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 2021, you can find all necessary details on the Call for Tracks webpage.
- Artificial Intelligence and High-Performance Computing for Advanced Simulations – AIHPC4AS
- Classifier Learning from Difficult Data – CLD2
- Computational Methods for Emerging Problems in (dis-)Information Analysis – DisA
- Computational Optimization, Modelling and Simulation – COMS
- Computational Science in IoT and Smart Systems – IoTSS
- Machine Learning and Data Assimilation for Dynamical Systems – MLDADS
- MeshFree Methods and Radial Basis Functions in Computational Sciences – MESHFREE
- Simulations of Flow and Transport: Modeling, Algorithms and Computation – SOFTMAC
- Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning – SmartSys
- Teaching Computational Science – WTCS
Artificial Intelligence and High-Performance Computing for Advanced Simulations – AIHPC4AS
Classifier Learning from Difficult Data – CLD2
The main aim of this workshop is to discuss the problems of data difficulties, to identify new issues, and to shape future directions for research.
Topics include (but not limited to):
- learning from imbalanced data
- learning from data streams, including concept drift management
- learning with limited ground truth access
- learning from high dimensional data
- learning with a high number of classes
- learning from massive data, including instance and prototype selection
- learning on the basis of limited data sets, including one-shot learning
- learning from incomplete data
- case studies and real-world applications
Computational Methods for Emerging Problems in (dis-)Information Analysis – DisA
However information spreading can be also used for disinformation. The problem of the fake news publication is not new and it already has been reported in ancient ages, but it has started having a huge impact especially on social media users or people watching media news (Internet, newspapers, tv etc.). Such false information should be detected as soon as possible to avoid its negative influence on the readers and in some cases on their decisions.
Another problem and emerging challenge is coming from using automated information analysis and decision support systems (based on Artificial Intelligence (AI) and Machine Learning (ML) advances) as black-box single truth providers. If those information analysis systems are misused, attacked or fooled, their results will also lead to (dis-) information.
The main aim of this workshop is to bring together researchers and scientists computational science who are pioneering (dis-)information analysis methods to discuss problems and solutions in this area, to identify new issues, and to shape future directions for research. Moreover, we invite prospective researchers to send papers concerning (dis-)information detection methods and architectures, explainability of information processing methods and decision support systems as well as their security.
The list of possible topics includes, but is not limited to:
- computational methods for (dis-) information analysis, especially in heterogenous types of data (images, text, tweets etc.)
- detection of fake news detection in social media
- deepfakes analysis
- images and video manipulation recognition
- architectural frameworks and design for (dis-)information detection
- aspects of explainability of information analysis systems and methods (including explainability of ML)
- adversarial attacks on information analysis
- explainability of deep learning
- learning how to detect the fake news in the presence of concept drift
- learning how to detect the fake news with limited ground truth access and on the basis of limited data sets, including one-shot learning
- proposing how to compare and benchmark the fake news detectors
- case studies and real-world applications
- human rights, legal and societal aspects of (dis-)information detection, including data protection and GDPR in practice
The session will be supported by by SocialTruth project (Socialtruth.eu), which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825477 and will be technically endorsed by IEEE SMC TC on Big Data Computing.
Computational Optimization, Modelling and Simulation – COMS
COMS2021 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 Science in IoT and Smart Systems – IoTSS
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 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
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, but also for a solution of partial and ordinary differential equations, 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, in general.
This ICCS 2021 workshop 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 also 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
- Radial Basis Functions for a mesh morphing and data mapping
- Meshfŕee methods for corrupted image reconstruction and inpainting removal
- Meshfree methods applications in general
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 9 years since 2011 within the International Conference on Computational Science (ICCS). The aim of this special issue 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 as well as review 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.