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.
- 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 Health – CompHealth
- Computational Optimization, Modelling and Simulation – COMS
- Computer Graphics, Image Processing and Artificial Intelligence – CGIPAI
- Machine Learning and Data Assimilation for Dynamical Systems – MLDADS
- Multiscale Modelling and Simulation – MMS
- Quantum Computing – QCW
- Simulations of Flow and Transport: Modeling, Algorithms and Computation – SOFTMAC
- Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning – SmartSys
- Software Engineering for Computational Science – SE4Science
- Solving Problems with Uncertainty – SPU
- Teaching Computational Science – WTCS
- Uncertainty Quantification for Computational Models – UNEQUIvOCAL
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 Health – CompHealth
Computational Optimization, Modelling and Simulation – COMS
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
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
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
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
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 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
Software Engineering for Computational Science – SE4Science
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
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
Uncertainty Quantification for Computational Models – UNEQUIvOCAL
- 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.