Registered Workshops

Thematic workshops organized by experts in a particular area constitute the core of the conference. The list of accepted workshops is below, please click through for brief information and workshop web/contact addresses to follow to find full details.

  1. Advances in High-Performance Computational Earth Sciences: Applications and Frameworks
  2. Agent-Based Simulations, Adaptive Algorithms and Solvers
  3. Applications of Matrix Methods in Artificial Intelligence and Machine Learning
  4. Architecture, Languages, Compilation and Hardware Support for Emerging ManYcore Systems
  5. Biomedical and Bioinformatics Challenges for Computer Science
  6. Computational Finance and Business Intelligence
  7. Computational Optimization, Modelling and Simulation
  8. Data-Driven Computational Sciences
  9. Data, Modeling, and Computation in IoT and Smart Systems
  10. Mathematical-Methods-and-Algorithms for Extreme Scale
  11. Multiscale Modelling and Simulation
  12. Simulations of Flow and Transport: Modeling, Algorithms and Computation
  13. Solving Problems with Uncertainties
  14. Teaching Computational Science
  15. Tools for Program Development and Analysis in Computational Science
  16. Urgent Computing

Advances in High-Performance Computational Earth Sciences: Applications and Frameworks – IHPCES 2018

Contact: Xing Cai,
Description: The IHPCES workshop series provides a forum for presentation and discussion of state-of-the-art research in high performance computational earth sciences. The emphasis of the eighth 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 co-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.

Agent-Based Simulations, Adaptive Algorithms and Solvers – ABS-AAS 2018

Contact: Maciej Paszynski,
Description: The aim of this workshop is to integrate results of different domains of computer science, computational science and mathematics.
We invite papers oriented toward simulations, either hard simulations by means of finite element or finite difference methods, or soft simulations by means of evolutionary computations, particle swarm optimization and other. The workshop is most interested in simulations performed by using agent-oriented systems or by utilizing adaptive algorithms, but simulations performed by other kind of systems are also welcome. Agent-oriented system seems to be the attractive tool useful for numerous domains of applications. Adaptive algorithms allow significant decrease of the computational cost by utilizing computational resources on most important aspect of the problem.
This year following the challenges of ICCS 2018 theme “Science at the Intersection of Data, Modelling and Computation” we invite submissions using techniques dealing with large simulations, e.g. agents based algorithms dealing with big data, model reduction techniques for large problems, fast solvers for large three dimensional simulations, etc.
To give – rather flexible – guidance in the subject, the following, more detailed, topics are suggested.
These of theoretical brand, like:

  1. multi-agent systems in high-performance computing,
  2. efficient adaptive algorithms for big problems,
  3. low computational cost adaptive solvers
  4. fast solvers for isogeometric finite element method,
  5. agent-oriented approach to adaptive algorithms,
  6. model reduction techniques for large problems,
  7. mathematical modeling and asymptotic analysis of large problems,
  8. finite element or finite difference methods for three dimensional or non-stationary problems,
  9. mathematical modeling and asymptotic analysis.

And those with stress on application sphere:

  1. agents based algorithms dealing with big application of adaptive algorithms in large simulation,
  2. simulation and large multi-agent systems,
  3. applications of isogeometric finite element method,
  4. application of adaptive algorithms in three dimensional finite element and finite difference simulations,
  5. application of multi-agent systems in computational modeling,
  6. multi-agent systems in integration of different approaches.

Applications of Matrix Methods in Artificial Intelligence and Machine Learning – AMAIML 2018

Contact: Kourosh Modarresi,
Description: With availability to large amount of data, the main challenge of our time is to get insightful information from the data. Therefore, artificial intelligence and machine learning are two main paths in getting the insights from the data we are dealing with. The data we currently have is a new and unprecedented form of data, “Modern Data”. “Modern Data” has unique characteristics such as, extreme sparsity, high correlation, high dimensionality and massive size. Modern data is very prevalent in all different areas of science such as Medicine, Environment, Finance, Marketing, Vision, Imaging, Text, Web, etc. A major difficulty is that many of the old methods that have been developed for analyzing data during the last decades cannot be applied on modern data. One distinct solution, to overcome this difficulty, is the application of matrix computation and factorization methods such as SVD (singular value decomposition), PCA (principal component analysis), and NMF (non- negative matrix factorization), without which the analysis of modern data is not possible. This workshop covers the application of matrix computational science techniques in dealing with Modern Data.

Architecture, Languages, Compilation and Hardware Support for Emerging ManYcore Systems – ALCHEMY 2018

Contact: Stephane Louise,
Description: Many-core systems are though to be the future of computing both in the large scale computing world and in the embedded processing world. However they convey a challenge for programmability. This workshop addresses all the questions and answers being at hardware level, or software level; proposed solutions and still open questions.

Biomedical and Bioinformatics Challenges for Computer Science – BBC 2018

Contact: Mario Cannataro,
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:

  • Advanced computing architectures
  • Data management and integration
  • Data analysis and knowledge discovery
  • Integration of quantitative/symbolic knowledge into executable biomedical “theories” or models.

Computational Finance and Business Intelligence – CFBI 2018

Contact: Yong Shi,
Description: The workshop focus on computational science aspects of asset/derivatives pricing & financial risk management that relate to business intelligence. It will include but not limited to modeling, numeric computation, soft computing, algorithmic and complexity issues in arbitrage, asset pricing, future and option pricing, risk management, credit assessment, interest rate determination, insurance, foreign exchange rate forecasting, online auction, cooperative game theory, general equilibrium, information pricing, network band witch pricing, rational expectation, repeated games, etc.

Computational Optimization, Modelling and Simulation – COMS 2018

Contact: Xin-She Yang,
Description: The 9th workshop “Computational Optimization, Modelling and Simulation (COMS 2018)” will be a part of the International Conference on Computational Science (ICCS 2018). This will be the 9th 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 and Switzerland. COMS 2018 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.
COMS2018 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.

Data-Driven Computational Sciences – DDCS 2018

Contact: Craig C. Douglas,
Description: In the late 1960’s, simple data assimilation revolutionarily transformed science in fields based on satellite data. Both NASA and NCAR produced stunningly revolutionary applications. The oil and gas industry jumped on this concept in the early to mid 1970’s creating commercial data assimilation pipeline products by multiple vendors that were used in more than 165 countries in short order. This led to intelligent data assimilation being the normal way to operate a reservoir or pipeline networks by the 1990’s by all of the major oil producers. Since the early 2000’s, government grant agencies (e.g., the National Science Foundation) applied this concept to update numerous fields creating astonishing improvemnts in simulations that continue to this day in many application areas.

Data, Modeling, and Computation in IoT and Smart Systems – DMC-IoT 2018

Contact: Vaidy Sunderam,
Description: Computational Science has rapidly evolved to become a mainstream paradigm in every walk of society – and is now embracing two additional dimensions: (1) Big Data; and (2) IoT and Smart Systems. The latter are emerging in numerous computational science environments e.g. industrial control systems, sensor networks, quantum computing simulators, and neural/learning approaches to traditional problems, and in all cases, involve the five V’s of big data. Methods and paradigms for addressing computation and data issues in such platforms will be crucial in the near future.
The proposed workshop on “Data, Modeling, and Computation in IoT and Smart Systems” is aimed at addressing applications and tools suitable for such systems in computational science settings. This workshop focuses on understanding and discussing computing paradigms, data management solutions, reliability, efficiency, and performance issues in IoT and Smart Systems. The workshop will also include and discuss advances in Computational Science that may be achieved through IoT platforms connected to traditional supercomputers.

Mathematical-Methods-and-Algorithms for Extreme Scale – MATH-EX 2018

Contact: Vassil Alexandrov,
Description: Novel mathematics and mathematical modelling approaches together with scalable scientific algorithms are needed to enable key science applications at extreme-scale. This is especially true as HPC systems continue to scale up in compute node and processor core count. These extreme-scale systems require novel mathematical methods to be developed that lead to scalable scientific algorithms to hide network and memory latency, have very high computation/communication overlap, have minimal communication, have no synchronization points. The workshop seeks strategic and position papers in the above areas and to serve as a forum for Computational Scientists to discuss the mathematical and algorithmic challenges and approaches towards exascale and beyond.

Multiscale Modelling and Simulation – MMS 2018

Contact: Derek Groen,
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 spatio-temporal 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 eco systems.
This workshop, which is part of the International Conference on Computational Science and will be organized for the 15th time, 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.

Simulations of Flow and Transport: Modeling, Algorithms and Computation – SOFTMAC 2018

Contact: Shuyu Sun,
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 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:

  1. 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;
  2. 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;
  3. 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;
  4. a-priori and a-posteriori error estimates in discretizations and decompositions; numerical convergence study; adaptive algorithms and implementation;
  5. 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;
  6. modeling and simulation of subsurface and surface transport and geochemistry, and its application to environmental sciences and engineering;
  7. computational thermodynamics of fluids, especially hydrocarbon and other oil reservoir fluids, and its interaction with flow and transport;
  8. 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.

Solving Problems with Uncertainties – SPU 2018

Contact: Vassil Alexandrov,
Description: The 6th SPU Workshop: Problems with uncertainty need to be tackled in an increasing variety of areas ranging from problems in physics, chemistry, computational biology to decision making in economics and social sciences. Uncertainty is unavoidable in almost all systems analysis, in risk analysis in decision making and economics and financial modelling, in weather and pollution modelling, disaster modelling and simulation (earthquake modelling, forest fires simulation etc.). 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 and Big Data tackling uncertainties in case of larger and more complex problems in a systematic way becomes even more important due to the variety and scale of uncertainties in such problems.

Teaching Computational Science – WTCS 2018

Contact: Angela B. Shiflet,
Description: The ninth Workshop on Teaching Computational Science (WTCS 2018) 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.

Tools for Program Development and Analysis in Computational Science – TOOLS 2018

Contact: Karl Fuerlinger,
Description: The primary intention of this workshop is to bring together developers of tools for scientific computing and their potential users. Paper submissions by both tool developers and users from the scientific and engineering community are encouraged in order to inspire communication between both groups. Tool developers can present to users how their tools support scientists and engineers during program development and analysis. Tool users are invited to report their experiences employing such tools, especially highlighting the benefits and the improvements possible by doing so.

Urgent Computing – UC 2018

Contact: Alexander Boukhanovsky,
Description: Complex, large-scale, collaborative simulations are becoming more and more crucial for decision making in critical situations like floods, earthquakes, wildfires, terroristic attacks, epidemics, pandemics, instabilities in financial markets and similar. Very often they are run in almost real time. Urgent computing is a new interdisciplinary research area of computer science addressing algorithms, methods and tools enabling prioritized and immediate access to distributed, large compute and storage systems (computers, grids, clouds) for such emergency computations required for clever decision making. This type of simulation provides data to decision support systems enabling decision makers to choose an optimal behavior scenario in time limitations.