MIMESIS Joint Research Lab (2023 - 2028)

Advancing Human-Centric Systems Engineering

Exploring the potential of the Industrial Metaverse for Systems Engineering

The Motivations

Overcoming the Barriers to MBSE Adoption

Compared to traditional Document-Based Systems Engineering, Model-Based Systems Engineering (MBSE) offers significant theoretical advantages for managing complex engineering projects. Practitioners perceive that the paradigm shift to a model-centric approach improves the complete capture of systems engineering and architectural knowledge. Furthermore, MBSE facilitates better communication, information sharing, improved consistency, traceability, systems understanding, complexity management, and the capacity for data reuse. Despite these reported benefits, the widespread adoption of MBSE is hindered by several critical barriers

  • The User Blind Spot in MBSE Tool Design: Tool development frequently prioritizes rigid standards over usability. This oversight alienates non-specialist audiences by forcing them to navigate steep learning curves.
  • Over-Reliance on Software Paradigms: Because modeling languages like SysML are rooted in software engineering, systems are frequently designed to look like software. This bias often ignores the spatial and physical realities of engineered products.
  • Esoteric Visual Notation: The use of esoteric graphical representations and poor diagrammatic visual notations acts as a massive barrier to cross-disciplinary communication. These highly codified models are difficult for non-experts to decode and comprehend effectively during design reviews.
  • Mental Integration Challenges: Information about a system is typically spread across multiple diagrams, requiring users to navigate various displays to find relevant data. Consequently, architects and stakeholders struggle with the perceptual and conceptual integration processes needed to synthesize this dispersed information into a cohesive mental representation.
  • The MBSE-CAD Gap: A severe lack of convergence persists between the world of systems and other specific engineering domains. Specifically, there is a persistent separation between MBSE methodologies and 3D Computer-Aided Design (CAD) environments, creating a gap between systems logic and physical geometry.
  • Fragmented Product-Process Design: Difficulties remain in efficiently co-architecting a product's architecture alongside its enabling industrial systems. This sequential rather than concurrent approach creates a major barrier to achieving true industrial product-process co-design.

Our Approach

Where MBSE Meets HCI

MIMESIS'Lab addresses these fundamental barriers by shifting toward a human-centered approach in the Industrial Metaverse. To overcome the limitations of traditional diagrammatic representations, our research focuses on the intersection of Model-Based Systems Engineering (MBSE) and Human-Computer Interaction (HCI), emphasizing the importance of visually rich and interactive 3D user interfaces. To overcome the limitations of traditional diagrammatic representations, our research focuses on the intersection of Model-Based Systems Engineering (MBSE) and Human-Computer Interaction (HCI), emphasizing the importance of visually rich and interactive 3D user interfaces. By replacing fragmented, heavily codified 2D diagrams with interactive 3D virtual environments, we transform abstract system data into intuitive, concrete representations. This significantly reduces the cognitive effort required to navigate complex digital threads, enabling multidisciplinary teams to understand and co-architect systems without needing to master esoteric modeling languages.

We invent collaborative, highly interactive 3D systems modeling interfaces by mixing three fundamental interaction paradigms:

Computer-as-Tool (HCI)

To extend engineers' capabilities. We equip systems architects and core team members with interactive - immersive or not - visualisation to review and co-design complex industrial systems from top program objectives to system architecture and detailed designs trade-off studies.

Computer-as-Partner (AI)

To delegate tasks to artificial agents. We integrate Generative AI to enable users to naturally interact with data through textual or speech interaction to explore digital threads, retrive data, maintain connsistency of MBSE artefacts across multiple modelling layer, simulate change impacts.

Computer-as-Medium (CSCW)

To communicate with teammates. We design (a)symmetric collaboration environments that connect multidisciplinary teams to review or co-design architectures with the output devices that best suit their tasks and habits and study how interactive visual representations impact the collaboration and team mental model.

Our Focus

Core Research Axes

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Research Axis 1

Review of Model-Based System Architecture

Focusing on 3D User Interfaces for managing complex systems Digital Threads. We are exploring multimodal human-computer interaction techniques that enable engineers to trace and maintain MBSE data across multiple views and levels of abstraction.

  • CAD-MBSE seamless data visual integration
  • Interactive Digital Threads 3D navigation and exploration
  • Immersive visual analytics of digital threads with Generative AI assistance

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Research Axis 2

Design of Model-Based System Architecture

Co-architecting complex industrial product-process systems by transitioning from esoteric 2D diagrammatic representations to multi-user interactive 3D modelling interfaces in a virtual world using desktop PC, wall-sized display or VR HMD on demand.

  • From 3D animated graphical CONOPS to physical system and factory architecture designs
  • Global complex industrial product-process systems architecture co-design and trade-off MBSE method
  • Empirical quality assessment (usability, utility, user satisfaction, cognition...) of MBSE visual notations

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Research Axis 3

Asymmetric Collaboration for Architecting Systems

Enabling multidisciplinary systems engineering team members to work together seamlessly using the devices that best suit to their role, task, and preferences while supporting the group awareness and team mental model .

  • Asymetric interactions for computer-mediated (a)synchronous collaboration
  • Generative conversational AI for reconstructing design decision, argumentation and justification traces
  • Group interaction analyses to study computer-mediated collaboration and cognitive synchronisation

Latest Updates

News & Events

Who We Are

Research Team & Committees

Research Team

  • Romain Pinquié
    Scientific Coordinator & Associate Prof. (Univ Grenoble Alpes, CNRS, Grenoble INP, G-SCOP)
    romain.pinquie@grenoble-inp.fr
  • Frédéric Noel
    Full Professor (G-SCOP UMR 5272)
  • Gilles Foucault
    Associate Professor (G-SCOP UMR 5272)
  • Camilo Medina & Ghislain Mugisha
    PhD Candidates (G-SCOP UMR 5272)
  • Haobo Wang
    Post-doctoral research fellow (G-SCOP UMR 5272)
  • Baptiste Paterne & Joshuel Kuilwijk
    XR Software Engineers (G-SCOP UMR 5272)

Committees

Executive Committee
  • Romain Pinquié & Frédéric Noel (G-SCOP UMR 5272)
  • Hugo Falgarone (CEO SkyReal) & Benjamin Ray (CTO SkyReal)
Steering Committee
  • Executive committee members
  • Peggy Zwolinski (Head of G-SCOP UMR CNRS)
  • Valérie Perrier (VP Scientific Board Univ Grenoble Alpes)
  • Ahmed Lbath (Head of Carnot LSI)
Scientific and Industrial Committee
  • Executive committee members
  • Nicolas Chevassus (Head of Ariane 6 Digital Transformation, Ariane Group)
  • Frédéric Mérienne (Full Prof., XR expert, Arts et Métiers ParisTech)
  • Françoise Darses (Full Prof., Cognitive ergonomics expert, Institute for Biomedical Research of Defense)