A.; Girkin, C. A.; Downs, J. C., A mesh-free approach to incorporate complex anisotropic heterogeneous material properties into eye-specific finite element models, Comput. Methods Appl. Mech. Engrg., 358, Article 112654 pp. (2020)
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