[Iefac.list] Call for Papers: SMO Special Issue on Digital Twin

[Hu, Chao [M E]]

I didn't realize the attached CFP file couldn't be sent. Below is what is included in the CFP file. Sorry for any confusion.

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Structural and Multidisciplinary Optimization Special Issue on "Advanced Optimization Enabling Digital Twin Technology"

A digital twin is a real-time digital/virtual representation of a physical product or process. The digital twin technology is centered around "individualized" digital models that capture the unique characteristics of individual product or process units. These models allow decision making to be optimized for each product or process unit, rather than based on the average characteristics of the entire population. This emerging technology poses new and challenging optimization problems at the forefront of model-based design, smart manufacturing, industrial IoT, machine learning (ML), and predictive maintenance. The industry-scale adoption of the digital twin concept entails creating novel optimization solutions that use data coming in from sensors and inspections (physical-to-digital) to provide decision makers with actionable information (digital-to-physical), thereby closing the digitalization loop. Major benefits include the ability to optimize control/maintenance actions to individual units and the potential to optimize the design of next-generation products.

This Special Issue is dedicated to the current state-of-the-art and future directions of advanced optimization enabling the digital twin technology. It will include original papers with clear relevance to the optimization of structures, fluids, or another major physics, contributed by researchers and practitioners from the fields of engineering design, smart manufacturing, structural health monitoring, prognostics and health management, model-based predictive control, and others.

Topics of Interest to this Special Issue include, but are not limited to, the following:
* Physics-based modeling of physical products or processes to enable physics-based digital twins
* Data-driven modeling of physical products or processes to enable data-driven digital twins
* Integration of physics-based simulation and ML for fault diagnostics and failure prognostics
* Integration of physics-based simulation and ML for asset performance and health management
* Uncertainty and robustness evaluation in digital twin applications
* Evaluation, validation, and comparison of digital twin models/platforms
* Implementation of digital twin technology in cloud computing environment for industrial IoT applications (smart sensors and gateway enabling connectivity between a physical system and its digital counterpart)
* Applications of digital twin technology to various domains such as model-based product design, virtual design validation, smart manufacturing, structural health monitoring, and predictive maintenance

Important Dates
February 15th, 2022: Deadline for paper submission
March 31st, 2022: Completion of first-round reviews
May 15th, 2022: Deadline for revised paper submission
June 30th, 2022: Final decision notification (tentative)

Guest Editors (Co-GEs listed in alphabetical order)
Chao Hu, Associate Professor, Iowa State University, US, chaohu at iastate.edu<mailto:chaohu at iastate.edu>
- Research interests: design for reliability, machine learning, predictive maintenance
Vicente A. González, Associate Professor, University of Auckland, NZ, v.gonzalez at auckland.ac.nz<mailto:v.gonzalez at auckland.ac.nz>
- Research interests: lean construction 4.0, mixed reality, AI, construction engineering digital twinning
Taejin Kim, Assistant Professor, JeonBuk National University, South Korea, tjkim at jbnu.ac.kr<mailto:tjkim at jbnu.ac.kr>
- Research interests: machine learning, predictive maintenance, digital twin in robotics applications
Omer San, Associate Professor, Oklahoma State University, US, osan at okstate.edu<mailto:osan at okstate.edu>
- Research interests: physics-informed machine learning, fluid dynamics
Pai Zheng, Assistant Professor, Hong Kong Polytechnic University, Hong Kong, pai.zheng at polyu.edu.hk<mailto:pai.zheng at polyu.edu.hk>
- Research interests: smart product-service systems, smart manufacturing system, industrial AI

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Chao Hu, Ph.D.
Associate Professor
Department of Mechanical Engineering
Department of Electrical and Computer Engineering (courtesy)
2026 Black Engineering
Iowa State University
Ames, IA 50011 USA
Email: chaohu at iastate.edu<mailto:chaohu at iastate.edu> | Phone: +1-515-294-0771<tel:%2B1-515-294-0771>
Website: http://www.me.iastate.edu/chaohu
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