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.

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.

  1. Biomedical and Bioinformatics Challenges for Computer Science – BBC
  2. Computational Optimization, Modelling and Simulation – COMS
  3. Data Driven Computational Sciences – DDCS
  4. Multiscale Modelling and Simulation – MMS
  5. Simulations of Flow and Transport: Modeling, Algorithms and Computation – SOFTMAC
  6. Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning – SmartSys
  7. Teaching Computational Science – WTCS

Biomedical and Bioinformatics Challenges for Computer Science – BBC 2019

Contact: Mario Cannataro, cannataro@unicz.it
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.

Computational Optimization, Modelling and Simulation – COMS 2019

Contact: Xin-She Yang, x.yang@mdx.ac.uk
Description: 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.

Data-Driven Computational Sciences – DDCS 2019

Contact: Craig C. Douglas, craig.c.douglas@gmail.com
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.

Multiscale Modelling and Simulation – MMS 2019

Contact: Derek Groen, Derek.Groen@brunel.ac.uk
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

Contact: Shuyu Sun, shuyu.sun@kaust.edu.sa
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 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:

  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.

Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning – SmartSys 2019

Contact: João Rodrigues, jrodrig@ualg.pt
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.

Teaching Computational Science – WTCS 2019

Contact: Angela B. Shiflet, shifletab@wofford.edu
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.