MODELS 2022
Sun 23 - Fri 28 October 2022 Montréal, Canada
Thu 27 Oct 2022 15:30 - 15:52 at A-3502.1 - Applications II Chair(s): Judith Michael

Environmental uncertainties and hyperconnectivity force techno-socio-economic systems to introspect and adapt to succeed and survive. Current practice is chiefly intuition-driven which is inconsistent with the need for precision and rigor. We propose that this can be addressed through the use of digital twins by combining results from Modelling & Simulation, Artificial Intelligence, and Control Theory to create a risk free in silico experimentation aid to help: (i) understand why system is the way it is, (ii) be prepared for possible outlier conditions, and (iii) identify plausible solutions for mitigating the outlier conditions in an evidence-backed manner. We use reinforcement learning to systematically explore the digital twin solution space. Our proposal is significant because it advances the effective use of digital twin to new problem domains that have greater impact potential. Our novel approach contributes a meta model for simulatable digital twin of industry scale techno-socio-economic systems, agent-based implementation of the digital twin, and an architecture that serves as a risk-free experimentation aid to support simulation-based evidence-backed decision-making. We also discuss validation of this approach, associated technology infrastructure, and architecture through a representative sample of industry-scale real-world use cases.

Thu 27 Oct

Displayed time zone: Eastern Time (US & Canada) change

15:30 - 17:00
15:30
22m
Talk
Digital Twin as Risk Free Experimentation Aid for Techno-socio-economic SystemsP&I
Technical Track
Souvik Barat Tata Consultancy Services Research, Vinay Kulkarni Tata Consultancy Services Research, Tony Clark Aston University, Balbir Barn Middlesex University, UK
15:52
22m
Talk
Digital TwinCity: A Holistic Approach towards Comparative Analysis of Business ProcessesDemo
Tools & Demonstrations
16:15
22m
Talk
Facilitating the migration to the microservice architecture via model-driven reverse engineering and reinforcement learningJ1st
Journal-first
Shekoufeh Rahimi University of Isfahan, MohammadHadi Dehghani Johannes Kepler University Linz, Massimo Tisi IMT Atlantique, LS2N (UMR CNRS 6004), Dalila Tamzalit
Link to publication
16:37
22m
Talk
Towards Model-based Bias Mitigation in Machine LearningVirtualP&I
Technical Track
Alfa Yohannis University of York, Universitas Pradita, Dimitris Kolovos University of York