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Structural constitutive models for soft biological tissues and biomaterials: the role of mechanical interactions

Mechanics of Soft Materials 2025年第1期7卷 1-39页

作者： Motiwale, Shruti Sacks, Michael S. James T. Willerson Center for Cardiovascular Modeling and Simulation The Oden Institute for Computational Engineering and Sciences Austin USA The Department of Mechanical Engineering The University of Texas at Austin Austin USA The Department of Biomedical Engineering The University of Texas at Austin Austin USA

In this review, we explore the development of the structural constitutive models for soft biological tissues and biomaterials, focusing on the role of mechanical interactions. Soft tissues, such as myocardium, heart valves, skin, and other fibrous biological structures, display complex, non-linear mechanical behaviors driven by their fibrous composition. While traditional constitutive models have been widely adopted to describe the anisotropic and hyperelastic nature of these tissues, they often fall short in capturing the detailed mechanical interactions between fibers, the ground matrix, and other tissue components under diverse loading conditions. To address these gaps, we focus on the development of meso-structural models that account for mechanism-driven fiber-matrix and fiber-fiber interactions. We delve into the kinematics of these interactions, highlighting the roles of fiber rotation, extension, and shear, and how these mechanisms differ across various tissues and biomaterials. Our review draws on experimental evidence from studies on exogenously crosslinked tissues, myocardium and electrospun polycarbonate urethane biomaterials, which reveal the presence of significant mechanical interactions that influence the overall stress response. In addition, we also discuss recent advancements in computational methods for implementing these models, particularly neural network-based approaches that mitigate the computational challenges typically associated with structural models while maintaining high predictive accuracy. Finally, we propose future directions for multiscale modeling to enhance our understanding of fiber network topologies and their mechanical roles in both native tissues and biomaterials.

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Matrix Stiffness Regulates Mechanotransduction and Vascular Network Formation of Hipsc-Derived Endothelial Progenitors Encapsulated in 3d Hydrogels

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SSRN 2025年

作者： Han, Jiwan Halwachs, Kathleen West, Toni Larsen, Bryce Sacks, Michael S. Rosales, Adrianne M. Zoldan, Janet Department of Biomedical Engineering The University of Texas at Austin AustinTX United States Department of Chemical Engineering The University of Texas at Austin AustinTX United States James T. Willerson Center for Cardiovascular Modeling and Simulation The Oden Institute for Computational Engineering and Sciences AustinTX United States

The mechanical properties of the extracellular matrix (ECM), particularly stiffness, regulate endothelial progenitor responses during vascular development, yet their behavior in physiologically compliant matrices (10 kPa), our results emphasize that vascularization is not solely driven by the most mechanotransductive environment but rather by a balance of compliance, contractility, and cell-induced remodeling. These findings underscore the need to design hydrogels that provide sufficient mechanotransduction for endothelial maturation while maintaining compliance to support dynamic vascular morphogenesis. This work provides a mechanically tuned framework for optimizing microenvironments to balance endothelial differentiation and vascular network formation in tissue engineering and regenerative medicine. © 2025, The Authors. All rights reserved.

关键词： Endothelial cells

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NUMERICAL FLOW VISUALIZATION: VISTA AND EXPEDITION

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Journal of Flow Visualization and Image Processing 2022年第3期29卷 1-27页

作者： Liu, Zhanping Department of Computational Modeling and Simulation Engineering Old Dominion University ODU-CMSE 1300 ECSB 4700 Elkhorn Avenue NorfolkVA23529 United States

Numerical flow visualization resorts to data processing, feature extraction, geometric construc-tion, image synthesis, graphical display, and scene manipulation to enable intuitive insightful interactive analysis of tangential directions of movement embedded in a velocity vector field. This paper presents some advances in numerical flow visualization over the past three decades, with emphasis on geometry-based and texture-based methods. Also introduced in this context is our own research for representing, depicting, and exploring flow data. Not intended for an exhaustive survey of the literature, our discussions revolve around streamline integration, interactive streamline placement, automatic streamline placement (including evenly spaced layout of streamlines), streamline clustering, time-dependent flow lines (e.g., pathlines), flow texture synthesis (e.g., line integral convolution), feature mining (e.g., flow topology), and parallel vi-sualization. These topics address flows ranging from planar to surface and volumetric domains, from steady to unsteady cases, and from Cartesian to curvilinear and unstructured grids. This scope allows us to examine a variety of approaches in the working mechanism, numerical accu-racy, representation effectiveness, visual quality, and computational performance so as to show the respective advantages and disadvantages. The findings may help visualization researchers pinpoint main long-lasting challenges, visualization developers identify practically applicable techniques, and domain scientists perceive important promising advances in numerical flow visualization. © 2022 by Begell House, Inc.

关键词： Textures

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Data retrieval from building information models based on visual programming

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Visualization in engineering 2017年第1期5卷 1-14页

作者： Preidel, Cornelius Daum, Simon Borrmann, André Chair of Computational Modeling and Simulation TUM Department of Civil Geo and Environmental Engineering Arcisstraße Munich 2180333 Germany

Background: With the rising adoption of Building Information modeling (BIM) in the AEC sector, computational models supersede traditional ways of information provision based on textual documents and two-dimensional drawings. The use of models enables the streamlining of workflows, and the included virtual construction increases the quality of the final product, the building. To create a comprehensive description of a planned building, information from different sources must be combined, specified and regularly updated by the project’s stakeholders. The emerging models are highly structured, and instance files entail large amounts of data. However, in an unprocessed state, these models are of limited suitability for performing engineering tasks as the amount and structure does not match the domain-specific and purpose-oriented views. Methods: Selection and filtering data for the user’s needs is a well-understood task in computer science, and various approaches are available. A promising approach is the usage of formal query languages. In this paper, selected common query languages are examined and assessed for processing building model information. Based on the analysis, we come to the conclusion that textual query languages are too complex to be employed by typical end users in the construction industry such as architects and engineers. Results: To overcome this issue, two Visual Programming Languages representing a new, more intuitive mechanism for data retrieval are introduced. The first one, QL4BIM, is designed for general filtering of IFC models, the second one, VCCL, has been developed for Code Compliance Checking. Both languages provide operators based on the Relational Algebra to allow handling of relations - a highly required feature of BIM QLs. Conclusions: The paper concludes with a discussion of the strengths and limitations of visual programming languages in the BIM context. © 2017, The Author(s).

关键词： Building information modeling Information retrieval Relational algebra Visual programming

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Artificial Intelligence Applications in the Development of Autonomous Vehicles:A Survey

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IEEE/CAA Journal of Automatica Sinica 2020年第2期7卷 315-329页

作者： Yifang Ma Zhenyu Wang Hong Yang Lin Yang Department of Information Technology and Decision Sciences Old Dominion UniversityNorfolkVA 23529 USA Department of Computational Modeling and Simulation Engineering Old Dominion UniversityNorfolkVA 23529 USA Department of Mechanical and Electrical Engineering Huazhong Agricultural UniversityWuhan 430070China

The advancement of artificial intelligence(AI)has truly stimulated the development and deployment of autonomous vehicles(AVs)in the transportation *** by big data from various sensing devices and advanced computing resources,AI has become an essential component of AVs for perceiving the surrounding environment and making appropriate decision in *** achieve goal of full automation(i.e.,self-driving),it is important to know how AI works in AV *** research have made great efforts in investigating different aspects of applying AI in AV ***,few studies have offered the research community a thorough examination of current practices in implementing AI in ***,this paper aims to shorten the gap by providing a comprehensive survey of key studies in this research ***,it intends to analyze their use of AIs in supporting the primary applications in AVs:1)perception;2)localization and mapping;and 3)decision *** investigates the current practices to understand how AI can be used and what are the challenges and issues associated with their *** on the exploration of current practices and technology advances,this paper further provides insights into potential opportunities regarding the use of AI in conjunction with other emerging technologies:1)high definition maps,big data,and high performance computing;2)augmented reality(AR)/virtual reality(VR)enhanced simulation platform;and 3)5G communication for connected *** paper is expected to offer a quick reference for researchers interested in understanding the use of AI in AV research.

关键词： Artificial intelligence(AI) autonomous vehicles(AVs) deep learning(DL) motion planning perception self-driving

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Towards a deep learning potential for anhydrous proton transport

Towards a deep learning potential for anhydrous proton trans...

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2020 AIChE Annual Meeting

作者： Achar, Siddarth Johnson, J. Karl Computational Modeling and Simulation Program University of Pittsburgh United States Department of Chemical Engineering University of Pittsburgh United States

ISBN: (纸本)9780816911141

Standard density functional theory (DFT) molecular dynamic (MD) simulations are prohibitively expensive in terms ofcompute time and memory requirements when the size of the physical system is larger than several hundred *** alternate approach is to develop a deep learning neural network potential that can accurately reproduce DFTenergies within an error of several 10s of meVs. Neural network potentials thus allow large-scale MD simulations formuch longer times than can be achieved with DFT-MD, but with comparable accuracy. We have used the recentlypublished DeePMD [1] formalism to develop potentials for systems relevant to modeling proton transport onfunctionalized graphane. We have used DFT-MD calculations for small structures of graphane and graphanol astraining data to build reliable machine-learned potentials. Apart from energies and forces, vibrational and thermalproperties such as phonon dispersion relations and thermal expansion coefficients from DFT-MD were used to validatethe accuracy of our trained DeePMD potentials. Our goal is to develop a machine learning potential that is able toaccurately predict proton conduction on graphanol in the complete absence of water. Previous DFT calculations haveshown that graphanol a promising candidate for anhydrous proton exchange membrane applications [2]. We seek toidentify large-scale and long-time proton hopping behavior for this system through the use of machine learningpotentials. © 2020 American Institute of Chemical Engineers. All rights reserved.

关键词： Density functional theory

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Proposed unified discrete event simulation content roadmap for M&S curricula

Proposed unified discrete event simulation content roadmap f...

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2017 Winter simulation Conference, WSC 2017

作者： Leathrum, James F. Mielke, Roland R. Collins, Andrew J. Audette, Michel A. Department of Modeling Simulation and Visualization Engineering Old Dominion University 1300 Engineering and Computational Sciences Building NorfolkVA23529 United States

ISBN: (纸本)9781538634288

This paper presents a new educational roadmap for teaching discrete event system (DES) simulation software design. This roadmap represents the hierarchical structure and inter-relationships characterizing the worldviews of DES simulation, namely, event scheduling and process interaction. The roadmap was developed from the authors' experience while teaching DES simulation to both undergraduate and graduate students, spanning several years. The roadmap's development was motivated by the need to strive for greater completeness as well as fewer inconsistencies in the material curriculum. The commonality between the worldviews is highlighted in striving for a uniform approach. The intent of this paper is to provide other educators a foundation for their own DES simulation course development. A simple example is used to illustrate the worldviews within the roadmap. © 2017 IEEE.

关键词： Curricula

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Detection and Classification of PCB Defects using Deep Learning Methods 31

Detection and Classification of PCB Defects using Deep Learn...

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31st IEEE Microelectronics Design and Test Symposium, MDTS 2022

作者： Legon, Andria Deo, Makarand Albin, Sacharia Audette, Michel Old Dominion University Department of Computational Modeling and Simulation Engineering NorfolkVA United States Norfolk State University Department of Engineering NorfolkVA United States

ISBN: (纸本)9781665468169

Automatic inspection methods are needed to cost-effectively discover defects in fabricated printed circuit boards (PCBs). This paper adapts a deep convolutional neural network design, called RetinaNet, to simultaneously locate and classify defects in PCBs. To improve detection performance, several network designs with regularization and dropout hyperparameters were tested. Not all defects render a PCB nonfunctional. This paper proposes re-evaluating network performance downweighing cosmetic defects. Adding a 25% dropout rate to each layer of the ResNet backbone, achieved an impressive 95.03% accuracy and 97.09% F-score on PCB images from a publicly available dataset. The study demonstrates the effectiveness of designing a single deep learning network to reliably and accurately detect as well as classify defects in PCB images. Our design can be extended to identification of defects in microelectronic circuits and semiconductor circuits with suitable modifications. © 2022 IEEE.

关键词： Printed circuit boards

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Runtime Energy Savings Based on Machine Learning Models for Multicore Applications

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Journal of Computer and Communications 2022年第6期10卷 63-80页

作者： Vaibhav Sundriyal Masha Sosonkina Department of Computational Modeling and Simulation Engineering Old Dominion University Norfolk USA

To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to maximize energy savings under a given performance degradation. Machine learning techniques were utilized to develop performance models which would provide accurate performance prediction with change in operating core-uncore frequency. Experiments, performed on a node (28 cores) of a modern computing platform showed significant energy savings of as much as 26% with performance degradation of as low as 5% under the proposed strategy compared with the execution in the unlimited power case.

关键词： Machine Learning RAPL DVFS Uncore Frequency Scaling Energy Savings Performance modeling

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A New MS engineering Program with A Base in Computer engineering

A New MS Engineering Program with A Base in Computer Enginee...

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2021 Winter simulation Conference, WSC 2021

作者： Leathrum, James F. Shen, Yuzhong Gonzalez, Oscar Old Dominion University Department of Computational Modeling and Simulation Engineering 1300 Engineering and Computational Sciences Building NorfolkVA23529 United States Old Dominion University Department of Electrical and Computer Engineering NorfolkVA23529 United States

ISBN: (纸本)9781665433112

The reality of the current academic climate, in particular faced with drops in enrollment over the next decade as a result from drops in birth rates, is forcing hard choices for small programs such as the modeling simulation engineering (MSE) program at Old Dominion University (ODU). The quality of the program and its benefit to its constituents do not offset the impracticality of continuing such programs. Two primary options for such programs are closure or consolidation. ODU decided on the latter course of action for MSE. Due to the computational nature of the existing program, a decision was made to place MSE as a major under the Computer engineering degree. This paper presents the justification for this decision and the resulting curriculum and the hard decisions made to allow it to fit under computer engineering. A discussion of feedback from constituents such as the industrial advisory board for the existing MSE program is included. © 2021 IEEE.

关键词： Drops

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