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.
We will be adding several more workshops in the coming weeks.
If you are interested in organizing a workshop at ICCS 2019, you can find all necessary details on the Call for Workshops webpage.
- Advanced Modelling Techniques for Environmental Sciences – AMES
- Advances in High-Performance Computational Earth Sciences: Applications and Frameworks – IHPCES
- Agent-Based Simulations, Adaptive Algorithms and Solvers – ABS-AAS
- Architecture, Languages, Compilation and Hardware Support for Emerging and Heterogeneous Systems – ALCHEMY
- Applications of Matrix Methods in Artificial Intelligence and Machine Learning – AMAIML
- Biomedical and Bioinformatics Challenges for Computer Science – BBC
- Classifier Learning from Difficult Data – CLD2
- Computational Finance and Business Intelligence – CFBI
- Computational Methods in Smart Agriculture – CMSA
- Computational Optimization, Modelling and Simulation – COMS
- Computational Science in IoT and Smart Systems – IoTSS
- Data Driven Computational Sciences – DDCS
- Machine Learning and Data Assimilation for Dynamical Systems – MLDADS
- Marine Computing in the Interconnected World for the Benefit of the Society – MarineComp
- Multiscale Modelling and Simulation – MMS
- Simulations of Flow and Transport: Modeling, Algorithms and Computation – SOFTMAC
- Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning – SmartSys
- Solving Problems with Uncertainties – SPU
- Teaching Computational Science – WTCS
- Tools for Program Development and Analysis in Computational Science – TOOLS
Advanced Modelling Techniques for Environmental Sciences – AMES 2019
Description: Our natural environment consists of a vast variety of different physical systems, all of which are all closely coupled. This intrinsic interdisciplinarity poses unique challenges to computational sciences, requiring advanced modelling and data techniques. This workshop addresses the associated challenges, focussing on multi-model, multi-scale and multi-physics techniques and solutions for environmental sciences. It addresses model developers, framework developers, and e-infrastructure providers who tackle the challenges posed by this unique setting. In particular, we will discuss model scaling and model coupling techniques, as well as algorithm development and workflow management in environmental sciences.
Advances in High-Performance Computational Earth Sciences: Applications and Frameworks – IHPCES 2019
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.
- 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 2019
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.
To give – rather flexible – guidance in the subject, the following, more detailed, topics are suggested.
These of theoretical brand, like:
- multi-agent systems in high-performance computing,
- efficient adaptive algorithms for big problems,
- low computational cost adaptive solvers
- fast solvers for isogeometric finite element method,
- agent-oriented approach to adaptive algorithms,
- model reduction techniques for large problems,
- mathematical modeling and asymptotic analysis of large problems,
- finite element or finite difference methods for three dimensional or non-stationary problems,
- mathematical modeling and asymptotic analysis,
- stabilization of finite element method simulations.
And those with stress on application sphere:
- agents based algorithms dealing with big application of adaptive algorithms in large simulation,
- simulation and large multi-agent systems,
- applications of isogeometric finite element method,
- application of adaptive algorithms in three dimensional finite element and finite difference simulations,
- application of multi-agent systems in computational modeling,
- multi-agent systems in integration of different approaches.
Architecture, Languages, Compilation and Hardware Support for Emerging and Heterogeneous Systems – ALCHEMY 2019
Description: This workshop aims at showing new way to tackle the performance out of distributed, parallel, and potentially heterogeneous systems by using a proper alchemy of any software and hardware ingredients including compilation technics and meta-programing, hardware support, specialized runtimes, specific accelerators, FPGA, etc.
Applications of Matrix Methods in Artificial Intelligence and Machine Learning – AMAIML 2019
Description: With the availability of 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.
Biomedical and Bioinformatics Challenges for Computer Science – BBC 2019
Description: Emerging technologies in biomedicine and bioinformatics are generating an increasing amount of complex data. In order 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, tools and systems. The aim of this workshop is to bring together computer and life scientists to discuss emerging directions in topics related to the key bioinformatics and computational biology techniques and technologies: advanced computing architectures; data management and integration; data analysis and knowledge discovery; algorithm design; Integration of quantitative/symbolic knowledge into executable biomedical models.
Classifier Learning from Difficult Data – CLD2 2019
Nowadays many practical decision task require to build models on the basis of data which included serious difficulties, as imbalanced class distributions, high number of classes, high-dimensional feature, small or extremely high number of learning examples, limited access to ground truth, data incompleteness, or data in motion, to enumerate only a few. Such characteristics may strongly deteriorate the final model performances. Therefore, the proposition of the new learning methods which can combat the mentioned above difficulties should be the focus of intense research.
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 Finance and Business Intelligence – CFBI 2019
Web Address: Coming soon.
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 Methods in Smart Agriculture – CMSA 2019
Web Address: Coming soon.
The agricultural sector is facing enormous challenges to increase food production despite limited availability of arable lands, the increasing need for fresh water and the impact of climate change. Natural resources, such as land, water, soil and genetic resources, must be better managed so that more productive and resilient agriculture can be achieved. This workshop is focussed on analytic tools and decision support systems that help to guide actions needed to transform and reorient agricultural systems.
Topics include, but are not limited to:
- Optimisation in agro-ecosystems
- Intelligent irrigation systems
- Integrated sensing technology
- Precision agriculture methods
- Agriculture decision support systems
Computational Optimization, Modelling and Simulation – COMS 2019
The 10th workshop “Computational Optimization, Modelling and Simulation (COMS 2019)” will be a part of the International Conference on Computational Science (ICCS 2019). This will be the 10th 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, Switzerland and China. COMS 2019 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.
COMS2019 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 2019
Description: The IoTSS workshop — “Computational Science in IoT and Smart Systems” is aimed at addressing systems, applications, and tools relating to IoT suitable for Computational Science. This workshop focuses on understanding and discussing computing paradigms, scalability, reliability, efficiency, and performance issues in IoT and Smart Systems.
Data-Driven Computational Sciences – DDCS 2019
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.
Machine Learning and Data Assimilation for Dynamical Systems – MLDADS 2019
Description: The object of the theory of dynamical systems addresses the qualitative behaviour of dynamical systems as understood from models.
Moreover, models are often not perfect and can be improved using data using tools from the field of Data Assimilation.
Additionally, the field of Machine Learning is concerned with algorithms designed to accomplish certain tasks whose performance improve with the input of more data.
The intersection of the fields of dynamical systems, data assimilation and machine learning is largely unexplored.
The goal of this symposium is to bring together researchers from these fields to fill the gap between these theories.
Marine Computing in the Interconnected World for the Benefit of the Society – MarineComp 2019
Description: Half of the world’s population live nowadays within 100 km of the sea. Ocean and coastal activities are amongst the largest sectors of the global economy. Advances in computational science produce nowadays large amounts of marine data, with few integration. Marine modelling can tackle this challenge in an increasingly interconnected world, providing tools to integrate and extend the new capabilities of permanent and ubiquitous marine observing sensors and platforms. This workshop aims at presenting the latest advances in ocean and costal modelling from the perspective of its interconnection with innovative/global ocean observing systems or producing significant impact on the society.
Multiscale Modelling and Simulation – MMS 2019
Description: 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.
Simulations of Flow and Transport: Modeling, Algorithms and Computation – SOFTMAC 2019
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 international workshop on “Simulations of Flow and Transport: Modeling, Algorithms and Computation” (SOFTMAC) has been held 7 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.
Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning – SmartSys 2019
Description: Smart Systems incorporate sensing, actuation, and intelligent control in order to analyze, describe or resolve situations, making decisions based on the available data in a predictive or adaptive manner. SmartSys’18 brings together computer vision, sensor networks, machine learning algorithms and application to solve present everyday problems. Other related areas are also welcome, such as augmented reality, human computer interaction, user experience, Internet of Things, Internet of everything, energy management systems, vehicle or person tracking system, operational research, and information systems in general, always with the focus in smart systems as tools to solve daily based problems.
Solving Problems with Uncertainties – SPU 2019
Problems with uncertainty need to be tackled in an increasing variety of areas ranging from problems in physics, chemistry, engineering, computational biology and environmental sciences to decision making in economics and social sciences. Uncertainty is unavoidable in almost all systems analysis, in risk analysis, in decision making and modelling and simulation. 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 Data Science and exascale computing, larger and larger problems have to be tackled in a systematic way and 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 uncertainty, 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, etc.
The workshop solicits strategic and full papers presenting Domain Applications and Case studies in the context of the Big Data and Computational Science/ HPC ecosystems covering but not limited to the following topics:
- methods and algorithms for solving problems with uncertainties
- stochastic methods and algorithms for solving problems with uncertainty
- hybrid (stochastic/deterministic) methods and algorithms for solving problems with uncertainties
- methods for quantifying uncertainties
- quantifying uncertainties while dealing with Big Data
- quantifying uncertainties while dealing with model inadequacy, model validation etc.
- sensitivity analysis
- case studies showing efficient methods and approaches solving problems with uncertainties
Teaching Computational Science – WTCS 2019
Description: The twelfth Workshop on Teaching Computational Science (WTCS 2019) 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 2019
The use of supercomputing technology, parallel and distributed processing, and sophisticated algorithms is of major importance for computational scientists. Yet, the scientists’ goals are to solve their challenging scientific problems, not the software engineering tasks associated with it. For that reason, computational science and engineering must be able to rely on dedicated support from program development and analysis tools.
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.