ANNSIM 2021 : Annual Modeling and Simulation Conference

AI and Simulation (AIS)
Joon-Seok Kim and Andreas Züfle

Description:  Modeling and simulation (M&S) have advanced our understanding of complex systems and our controls over them in different areas. In the enterprise of exploring such complex systems, artificial intelligence (AI) and machine learning (ML) are a transformative technology shaping our knowledge and facilitating navigation in unconquered, unexplored, unknown frontiers. To push the boundaries of human understanding, marrying M&S with AI and ML is ineluctable and visionary. Leveraging the best of our knowledge of AI and ML allows us to accelerate, enhance, and delineate M&S.

This AI and Simulation (AIS) track is a dedicated forum to exchange our views, ideas, research methods, and applications to resolve the scientific question of what and how cutting-edge AI and ML methods can synergize M&S and vice versa. This track explorers M&S practices to which AI and ML methods are applied such as knowledge reasoning, computer vision, natural language processing, deep learning, and reinforcement learning.

In this track, we are seeking full papers (up to 12 pages) on original contributions across all modes of M&S leveraging AI and ML and interdisciplinary contributions that advance the state of the arts in AI and ML. This track will use the traditional format of having oral paper presentations. Topics of interest include, but not limited to:

• Advancing validation and verification (V&V) using AI and ML
• AI and ML methods for training and evolving agents
• Best practices of convergence of AI and Simulation
• Empirical comparisons of state-of-the-art AI and ML methods in M&S
• Facilitating experimentation using AI and ML
• M&S to improve AI and ML solutions
• Simulation modeling tools and methods based on AI and ML
• Simulation optimization using AI and ML
• Visionary methodology of AI and ML in simulation

Annual Simulation Symposium (ANSS)
José Luis Risco Martín and Erika F. Frydenlund

Description:  Modeling and simulation (M&S) is currently the core of many industrial processes. It is a vital ingredient, since it can support early evaluation and optimization of designs, as well as ongoing verification while changes occur to make sure that the right product is developed with the required quality. However, there are still businesses that must understand that embracing M&S in project development and management is good practice, and this can be done showing real-world examples of success.

Real-world applications have been always the driving force for the development of Modeling and Simulation (M&S) theories. For over 50 years, the Annual Simulation Symposium has been a forum to exchange ideas, results, and methods related to real-world theories and applications of M&S for simulationists in industry, government, and academia.

The purpose of this track is to highlight and advance rigorous experimental, computational practices of M&S devoted to the study of real-world problems. Research on all topics concerning the practice of M&S theories are welcome. Authors are invited to present research of all kinds, including case studies and applications. Recommended topics in the track include, but are not limited to, the following with application to real-world problems:

• Advances in the field of M&S for implementation purposes
• Application of modeling formalisms into real world applications
• Rigorous comparisons across M&S techniques
• New applications of M&S
• Novel uses of M&S in real world applications
• Application of M&S to co-design, hardware-in-the-loop, co-simulation
• M&S tools: performance analysis, scalability

Communications and Networking Simulation (CNS)
Abdolreza Abhari and Ala’a Al-Habashna

Description:  The Communications and Network (CNS) Track discusses Communications and Networks as they are the nerve system of Information Technology and a dominating part of our daily lives. Over the years, topics related to communication and network research have expanded from traditional compute network systems to emerging systems such as Internet of Things and to information and network-based systems in broader contexts. Quality of Service, Security, Interoperability, speedy access, and many other important requirements need to be studied together with these network-based systems to be able to measure and monitor them by methodologies such as simulation, a proven technique that can save costs, time, and lives. All papers describing design principles, network simulation, and applications pertinent (but not limited) to the following topics are welcome:

Broader Communication and Network-based Systems

• Social networks modeling and simulation
• Data-driven techniques for modeling and simulation
• Web-based simulation and applications
• Peer-to-Peer communications and computations
• Neural networks, deep learning and AI related networks
• Biological computing and software-defined networking
• Healthcare system & infrastructure
• Traffic network modeling and simulation
• Large scale networks simulation
• High-Level architecture simulation and applications
• Network modeling techniques, verification, and validation

Cloud Computing and Distributed Systems

• Cloud Performance
• Cloud Interoperability
• Hybrid & Enterprise Cloud Architecture
• Simulation on the Cloud
• Edge Computing and Smart Cities
• Simulation tools for Distributed Computing

Internet of Things (IoT)

• IoT Network and Architecture
• IoT in Army and Manufacturing
• IoT Smart Homes, Smart Cities and Environment
• IoT Security, Energy and more
• IoT Enabling Technologies and Applications

Wireless Network & Mobile Network

• Cellular and Ad Hoc Network Simulation
• Wireless Communications and Networks
• Internet and Mobile  Application, Mobile QoS
• Signaling and Routing Protocols
• Smart Phone Application

Cyber Physical Systems (CPS)
Umut Durak and Claudio Gomes

Description:  The rate at which Cyber-Physical Systems (CPS) become complex is outpacing our ability to comprehend them. Evidence of this can be seen in the accidents of recent years. This is not only due to the fact that CPSs are complex. Their development process is also complex. While systems comprised of many interacting, heterogeneous, components, are fundamental to our society, the complexity in their development process should also be managed. For instance:

– Intellectual property protection poses challenges in producing holistic simulation of systems comprised of third party components; and
– Distributed development demands innovative collaborative modelling techniques.
Modeling and Simulation (M&S) techniques have proven to mitigate these issues, but they face new challenges as more is being demanded from these techniques. Some example questions include:
– Can the models be used for monitoring the system across whole of its lifecycle, as in Digital Twins?
– How much can be simulations be relied upon, as in Simulation Governance?

This track focuses on new developments in Modelling and Simulation (M&S) that enable us to manage the complexity of CPS and their development, and addressing the above questions.

Partial list of topics of interest:

• Applied Theory of Modeling and (Co-)Simulation of CPS
• Advanced and Visual Analytics for (Co-)Simulation of CPS
• (Co-)Simulation in Advanced Manufacturing and Industry 4.0
• (Co-)Simulation Verification and Validation
• Modeling and (Co-)Simulation as a Service
• Correct and IP Safe (Co-)Simulation Cyber-Physical Systems
• (Co-)Simulation Based Verification of Cyber-Physical Systems
• (Co-)Simulation Based Optimization of Cyber-Physical Systems

Emerging Topic – Aspects of Pandemic Modeling (ET-APM)
Andreas Tolk and Jiangzhuo Chen

Description:  In early 2020, the SARS-CoV-2 virus took the world by surprise and resulted in the COVID-19 pandemic. The virus not only caused significant loss of life, but it also endangered worldwide the sustainability of health care systems. Non-pharmaceutical interventions (NPIs) were designed to counter these dangers, but they sometimes resulted in unintended consequences with possible negative long-term consequences, such as pausing of preventative medical care or delaying non-essential procedures. There were also social as well as economic consequences that affected countless families. This year’s track on emerging topics focuses on all these aspects of pandemic modeling, including intended and unintended effects of the NPIs, helping to understand the COVID-19 challenge as a multi-value, multi-criteria decision problem that requires interdisciplinary contributions to allow for better strategic planning now and in the future. Topics of interest to this track include, but are not limited to:

• Compartmental SEIR models for epidemic research
• Agent-based approaches for epidemic research
• Artificial Intelligence/Machine Learning support
• Hybrid Modeling and Simulation
• Calibrating models with real-world data
• Using real-world-data to develop models
• Artificial societies and social simulation for NPI evaluation
• Visualization of multi-value, multi-criteria options for decision support
• Modeling and simulation-based Operations Research methods

High Performance Computing and Simulation (HPC)
Philip Wilsey and Sounak Gupta

Description:  Advances in novel and heterogeneous architectures, high-end computers, large data stores are ushering in a new era of high performance parallel and distributed simulations. Along with these new capabilities come new challenges in computing and system modeling. The goal of HPC 2021 is to encourage innovation in high performance computing and communication technologies and to promote synergistic advances in modeling methodologies and simulation. It will promote the exchange of ideas and information between universities, industry, supercomputing centers, and national laboratories about new developments in system modeling, high performance computing and communication, scientific computing as well as simulation.

Topics of interest include but are not limited to:

• High performance computing for Big Data analytics
• GPU, accelerator and co-processor computing; multicore and many-core computing
• Exascale challenges
• Power and energy-aware computing
• Cloud, distributed, and grid computing
• High performance numerical methods and programming
• High performance system modeling and simulation
• Large scale visualization and data management
• Tools and environments for coupling parallel codes
• Reproducibility of application performance results

Humans, Societies, and Artificial Agents (HSAA)
Philippe J. Giabbanelli and Andrew Crooks

Description:  Artificial societies have typically relied on agent-based models, Geographical Information Systems (GIS), or cellular automata to capture the decision-making processes of individuals in relation to places and/or social interactions. This has supported a wide range of applications (e.g., in archaeology, economics, geography, psychology, political science, or health) and research tasks (e.g., what-if scenarios or predictive models, models to guide data collection). Several opportunities have recently emerged that augment the capacity of artificial societies at capturing complex human and social behavior. Mixed-methods and hybrid approaches now enable the use of ‘big data’, for instance by combining machine learning with artificial societies to explore the model’s output (i.e., artificial societies as input to machine learning), define the model structure (i.e. machine learning as a preliminary to designing artificial societies), or run a model efficiently (i.e. machine learning as a proxy or surrogate to artificial societies). Datasets are also broader in type since artificial societies can now be built from text, or generate textual as well as visual outputs to better engage end-users. Authors are encouraged to submit papers in the following areas:

• Applications of artificial societies (e.g., modeling group decisions and collective behaviors, emergence of social structures and norms, dynamics of social networks)
• Data collection for artificial societies (e.g., using simulations to identify data gaps, population simulations with multiple data sources, use of the Internet-of-Things)
• Design and implementation of artificial agents and societies (e.g., case studies, analyses of moral and ethical considerations)
• Participatory modeling and simulation
• Policy development and evaluation through simulations
• Predictive models of social behavior
• Simulations of societies as public educational tools
• Mixed-methods (e.g., analyzing or generating text data with artificial societies, combining machine learning and artificial societies)
• Models of individual decision-making, mobility patterns, or socio-environmental interactions
• Testbeds and environments to facilitate artificial society development
• Tools and methods (e.g., agent-based models, case-based modeling, soft systems)