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. Advances in High-Performance Computational Earth Sciences: Applications and Frameworks – IHPCES
  2. Artificial Intelligence and High-Performance Computing for Advanced Simulations – AIHPC4AS
  3. Biomedical and Bioinformatics Challenges for Computer Science – BBC
  4. Computational Collective Intelligence – CCI
  5. Computational Health – CompHealth
  6. Computational Optimization, Modelling and Simulation – COMS
  7. Computer Graphics, Image Processing and Artificial Intelligence – CGIPAI
  8. Machine Learning and Data Assimilation for Dynamical Systems – MLDADS
  9. Multiscale Modelling and Simulation – MMS
  10. Quantum Computing – QCW
  11. Simulations of Flow and Transport: Modeling, Algorithms and Computation – SOFTMAC
  12. Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning – SmartSys
  13. Software Engineering for Computational Science – SE4Science
  14. Solving Problems with Uncertainty – SPU
  15. Teaching Computational Science – WTCS
  16. Uncertainty Quantification for Computational Models – UNEQUIvOCAL

Advances in High-Performance Computational Earth Sciences: Applications and Frameworks – IHPCES

Contact: Takashi Shimokawabe, University of Tokyo, Japan, email
Description: The IHPCES workshop provides a forum for presentation and discussion of state-of-the-art research in high performance computational earth sciences. The emphasis of the eleventh workshop continues to be on advanced numerical algorithms, large-scale simulations, architecture-aware and power-aware applications, computational environments and infrastructure, and data analytics methodologies in geosciences. With the imminent arrival of the exascale era, strong multidisciplinary collaborations between these diverse scientific groups are critical for the successful development of earth sciences HPC applications. The workshop facilitates communication between earth scientists, applied mathematicians, computational and computer scientists and presents a unique opportunity to exchange advanced knowledge, computational methods and science discoveries. Work focusing emerging data and computational technologies that benefit the broader geoscience community is especially welcome.
Topics of interest include, but not limited to:
  • Large-scale simulations on both homogeneous and heterogeneous supercomputing systems in earth sciences, such as atmospheric science, ocean science, solid earth science, and space & planetary science, as well as multi-physics simulations.
  • Advanced modeling and simulations on natural disaster prevention and mitigation.
  • Advanced numerical methods such as FEM, FDM, FVM, BEM/BIEM, Mesh-Free method, and Particle method etc.
  • Parallel and distributed algorithms and programming strategies focused on issues such as performance, scalability, portability, data locality, power efficiency and reliability.
  • Software engineering and code optimizations for parallel systems with multi-core processors, GPU accelerators or Xeon Phi processors.
  • Algorithms for Big Data analytics and applications for large-scale data processing such as mesh generation, I/O, workflow, visualization and end-to-end approaches.
  • Methodologies and tools designed for extreme-scale computing with emphasis on integration, interoperability and hardware-software co-design.

Artificial Intelligence and High-Performance Computing for Advanced Simulations – AIHPC4AS

Contact: Maciej Paszynski, AGH University, Poland, email
Description: This workshop aims to integrate knowledge in computer science, computational science, and mathematics.
We invite papers oriented toward the applications of artificial intelligence (AI) and high-performance computing (HPC) in simulations, either in continuous simulations (e.g., finite element simulations of stationary problems using AI adaptive algorithms, HPC isogeometric analysis simulations of time-dependent problems, or application of deep learning for stabilization of space-time finite element simulations), as well as in discrete event simulations of complex-systems consisting of interacting individuals (e.g., HPC multi-agent simulations of the disease spread, or AI matching cellular automata parameters for the simulations of tumor growth).
We invite papers oriented toward the applications of AI and HPC in advanced simulations of phenomena often governed by either of the following:
Partial Differential Equations (PDEs): linear, non-linear, stationary, and time-dependent.
Complex systems consisting of very large collections of interacting individual elements. These systems may include molecules in a material, cells in the human body, interacting species in an ecosystem, and individuals transmitting an infectious disease within a group.
Likewise, we also encourage papers focused on applications and analysis of such advanced simulation methods, including the development of advanced inversion methods. The topics of this workshop include, but are not limited, to the following:
  • artificial intelligence including soft computing for simulation and inversion of PDEs or and complex systems
  • efficient adaptive algorithms for large problems
  • low computational cost adaptive solvers
  • artificial intelligence in Isogeometric Analysis and Petrov-Galerkin methods
  • model reduction techniques for large problems
  • memetic algorithm
  • multi-agent systems
  • supermodeling techniques
  • advanced parallelization techniques
  • high-performance computing
  • computational and mathematical analysis of advanced simulation methods
  • advanced methods applied to inverse problems
  • applications of advanced simulation methods

Biomedical and Bioinformatics Challenges for Computer Science – BBC

Contact: Mario Cannataro, University “Magna Graecia” of Catanzaro, Italy, email
Description: Emerging technologies in biomedicine and bioinformatics are generating an increasing amount of complex data. To tackle the growing complexity associated with emerging and future life science challenges, bioinformatics and computational biology researchers need to explore, develop and apply novel computational concepts, methods, and tools.
The aim of this workshop is to bring together computer science and life scientists to discuss emerging and future directions in topics related to key bioinformatics and computational biology techniques: (i) Advanced computing architectures; (ii) Algorithm design; (iii) Data analysis and knowledge discovery; (iv) Data management and integration; (v) Integration of quantitative/symbolic knowledge into executable biomedical “theories” or models. A special session will be devoted to bioinformatics and computer science methods to fight the COVID-19 pandemics.

Computational Collective Intelligence – CCI

Contact: Marcin Maleszka, Wroclaw University of Science and Technology, Poland, 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 Health – CompHealth

Contact: Sergey Kovalchuk, ITMO University, Russian Federation, email
Description: The field of computational science application in healthcare and medicine (H&M) is rapidly growing. Modeling and simulation, data and process mining, numerical methods, intelligent technologies provide new insights, support decision making, policy elaboration, etc. Moreover, this area gives quantitative support to emerging concepts in the area like P4-medicine (personalized, predictive, preventive, and participatory), value-based healthcare, and others. This workshop is aimed to bring together research in computational science and intelligent technologies applied in H&M in all the diversity of scales and aspects.

Computational Optimization, Modelling and Simulation – COMS

Contact: Xin-She Yang, Middlesex University London, United Kingdom, 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

Computer Graphics, Image Processing and Artificial Intelligence – CGIPAI

Web Address: TBA
Contact: Andres Iglesias, University of Cantabria, Spain, email
Description: Computer Graphics, Image Processing and Artificial Intelligence are three of the most popular, exciting and hot domains in Computational Sciences. The three areas share a broad range of applications in many different fields, and new impressive developments are arising every year. This workshop is aimed at providing a forum for discussion about new techniques, algorithms, methods, and technologies in any of those areas as well as their applications to science, engineering, industry, education, health, and entertainment. The interplay between any two of these areas is also of interest for this workshop. The workshop is part of the activities of the Horizon 2020 European project PDE-GIR, but it is open to researchers and practitioners working in these areas. We invite prospective authors to submit their contributions for fruitful interdisciplinary cooperation and exchange of new ideas and experiences, as well as to identify new issues and challenges, and to shape future directions, trends for research.
Potential topics include, but are not limited to:
  • Geometric and Solid Modeling & Processing, CAD/CAM/CAE, Curve/Surface Reconstruction
  • Computer Graphics Techniques, Algorithms, Software and Hardware
  • Computer Animation and Video Games
  • Virtual and Augmented Reality, Virtual Environments and Autonomous Agents
  • Computer Graphics Applications (Science, Engineering, Education, Health, Industry, Entertainment)
  • Image Processing techniques
  • Image Processing processes (e.g., image denoising, image deblurring, image segmentation, image reconstruction, depth estimation, 3D surface restoration)
  • Image Processing applications
  • Evolutionary and Nature-Inspired Algorithms (evolutionary programming, genetic algorithms)
  • Neural Networks, Machine Learning, Deep Learning and Data Mining
  • Swarm Intelligence and Swarm Robotics
  • Bio-informatics and bio-engineering
  • Natural Computing, Soft Computing and Evolutionary Computing
  • Artificial Intelligence Applications
  • Interplay among some of the previous areas.

Machine Learning and Data Assimilation for Dynamical Systems – MLDADS

Contact: Rossella Arcucci, Imperial College London, United Kingdom, 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, United Kingdom, 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, Poland, 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), Saudi Arabia, 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, Portugal, 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.

Software Engineering for Computational Science – SE4Science

Contact: Jeffrey Carver, University of Alabama, United States, email
Description: This is a time of great growth at the intersection of software engineering and computational science, increasingly manifested in the emerging discipline of Research Software. There is a need for members of these communities to share experiences, identify problems, and enumerate common goals to form the basis for an ongoing research agenda. The goal of this workshop is to provide a unique venue for the presentation of results and to facilitate interaction between software engineers and computational scientists, including those from the humanities and engineering. To address this goal, we seek contributions from members of those communities that describe perspectives, research outcomes, and lessons learned (positive or negative) from the development of computational science software.
Specifically, we are interested in the software development and software engineering challenges and enablers relating to the following topics. (1) Computational science software applications that solve complex software- or data-intensive research problems, from large parallel models/simulations of the physical world using HPC systems to smaller scale simulations developed by a single researcher on a desktop machine or a small cluster. (2) Applications that support scientific research and experiments at scale. Such applications include, but are not limited to, systems for managing and/or manipulating large amounts of data and systems that provide infrastructure for scientific or engineering applications such as libraries or HPC/Cloud software. (3) The process for building, reusing, and publishing software and data used in scientific experiments or engineering innovations. Among others, these processes include agile approaches, open source/open data issues, testing scientific software, and managing software or data repositories for publishing goals. (4) The process of theory-software translation, where loss or errors may occur due to challenges mapping between scientific theory and its representation in code, or between the outputs of computational research and its representation in theory.
This workshop will build upon previous SE4Science workshops. Similar to the format of the previous workshops, in addition to presentation and discussion of the accepted papers, we plan to devote significant time during the workshop to discussing important topics that arise from the paper presentations. The goal of these discussions is to (1) develop a joint research plan that can be conducted collectively by workshop participants and (2) development of ideas/draft of position statements to be published externally.

Solving Problems with Uncertainty – SPU

Contact: Vassil Alexandrov, Hartree Centre – STFC, United Kingdom, email
Description: Problems with uncertainty need to be tackled in an increasing variety of areas, from physics, chemistry, engineering, computational biology and environmental sciences to decision making in economics and social sciences. Uncertainty is unavoidable in almost all systems analysis, risk analysis, decision making and modelling and simulation. How uncertainty is handled and quantified shapes the integrity of the analysis, and the correctness and credibility of the solution and the results.
With the advent of exascale computing, big data analytics and scalable AI, larger and larger problems – often requiring hybrid approaches – have to be tackled in a systematic way at scale. The problem of solving such problems with uncertainties and quantifying the uncertainties becomes even more important due to the variety and scale of uncertainties in such problems.
The focus of the workshop will be on methods and algorithms for solving problems with uncertainties, stochastic methods and algorithms for solving problems with uncertainties, methods and algorithms for quantifying uncertainties such as dealing with data input and missing data, sensitivity analysis (local and global), dealing with model inadequacy, model validation and averaging, software fault-tolerance and resilience and more.

Teaching Computational Science – WTCS

Contact: Angela B. Shiflet , Wofford College, United States, 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.

Uncertainty Quantification for Computational Models – UNEQUIvOCAL

Contact: Wouter Edeling, Centrum Wiskunde & Informatica, Netherlands, email
Description: Given that uncertainty is unavoidable in almost all scientific fields, due to e.g. unknown parameters or simplifying modelling assumptions, uncertainty quantification is an indispensable part in state-of-the-art computational models. In order to build confidence in their results, it is therefore crucial that these models carry their own measure of uncertainty, especially when they are extrapolated beyond the domain in which they were originally calibrated. Also, the oncoming Exascale Computing resources will open up the possibility of solving problems with increased complexity and computational burden, exacerbating the importance (and demands) of reliable uncertainty quantification methods. This thematic track aims to attract research that focuses on new methods, which outperform existing techniques, as well as uncertainty quantification applications to complex problems. Topics of interest include:
  • Forward and inverse uncertainty quantification;
  • Model inadequancy;
  • Sensitivity analysis;
  • Dimension reduction;
  • Surrogate modelling (including machine-learning techniques and reduced-order modelling);
  • Case studies showing efficient uncertainty quantification methods;
  • Software for uncertainty quantification.