We encourage submissions that align with the tracks. However, the topics of the tracks are not considered comprehensive: we accept high-quality papers on all aspects of M&S. Submit papers via email: iccmsa2025@126.com.

Regular Tracks

1. Simulation algorithms and infrastructure
Track Co-Chairs:

Alessandro Pellegrini (Tor Vergata University of Rome, Italy)
Philipp Andelfinger (NTU, Singapore)

    Synchronization, scheduling, memory management, load balancing, runtime environments, fault tolerance
    Simulation on emerging hardware platforms
    Data-driven simulation systems: Digital Twins, DDDAS, symbiotic simulation
    Simulation engines and techniques for differentiable simulation and reinforcement learning
    Performance evaluation of simulation systems

2. Modeling Methodology
Track Co-Chairs:

Hans Vandheluwe (University of Antwerp, Belgium)
Pia Wilsdorf (University of Rostock, Germany)

    Modeling formalisms and domain-specific languages
    Model-based approaches for reuse, composition
    Experiment design, V&V, model calibration, uncertainty quantification, simulation-based inference
    Conceptual modeling
    Automatic model generation, data-driven model creation, and model adaptations
    Model ensembles, explorative modeling

3. Applications of advanced discrete simulation methods
Track Co-Chairs:

Niki Popper (TU Wien, Austria)
Fabian Lorig (Malmö University, Sweden)

Case studies considering the application of new or advanced computational methods to applications of contemporary interest, such as:

    Cell biology
    Cyber-physical systems
    Emergency response and crisis preparedness
    Epidemics and healthcare
    Manufacturing and logistics
    Social networks and human behavior
    Transportation and urban planning
    Wireless, mobile, ad-hoc, and sensor networks

Special Tracks:

1. Model reduction

Track Chair:
Jerome Feret (DI-NES, Paris, France)

Due to the inherent complexity of models found in biology and other application areas, their simulation can often only be achieved with model reduction. Model reduction is an interdisciplinary area where ideas from mathematics, physics, and computer sciences are combined to discover the most important quantities to drive the dynamics of the modeled systems. This session will cover:

    Exact, numerically approximate, or interval-based model reduction approaches
    Both differential and stochastic approaches
    Approaches based on low-level descriptions (such as ODEs and CTMCs) or higher lever descriptions (such as rule-based models) of models.

2.Cutting-edge approaches in Computational Economics

Track Co-Chairs:
Andrea Vandin (Scuola Superiore Sant’Anna, Pisa, Italy)
Marco Pangallo (CentAI, Turin, Italy)

We encourage contributions highlighting novel approaches in computational economics, including, but not limited to:

    Reasoning agents: Advanced reasoning capabilities in agent-based models Learning agents: Multi-agent reinforcement learning
    Discrete vs continuous simulations: does it matter?
    Can we trust agents? Methodologies to explain and automate agent-based analyses
    Methodologies to automate the calibration of agent models
    Supporting modelers: advanced tool support for agent-based models
    Agents mining: inference and mining of agent-based models

The target audience is interdisciplinary: on the one hand, we seek contributions from social scientists interested in computational and data-driven approaches; on the other hand, we solicit contributions from STEM scientists and engineers interested in applications from the social sciences.

3. Visualization for communicating and understanding simulation studies and results

Track Chair:
Stefan Bruckner (University of Rostock, Germany)

This track encourages submissions that focus on visualization and visual analytics methods and tools for enhancing the communication and interpretation of simulation results. Topics of interest include, but are not limited to:

    Visualization for Decision Support: Design and development of visual tools that enhance decision-making processes by effectively conveying simulation outcomes
    Comparative Visualization: Techniques for comparing multiple simulations or datasets visually to identify patterns, differences, and trends
    Uncertainty Visualization: Visual methods for representing and communicating uncertainties inherent in simulation studies
    Scalability in Visualization: Approaches for handling large-scale simulation data in a visually effective manner
    Storytelling and Narrative Visualization: Methods and approaches for creating dynamic narratives around simulation studies
    Immersive Analytics: Solutions that allow users to interact with and explore simulation data in immersive environments
    User-Centered Design: Creating visualization tools tailored to the needs of diverse stakeholders, from modelers to decision-makers
    Automated Visualization Pipelines: Methodologies for automating the generation of insightful visualizations from simulation results
    Machine Learning Integration: Visualization methods that incorporate machine learning to automatically generate, optimize, or refine visual representations of simulation data

This track is designed to attract an interdisciplinary audience, including visualization researchers and practitioners who are keen on developing innovative techniques for interpreting and communicating complex data, as well as members of the simulation community interested in leveraging advanced visualization methods to enhance the analysis and presentation of their simulation studies. The goal is to foster collaboration and knowledge exchange between these communities to drive forward both fields.

4. Digital Twins

Track Co-Chairs:
Dong (Kevin) Jin (University of Arkansas, USA)
Srikanth Yoginath (Oak Ridge National Laboratory, USA)

This mini-track explores innovative applications and advancements of digital twin technology in Modeling and Simulation. Topics include but are not limited to:

    Optimization of Simulation for Digital Twins: Optimization techniques to ensure simulations meet the operational performance requirements of digital twins
    Digital Twins Multi-Scale Issues: Techniques to address challenges in integrating simulations of multiple scales within digital twins
    Digital Twins Validation and Verification: Techniques for validating and verifying the accuracy and reliability of digital twin simulations
    Digital Twins of Complex Systems: Modeling and simulation techniques for creating digital twins of complex cyber-physical systems in various sectors (energy, manufacturing, transportation, smart building, etc.)
    Digital Twins Energy Aspects: Modeling and simulation techniques to study and optimize energy use and sustainability within digital twins
    Digital Twins Real-Time Constraints: Real-time challenges and solutions using simulation and optimization strategies to balance the need for high-fidelity models with real-time constraints in digital twins.