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 2024, you can find all necessary details on the Call for Tracks webpage.
- Advances in High-Performance Computational Earth Sciences: Numerical Methods, Frameworks & Applications – IHPCES
- Artificial Intelligence and High-Performance Computing for Advanced Simulations – AIHPC4AS
- Biomedical and Bioinformatics Challenges for Computer Science – BBC
- Computational Health – CompHealth
- Computational Optimization, Modelling and Simulation – COMS
- Generative AI and Large Language Models (LLMs) in Advancing Computational Medicine – CMGAI
- Machine Learning and Data Assimilation for Dynamical Systems – MLDADS
- Multiscale Modelling and Simulation – MMS
- Network Models and Analysis: From Foundations to Artificial Intelligence – NMAI
- Numerical Algorithms and Computer Arithmetic for Computational Science – NACA
- 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: Numerical Methods, Frameworks & Applications – IHPCES
Topics of interest include, but not limited to:
- Numerical methods for computational fluid dynamics (CFD) and continuum mechanics as a basis for simulations in atmospheric science, ocean science, solid earth science, space & planetary science, and other earth sciences.
- 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 or GPU accelerators.
- 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.
- 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 Health – CompHealth
- Simulation and modeling in healthcare and medicine (H&M)
- Complex processes and systems in H&M
- Networks in H&M
- Uncertainty management in H&M
- Numerical methods in H&M
- Data & process mining, ML & AI in H&M
- Knowledge and data processing in H&M
- Decision support and recommending systems in H&M
- Advanced medical information systems
Computational Optimization, Modelling and Simulation – COMS
COMS2024 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
Generative AI and Large Language Models (LLMs) in Advancing Computational Medicine – CMGAI
- Clinical Decision Support: Uncover the potential of these advanced models in providing decision support for healthcare professionals. How can Generative AI enhance clinical decision-making processes.
- Patient-Clinician Communication: Explore the role of Generative AI and LLMs in facilitating effective communication between patients and healthcare providers. How can these models improve the patient experience.
- Biomedical Text Mining: Navigate the intricate world of biomedical text mining and discover how Generative AI and LLMs can revolutionize information extraction and knowledge discovery in computational medicine.
- Drug Discovery and Repurposing: Investigate the impact of Generative AI on drug discovery and repurposing efforts. How can these models expedite the identification of potential treatments and therapeutic combinations.
- Integrating Large Language Models and Knowledge Graphs for Generative AI to empower BioMedical Applications
- Using GenAI to advance biomedical and lifescience workflow systems
- Assessment of content, generated detection, fact checking for biomedical applications
- Literature mining using Generative AI and LLMs for biomedical discoveries
- Advancing search engines and semantic search engines using Generative AI and LLMs
- The use of GenAI and LLMs to extract entity relationships from text (including causal relatiosnships)
Machine Learning and Data Assimilation for Dynamical Systems – MLDADS
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.
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 Artificial Intelligence – NMAI
Most recently, Network science and Artificial intelligence (AI) are become two interconnected fields that can complement each other in various ways.
The combination of these two domains has the potential to address complex problems. Graph Neural Networks (GNNs): GNNs have gained popularity in recent years. These neural networks are designed to operate on graph-structured data, making them well-suited for tasks where relationships between data points are crucial.
The goal of the workshop to bring together scientists in the fields of bioinformatics, biomedicine, medical informatics, as well as scientists working in biology and medicine, to collect advanced works on development of new pipelines, algorithms and tools for the network analysis of complex systems in different domains coupled with AI.
Topics of interest include, but are not limited to:
- Network-based bioinformatics methods
- Networks-based applications in computational biology, genomics, medicine, and healthcare
- Graph representation learning for visualizing and interpreting biological and biomedical data
- Bioinformatics methods for network-based analysis and visualization
- Network-based modeling and analysis of complex diseases
- Complex network models for structure and function analysis
- Network models in epidemiology
- Next-generation network science
- Artificial intelligence for network models of complex diseases
- Applications of deep learning approaches in computational biology, genomics, medicine, and healthcare
- Networks Alignment
- Complex Prediction
- Network Embedding
- Pathways Analysis
- Multilayer Network
Numerical Algorithms and Computer Arithmetic for Computational Science – NACA
- Foundations of computer arithmetic: emerging number systems and their applications
- Novel arithmetic algorithms, their analysis, and applications
- Efficient, high-performance, novel implementations of computer arithmetic in software and hardware
- Integer or floating-point operations, elementary and special functions, multiple-precision computing, interval arithmetic
- New algorithms and properties of floating-point arithmetic in emerging domains and applications
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 12 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 solved 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
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 workforce, and flexible learning.