Multiscale Modeling in Computational Biomechanics: When Contemporary AI Meets Virtual Human Twin

Dear Colleagues,

Computational biomechanics has been intensively investigated in the last several decades for the study of human body systems (musculoskeletal, cardiovascular, digestive, etc.) at multiple scales. Computational biomechanics is usually coupled with experimental biomechanics to enhance our understanding of the human body shape–function properties and their causal relationships. Thus, novel biomarkers and quantitative indicators have been extracted for clinical decision support and medical device optimization.

We organized a Special Issue on the “Multiscale Modeling in Computational Biomechanics: When Contemporary AI Meets Virtual Human Twin” and received valuable contributions on the development and evaluation of Virtual Human Twin at different scales. In particular, the use of contemporary AI approaches (e.g., deep learning and reinforcement learning) opens new avenues in generating new knowledge to advance the biomechanics field.

We are pleased to invite you to contribute to the second volume of this challenging and exciting field. This volume aims to bring methodological approaches and practical applications related to the Virtual Human Twin and especially its coupling with contemporary AI approaches (e.g., deep learning, generative AI, reinforcement learning, transfer learning, and federated learning).

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• AI-Driven Biomechanics in Medicine: Integration of AI techniques (e.g., deep learning and generative AI) with biomechanical modeling to improve diagnostics, predict disease progression, and optimize therapeutic interventions.
• Machine Learning for Biological Systems: Applications of machine learning for analyzing the mechanical behavior of cells, tissues, and organs, as well as for uncovering new insights into disease mechanisms.
• Generative AI in Biomechanics: Use of generative models for synthetic data generation, tissue and organ modeling, and the design of personalized medical devices.
• Transfer Learning for Multiscale Biomechanical Modeling: Leveraging pre-trained AI models to accelerate the development of patient-specific simulations and reduce the need for large datasets.
• Federated Learning in Biomedical Research: Privacy-preserving AI approaches for collaborative modeling and the analysis of biomechanical data across institutions, enabling large-scale studies without compromising data confidentiality.
• AI-Enhanced Medical Device Design: Application of AI-driven biomechanical modeling to optimize the design and functionality of implants, prosthetics, and wearable devices.
• Biomechanics of Biological Tissues: Computational modeling of the mechanical properties and behavior of soft tissues, hard tissues, and biofluids in health and disease.

I look forward to receiving your contributions.

Prof. Dr. Tien Tuan Dao
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• multiscale modeling
• computational biomechanics
• AI-driven biomechanics
• virtual human twin
• generative AI in medicine