Thematic tracks organized by experts in particular areas constitute a fundamental part of the conference.
The list of accepted tracks is below. Please click through for a each track’s scope, dedicated web address, and track chair contact details.
We will be adding several more tracks in the coming weeks.
If you are interested in organizing a thematic track at ICCS 2023, you can find all necessary details on the Call for Tracks webpage.
- Advances in High-Performance Computational Earth Sciences: Applications and Frameworks – IHPCES
- Artificial Intelligence and High-Performance Computing for Advanced Simulations – AIHPC4AS
- Biomedical and Bioinformatics Challenges for Computer Science – BBC
- Computational Collective Intelligence – CCI
- Computational Diplomacy and Policy – CoDiP
- Computational Health – CompHealth
- Computational Modelling of Cellular Mechanics – CMCM
- Computational Optimization, Modelling and Simulation – COMS
- Computational Social Complexity – CSCx
- Computational Steering – CSTR
- Computer Graphics, Image Processing and Artificial Intelligence – CGIPAI
- Machine Learning and Data Assimilation for Dynamical Systems – MLDADS
- MeshFree Methods and Radial Basis Functions in Computational Sciences – MESHFREE
- Multiscale Modelling and Simulation – MMS
- Network Models and Analysis: From Foundations to Complex Systems – NMA
- Quantum Computing – QCW
- Simulations of Flow and Transport: Modeling, Algorithms and Computation – SOFTMAC
- Smart Systems: Bringing Together Computer Vision, Sensor Networks and Artificial Intelligence – SmartSys
- Solving Problems with Uncertainties – SPU
- Teaching Computational Science – WTCS
Advances in High-Performance Computational Earth Sciences: Applications and Frameworks – IHPCES
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
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
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
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 Diplomacy and Policy – CoDiP
In recent years computational techniques (e.g., modelling and simulation, data science and artificial intelligence) have become a mainstay of the political process. There are also a growing number of international examples of academic-governmental collaborations where solutions are co-created by decisions makers and scientists. However, while the role of computational science is increasing, the academic fields of computational policy/diplomacy are still in their infancy. The CoDiP workshop aims to bring together computational scientists who are applying modelling and computational analysis to help support diplomatic and political decisions. The aim is to build a community around this area and to share experience and knowledge about the fundamental scientific challenges.
These relevant topics include (but not limited to):
- Applications of models in Diplomacy/Policy
- Methods for Knowledge Elicitation (Group Model Building)
- Complex Systems analysis of Policy/Diplomacy
- Uncertainty Quantification/Sensitivity Analysis in decisions
- Machine Learning/AI for policy/diplomacy (including ethics)
Computational Health – CompHealth
Computational Modelling of Cellular Mechanics – CMCM
In order to answer the complex emerging challenges, the researchers need to explore, develop, validate, and apply novel computational models.
The central aim of this track is to bring together the prominent members of the field to discuss ideas, challenges, and state-of-the-art solutions to the key questions at the forefront of cellular mechanics research. These discussions will focus on numerical techniques and on the connection of these techniques to complementary experiments.
The main topics include (but not limited to):
- Cell growth
- Cytoskeletal network
- Rheology and red blood cells
- Platelet margination
- Cell migration
- Cell mechanosensing
- Viscoelastic behaviour of cells
- Cell extravasation
- Micromanipulation techniques (optical tweezer, atomic force microscopy, micropipettes, etc.)
Computational Optimization, Modelling and Simulation – COMS
COMS2023 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
Computational Social Complexity – CSCx
The aim of the workshop is to stimulate interdisciplinary research to develop complex systems-based approaches aimed at understanding social systems. In general, the workshop will focus on the following questions: What are the patterns in social systems that existing theories and data cannot explain? What kinds of observational and empirical data are needed to better inform the models? What new modeling techniques and methods need to be developed?
Broad topics in social complexity that we welcome papers for:
- Modeling for sustainable development goals (e.g., poverty, well-being, food security, water, energy) in rapidly growing urban complex societies. Focusing on the role of modeling in policy and urban planning.
- Emergent phenomena of complex social systems: cooperation, self-organization, regime shifts, tipping points, and resilience.
- Social Digital Twins and The Social Complexity of Digital Data (complex realism in social research, new metatheory), including novel (curated) datasets and empirical calibration and validation of computational models for understanding complex social systems.
- New modeling techniques in computational social science.
1. (Re)statement of principal findings
2. Strengths and weaknesses of the study
2.1 Strengths and weaknesses in relation to other studies, discussing particularly any differences in results
3. Meaning of the study: possible mechanisms and implications for policymakers/clinicians
4. Unanswered questions and future research
4.1 What kinds of observational and empirical data are needed to better inform the models?
4.1.1 What (finer level) data would be ideal to inform the mechanisms considered in the model, experimental or observational?
4.1.2 What (aggregated level) data would be ideal to identify new higher-level patterns that could emerge from the model?
4.2 What are the patterns in social systems that cannot be explained by the existing theories and data?
4.3 What new modeling techniques and methods need to be developed?
4.4 [Optional] Other
Computational Steering – CSTR
Emerging paradigms like computational steering will immensely impact monolithic application runs. The future of simulation will involve design through analysis workflows, AI-steered computing and human interaction through user-friendly software interfaces powered by modern tools like interactive HPC, Cloud & edge computing and Augmented reality.
The workshop aims to bring together researchers, computer scientists, practitioners and computational scientists developing algorithms, workflows, and tools. We invite research papers, demos, technology pitches and technology evangelists to this forum to discuss current and future paradigms of computational steering to advance science, research, and education.
Computer Graphics, Image Processing and Artificial Intelligence – CGIPAI
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
This is the fifth edition of the MLDADS workshop which goal is bring together contributions from the ML, DA and DS 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 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.
MeshFree Methods and Radial Basis Functions in Computational Sciences – MESHFREE
Standard computational methods used across many application fields require tessellation in 2D or 3D using triangular or tetrahedral meshes. Tessellation itself is computationally expensive especially in higher dimensions and the result of that computation is again discrete, and physical phenomena are not smoothly interpolated.
The meshfree methods are especially convenient for scattered data processing as they do not require tessellation. They are used not only for interpolation and approximation, and also for a solution of partial and ordinary differential equations, GIS systems etc.
Meshfree methods are scalable to higher dimensions and offer smooth final representation and they lead to a solution of a system of linear equations.
This ICCS 2023 thematic track is intended to explore broad computational applicability of the Meshfree methods especially based on Radial Basis Functions across many areas, including theoretical and mathematical aspects of the Meshfree methods.
The aim is to connect latest theoretical research results with possible computational applications, i.e. put together theory and applications in computational sciences.
Main topics (but not limited to):
- Meshfree methods in engineering problems
- Meshfree methods and differential equations
- Meshfree methods and GIS, CAD/CAM systems
- Meshfree methods in theory and practice
- Meshfree methods and computational and numerical issues
- Meshfree interpolation and approximation methods for large scalar and vector data sets
- Meshfree methods for scattered spatio-temporal data, t-varying systems etc.
- Radial Basis Functions (RBF) in computer graphics, visualization etc.
- Meshfree methods in image processing and computer vision
- Meshfree methods and projective space representation
- Comparison of meshfree and mesh based computational methods
- Scattered data interpolation and approximation methods
- RBF for a mesh morphing and data mapping
- Meshfree methods for corrupted image reconstruction and inpainting removal
- Meshfree methods applications in general
Multiscale Modelling and Simulation – MMS
This MMS track 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.
Network Models and Analysis: From Foundations to Complex Systems – NMA
Currently, in bioinformatics and systems biology, there is a growing interest in analysing associations among biological molecules at a network level. Since the study of associations in a system-level scale has shown great potential, the use of networks has become the de-facto standard for representing such associations, and its application fields span from molecular biology to brain connectome analysis. Molecules of different types, e.g., genes, proteins, ribonucleic acids, and metabolites, have fundamental roles in the mechanisms of the cellular processes. The study of their structure and interactions is crucial for different reasons, comprising the development of new drugs and the discovery of disease pathways. Thus, the modelling of the complete set of interactions and associations among biological molecules as a graph is convenient for a variety of reasons. Networks provide a simple and intuitive representation of heterogeneous and complex biological processes. Moreover, they facilitate modelling and understanding of complicated molecular mechanisms combining graph theory, machine learning and deep learning techniques. In general, complex biological systems represented as networks, provide an integrated way to look into the dynamic behaviour of the cellular system through the interactions of components. In computational biology, homogeneous networks have been used to model interactions among single type of biological macromolecules inside cells, such as protein-protein interactions (PPI), or gene-gene interactions. Also, heterogeneous networks are used to model the interplay of molecules of different types, e.g., genes, proteins and ribonucleic acids) that represent constitutive blocks of mechanisms inside cells, by using nodes and edge of different types, (i.e. implemented as node/ edge-coloured graphs). Special cases of heterogeneous networks are multilayer networks. The multilayer network model is widely used as a powerful tool to represent the organization and relationships of complex data in many domains. Multi- layer networks, which initially gained momentum in social computing, are designed to provide a more realistic representation of the different and heterogeneous relations that may characterize an entity in the network system. Thus, networks and network analysis methods are a keystone in computational biology and bioinformatics and are increasingly used to study biological and clinical data in an integrated way.
Quantum Computing – QCW
Simulations of Flow and Transport: Modeling, Algorithms and Computation – SOFTMAC
The international workshop on “Simulations of Flow and Transport: Modeling, Algorithms and Computation” (SOFTMAC) has been held 11 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 Artificial Intelligence – SmartSys
Solving Problems with Uncertainties – SPU
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.