Special Journal Issue on Data-Driven Methods in Biomechanics

Dec 7, 2021

The ASME Journal of Biomechanical Engineering is currently accepting manuscripts for a special issue focusing on the topic “Data-Driven Methods in Biomechanics.” Authors who are interested in having their manuscripts included in the special issue, to be published in late August 2022, should submit their manuscripts by February 28, 2022.
The new advent in machine learning and data-driven methods has spread across engineering applications, including biomechanics, for constitutive modeling of soft tissue, physics solver replacement or acceleration, and analysis of imaging data to extract features that would be impossible to detect with traditional methods.  At the same time, machine learning and artificial intelligence algorithms have most likely evolved in different contexts other than biomechanics.  Biomechanical engineering systems have different features from the traditional domains of machine learning, and thus require unique data-driven approaches.  Recent efforts at this intersection are physics informed neural networks, and homogenization of micromechanics models with deep neural networks or Gaussian processes, to name only a couple of examples.
The special issue will foster the integration of engineering fields and machine learning by focusing on the unique intersection of data-driven methods and biomechanics.
The journal seeks contributions of new data-driven or machine learning methods and/or novel applications of data-driven methods to biomechanics problems, including but not limited to: constitutive models of tissues; image segmentation and registration; physics solver acceleration; and physics informed machine learning.
Manuscripts should be submitted electronically to the journal by February 28, 2022, via Journals Connect at journaltool.asme.org. Authors who have an account should log in and select “Submit Paper” at the bottom of the page. Authors without an account should select “Submissions” and follow the steps.  At the Paper Submittal page, authors should select “ASME Journal of Biomechanical Engineering” and then select the special issue “Data-Driven Methods in Biomechanics.”
Authors are encouraged to submit an abstract to one of the Guest Editors before the formal submission of the manuscript to gauge fitness to the special issue.
The guest editors for the special issue are Adrian Buganza Tepole, Purdue University, USA, abuganza@purdue.edu;
Jessica Zhang, Carnegie Mellon University, USA, jessicaz@andrew.cmu.edu; and Hector Gomez, Purdue University, USA, hectorgomez@purdue.edu.
For more information on the ASME Journal of Biomechanical Engineering, visit https://asmedigitalcollection.asme.org/biomechanical. To learn more about the ASME Journal Program, visit www.asme.org/publications-submissions/journals/information-for-authors.

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