Enhancing Digital Twin Intelligence: Key Considerations and a Vision
Sanja Lazarova-Molnar, Karlsruhe Institute of Technology (KIT), Germany
Simulating Sustainability: Challenges and Opportunities in Open-Source Agent-Based Platforms Like GAMA for Supporting Transdisciplinary Approaches
Alexis Drogoul, UMMISCO, IRD, France
Optimization with Simulation and Swarm Intelligence: Inspiration from Nature
Xin-She Yang, Middlesex University London, United Kingdom
Enhancing Digital Twin Intelligence: Key Considerations and a Vision
Sanja Lazarova-Molnar
Karlsruhe Institute of Technology (KIT)
Germany
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Brief Bio
Sanja Lazarova-Molnar holds two full professorships, at the Karlsruhe Institute of Technology, and the University of Southern Denmark. Her research focuses on data-driven simulation, digital twins, and cyber-physical systems modeling for reliability and energy efficiency enhancement. Actively engaged in developing advanced methodologies, she leverages her expertise to optimize complex systems through data-driven simulation. She leads activities focused on digital twins and data-driven simulation in several European and national projects. Furthermore, Professor Lazarova-Molnar assumes leadership roles in IEEE and The Society for Modeling & Simulation International (SCS), contributing significantly to these professional organizations. She was also one of the Proceedings Editors for the Winter Simulation Conference in 2019 and 2020 and an associate editor of SIMULATION: Transactions of The Society for Modeling and Simulation International.
Abstract
This talk offers a focused exploration of Digital twins, emphasizing their dynamic nature and goal-oriented design. It, furthermore, discusses the integration of human expertise into Digital Twins, highlighting its critical role in enhancing efficacy and accuracy. Through case studies, like data-driven fault tree analysis and reliability-oriented Digital Twins, examples will be provided of the fusion of data and expert knowledge. Finally, an overview of the pressing challenges for the new generation of Digital Twins that are able to benefit from “everything that can be known” will be presented and discussed. This presentation aims to advance the scientific discourse on Digital Twins, offering insights for their evolution towards enhanced intelligence and efficacy.
Simulating Sustainability: Challenges and Opportunities in Open-Source Agent-Based Platforms Like GAMA for Supporting Transdisciplinary Approaches
Alexis Drogoul
UMMISCO, IRD
France
Brief Bio
Alexis Drogoul holds a doctorate and a habilitation thesis in computer science. He was a professor at Sorbonne University from 1994 to 2004, and since 2005 he has been a senior researcher at IRD, the French National Research Institute for Sustainable Development. His work, which has been the subject of over 200 articles, focuses on the development of software tools (such as GAMA, http://gama-platform.org/) to support the modelling and simulation of complex socio-environmental systems, with a view to facilitating interdisciplinary work and involving social stakeholders in the construction of models. In this capacity, he has helped to define the fundamental concepts of 'agent-based modelling', while working on the applications of this paradigm to environmental decision support, particularly in Vietnam, where he has helped to set up several research laboratories on this theme since 2006.
Abstract
Open-source agent-based modeling and simulation platforms (ABPs) such as GAMA offer powerful tools for simulating the evolution of complex socio-environmental systems. However, the design, construction, maintenance, and evolution of these platforms pose significant research and software engineering challenges, particularly in fast-growing fields such as sustainability science, which, by its cross-disciplinary nature, emphasizes the use of inter- and trans-disciplinary methods while tackling issues of unprecedented complexity.
This translates into a growing demand for openness, coupling, participation, interactivity, interpretability, scalability and integration of modeling with the various scientific practices of sustainability, themselves increasingly based on the virtual exploration of potential scenarios.
After an introduction to GAMA and a brief account of its 17-year history, I will compare GAMA's current capabilities with these emerging demands, highlighting areas where research efforts, particularly in generative AI, and software engineering are needed. This presentation also aims to inform the future development of open source ABPs, ensuring that they remain at the forefront of open science practices and facilitate innovative research and stakeholder interaction in areas such as sustainability science. Examples will be drawn from current applications in Vietnam, which support transdisciplinary approaches through new visualization techniques (tangible interfaces, virtual reality) to enhance stakeholder engagement.
Optimization with Simulation and Swarm Intelligence: Inspiration from Nature
Xin-She Yang
Middlesex University London
United Kingdom
Brief Bio
Xin-She Yang obtained his DPhil in Applied Mathematics from the University of Oxford. He then worked at Cambridge University and then later at National Physical Laboratory (UK) as a Senior Research Scientist. Now he is Reader in Modelling and Optimization at Middlesex University London, and a co-Editor of the Springer Tracts in Nature-Inspired Computing. He is also an elected Fellow of the Institute of Mathematics and its Applications (FIMA), UK. He was the IEEE Computational Intelligence Society (CIS) chair for the Task Force on Business Intelligence and Knowledge Management (2015 to 2020). He has published more than 40 books and more than 350 peer-reviewed research papers with more than 90,000 citations, and he has been on the prestigious list of most influential researchers or highly-cited researchers (Web of Sciences) for eight consecutive years since 2016.
Abstract
Many design optimization problems in engineering and industry have stringent constraints with highly nonlinear, multimodal design objectives, and such optimization requires techniques beyond traditional gradient-based methods and Monte Carlo simulation. Nature-inspired, swarm intelligence-based optimization algorithms are a class of metaheuristic techniques for solving optimization problems. This talk summarizes the latest developments and highlights some open problems for further research.