Thematic Tracks

Thematic tracks organized by experts in a particular area constitute the core of the conference.
The list of accepted tracks is below, please click through for brief information and track web/contact addresses to follow to find full details.
We will be adding several more tracks in the coming weeks.

If you are interested in organizing a thematic track at ICCS 2022, you can find all necessary details on the Call for Tracks webpage.

  1. Computational Collective Intelligence – CCI
  2. Computational Optimization, Modelling and Simulation – COMS
  3. Machine Learning and Data Assimilation for Dynamical Systems – MLDADS
  4. Multiscale Modelling and Simulation – MMS
  5. Quantum Computing – QCW
  6. Simulations of Flow and Transport: Modeling, Algorithms and Computation – SOFTMAC
  7. Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning – SmartSys
  8. Teaching Computational Science – WTCS

Computational Collective Intelligence – CCI

Web Address: TBA
Contact: Marcin Maleszka, Wroclaw University of Science and Technology, email
Description: This special session during ICCS deals with the problem of Collective Intelligence, which is most often understood as an AI sub-field dealing with soft computing methods which enable making group decisions or processing knowledge among autonomous units acting in distributed environments. Web-based systems, social networks and multi-agent systems very often need these tools for working out consistent knowledge states, resolving conflicts and making decisions, which is an important part of modern and future computing.
The session is organized by the IEEE Computational Collective Intelligence technical committee, but welcomes all interested parties. The goal is to provide an internationally respected forum for scientific research in the computer-based methods of collective intelligence and their applications in (but not limited to) such fields as semantic web, social networks and multi-agent systems.
The topics of interest include, but are not limited to:
  • Group decision making
  • Consensus computing
  • Collective action coordination
  • Inconsistent knowledge processing
  • Ontology mapping and merging
  • Collaborative ontology
  • Ontology development in social networks
  • Semantic social networks
  • Semantic and knowledge grids
  • Semantic annotation of web data resources
  • Group web services (service description, discovery, composition)
  • Automatic metadata generation
  • Semantic web inference schemes
  • Reasoning in the semantic web
  • Knowledge portals
  • Knowledge discovery
  • Information retrieval
  • Advanced analysis for social networks dynamics
  • Social networks and semantic communication
  • Cooperative distributed problem solving
  • Multiagent planning
  • Negotiation protocols
  • Multiagent learning

Computational Optimization, Modelling and Simulation – COMS

Contact: Xin-She Yang, Middlesex University London, email
Description: The 13th workshop “Computational Optimization, Modelling and Simulation (COMS 2022)” will be a part of the International Conference on Computational Science (ICCS 2022). This will be the 13th event of the COMS workshop series with the first held during ICCS 2010 in Amsterdam, then within ICCS in Singapore, USA, Spain, Australia, Iceland, USA, Switzerland, China, Portugal, Netherlands and Poland. COMS 2022 intends to provide a forum and foster discussion on the cross-disciplinary research and development in computational optimization, computer modelling and simulation. Accepted papers will be published in Springer’s LNCS Series.
COMS 2022 will focus on new algorithms and methods, new trends, and latest developments in computational optimization, modelling and simulation as well as applications in science, engineering and industry.
Topics include (but not limited to):
  • Computational optimization, engineering optimization and design
  • Bio-inspired computing and algorithms
  • Metaheuristics (ant and bee algorithms, cuckoo search, firefly algorithm, genetic algorithms, PSO, simulated annealing etc)
  • Simulation-driven design and optimization of computationally expensive objectives
  • Surrogate- and knowledge-based optimization algorithms
  • Scheduling and network optimization
  • Integrated approach to optimization and simulation
  • Multiobjective optimization
  • New optimization algorithms, modelling techniques related to optimization
  • Design of experiments
  • Application case studies in engineering and industry

Machine Learning and Data Assimilation for Dynamical Systems – MLDADS

Contact: Rossella Arcucci, Imperial College London, email
Description: The object of the theory of dynamical systems addresses the qualitative behaviour of dynamical systems as understood from models. Moreover, models are often not perfect and can be improved using data using tools from the field of Data Assimilation. Additionally, the field of Machine Learning is concerned with algorithms designed to accomplish certain tasks whose performance improve with the input of more data. The intersection of the fields of dynamical systems, data assimilation and machine learning is largely unexplored. The goal of this workshop is to bring together researchers from these fields to fill the gap between these theories.
The intersection of the fields data assimilation, machine learning and dynamical systems is largely unexplored, and the goal of the MLDADS workshop is to bring together contributions from these fields to fill the gap between these theories in the following directions:
1) Machine Learning for Data Assimilation: how to assist or replace the traditional methods in making forecasts, without the unrealistic assumption (particularly linearity, normality and zero error covariance) of the conventional methods.
2) Machine Learning for Dynamical Systems: how to analyze dynamical systems on the basis of observed data rather than attempt to study them analytically.
3) Data Assimilation for Machine Learning and/or Dynamical Systems: how well does the model under consideration (Machine Learning model and/or Dynamical System) represent the physical phenomena.
4) Data Assimilation and/or Machine Learning for Dynamical Systems: how can tools from the interaction between the theories of Data Assimilation and Machine Learning be used to improve the accuracy of the prediction of dynamical systems.

Multiscale Modelling and Simulation – MMS

Contact: Derek Groen, Brunel University London, email
Description: Modelling and simulation of multiscale systems constitutes a grand challenge in computational science, and is widely applied in fields ranging from the physical sciences and engineering to the life science and the socio-economic domain. Most of the real-life systems encompass interactions within and between a wide range of space and time scales, and/or on many separate levels of organization. They require the development of sophisticated models and computational techniques to accurately simulate the diversity and complexity of multiscale problems, and to effectively capture the wide range of relevant phenomena within these simulations. Additionally, these multiscale models frequently need large scale computing capabilities as well as dedicated software and services that enable the exploitation of existing and evolving computational ecosystems.
This MMS workshop aims to provide a forum for multiscale application modellers, framework developers and experts from the distributed infrastructure communities to identify and discuss challenges in, and possible solutions for, modelling and simulating multiscale systems, as well as their execution on advanced computational resources and their validation against experimental data.

Quantum Computing – QCW

Contact: Katarzyna Rycerz, AGH University of Science and Technology, email
Description: Quantum computing is a new paradigm that exploits the fundamental principles of quantum mechanics to solve problems in various fields of science that are beyond the possibilities of classical computing infrastructures. Despite the increasing activity in both theoretical research and hardware implementations, reaching the state of useful quantum supremacy is still an open question. This workshop aims to provide a forum for computational scientists, software developers, computer scientists, physicists and quantum hardware providers to understand and discuss research on current problems in quantum informatics.

Simulations of Flow and Transport: Modeling, Algorithms and Computation – SOFTMAC

Contact: Shuyu Sun, King Abdullah University of Science and Technology (KAUST), email
Description: Modeling of flow and transport is an essential component of many scientific and engineering applications, with increased interests in recent years. Application areas vary widely, and include groundwater contamination, carbon sequestration, air pollution, petroleum exploration and recovery, weather prediction, drug delivery, material design, chemical separation processes, biological processes, and many others. However, accurate mathematical and numerical simulation of flow and transport remains a challenging topic from many aspects of physical modeling, numerical analysis and scientific computation. Mathematical models are usually expressed via nonlinear systems of partial differential equations, with possibly rough and discontinuous coefficients, whose solutions are often singular and discontinuous. An important step of a numerical solution procedure is to apply advanced discretization methods (e.g. finite elements, finite volumes, and finite differences) to the governing equations. Local mass conservation and compatibility of numerical schemes are often necessary to obtain physical meaningful solutions. Another important solution step is the design of fast and accurate solvers for the large-scale linear and nonlinear algebraic equation systems that result from discretization. Solution techniques of interest include multiscale algorithms, mesh adaptation, parallel algorithms and implementation, efficient splitting or decomposition schemes, and others.
The international workshop on “Simulations of Flow and Transport: Modeling, Algorithms and Computation” (SOFTMAC) has been held 10 years since 2011 within the International Conference on Computational Science (ICCS). The aim of this symposium is to bring together researchers in the aforementioned field to highlight the current developments both in theory and methods, to exchange the latest research ideas, and to promote further collaborations in the community. We invite original research articles describing the recent advances in mathematical modeling, computer simulation, numerical analysis, and other computational aspects of flow and transport phenomena of flow and transport.
Potential topics include, but are not limited to:
  • advanced numerical methods for the simulation of subsurface and surface flow and transport, and associated aspects such as discretization, gridding, upscaling, multiscale algorithms, optimization, data assimilation, uncertainty assessment, and high performance parallel and grid computing;
  • spatial discretization schemes based on advanced finite element, finite volume, and finite different methods; schemes that preserve local mass conservation (such as mixed finite element methods and discontinuous Galerkin methods) are of particular interest;
  • decomposition methods for improved efficiency and accuracy in treating flow and transport problems; decomposition methods for nonlinear differential equations and dynamical systems arising in flow and transport; temporal discretization schemes for flow and transport;
  • a-priori and a-posteriori error estimates in discretizations and decompositions; numerical convergence study; adaptive algorithms and implementation;
  • modeling and simulation of single-phase and multi-phase flow in porous media or in free space, and its applications to earth sciences and engineering;
  • modeling and simulation of subsurface and surface transport and geochemistry, and its application to environmental sciences and engineering;
  • computational thermodynamics of fluids, especially hydrocarbon and other oil reservoir fluids, and its interaction with flow and transport;
  • computational modeling of flow and transport in other fields, such as geological flow/transport in crust and mantle, material flow in supply chain networks, separation processes in chemical engineering, information flow, biotransport, and intracellular protein trafficking, will also be considered.

Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning – SmartSys

Contact: Pedro Jorge Sequeira Cardoso, University of Algarve & LARSyS, email
Description: Smart Systems incorporate sensing, actuation, and intelligent control in order to analyze, describe or resolve situations, making decisions based on the available data in a predictive or adaptive manner. Appropriated for computer scientists, mathematicians, as well as researchers from many application areas, pioneering computational methods in distinct research fields, such as space, physics, chemistry, life sciences, economics, security, engineering, arts, humanitarian etc., SmartSys’22 thematic track brings together computer vision, sensor networks, machine learning algorithms, applications etc. to solve computational science problems. But other related areas are also welcome, such as augmented reality, human computer interaction, user experience, Internet of Things, Internet of everything, energy management systems, smart grids, vehicle or person tracking and management systems, operational research, evolutionary computation, and information systems in general, always with the focus in smart systems as tools to solve daily computational science based problems.

Teaching Computational Science – WTCS

Contact: Angela B. Shiflet , Wofford College, email
Description: The Workshop on Teaching Computational Science (WTCS 2022) solicits submissions that describe innovations in teaching computational science in its various aspects (e.g. modeling and simulation, high-performance and large-data environments) at all levels and in all contexts. Typical topics include, but are not restricted to, innovations in the following areas: course content, curriculum structure, methods of instruction, methods of assessment, tools to aid in teaching or learning, evaluations of alternative approaches, and non-academic training in computational sciences. These innovations may be in the context of formal courses or self- directed learning. They may involve, for example, introductory, service, or more advanced courses; specialist undergraduate or postgraduate topics; professional development; or industry-related short courses. We welcome submissions directed at issues of current and local importance, as well as topics of international interest. Such topics may include transition from school to university, articulation between vocational and university education, quality management in teaching, development of needed HPC and other computational research skills, teaching people from other cultures, attracting and retaining female students, diversification of the work force, and flexible learning.