Thematic Tracks

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.

  1. Advances in High-Performance Computational Earth Sciences: Applications and Frameworks – IHPCES
  2. Agent-Based Simulations, Adaptive Algorithms and Solvers – ABS-AAS
  3. Applications of Computational Methods in Artificial Intelligence and Machine Learning – ACMAIML
  4. Biomedical and Bioinformatics Challenges for Computer Science – BBC
  5. Classifier Learning from Difficult Data – CLD2
  6. Complex Social Systems through the Lens of Computational Science – CSOC
  7. Computational Health – CompHealth
  8. Computational Methods for Emerging Problems in (dis-)Information Analysis – DisA
  9. Computational Optimization, Modelling and Simulation – COMS
  10. Computational Science in IoT and Smart Systems – IoTSS
  11. Computer Graphics, Image Processing and Artificial Intelligence – CGIPAI
  12. Data Driven Computational Sciences – DDCS
  13. Machine Learning and Data Assimilation for Dynamical Systems – MLDADS
  14. Meshfree Methods in Computational Sciences – MESHFREE
  15. Multiscale Modelling and Simulation – MMS
  16. Quantum Computing Workshop – QCW
  17. Simulations of Flow and Transport: Modeling, Algorithms and Computation – SOFTMAC
  18. Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning – SmartSys
  19. Software Engineering for Computational Science – SE4Science
  20. Solving Problems with Uncertainties – SPU
  21. Teaching Computational Science – WTCS
  22. Towards the Future of Marine Computation for Sustainable Development – MarineComp
  23. Uncertainty Quantification for Computational Models – UNEQUIvOCAL

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

Contact: Takashi Shimokawabe,

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.
  • 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

Contact: Maciej Paszynski,
Description: This workshop aims to integrate the 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, ant optimization, and others. The workshop is most interested in simulations performed by using agent-oriented systems or by utilizing adaptive algorithms, but simulations performed by other kinds of systems are also welcome. The agent-oriented system seems to be an attractive tool useful for numerous domains of applications. Adaptive algorithms allow a significant decrease in the computational cost by utilizing computational resources on the most important aspect of the problem. This year for the anniversary ICCS meeting, we will accept high-quality papers meeting three criteria a) novel computational model b) novel numerical results c) verification of the numerical results by the computational model.

Applications of Computational Methods in Artificial Intelligence and Machine Learning – ACMAIML

Contact: Kourosh Modarresi,
Description: Our time could be defined as the age of Data. With the availability of large amount of data and massive computational resources, the main challenge before data scientists is to get insightful information from the data. Naturally, AI (Artificial Intelligence) and ML (Machine Learning) are two main vehicles in getting the insights. The main type of recently available data is indeed a new, modern and unprecedented form of 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 in dealing with modern data is that many of the old methods that have been developed for analyzing data during the last decades cannot be applied directly to modern data. One major solution, to overcome this challenge, is to effectively use ensemble methods such as deep artificial neural networks. These types of ensemble models are heavily reliant on the deployment of efficient computational methods. Thus, it’s even more imperative to deploy faster, more accurate and robust computational techniques for AI and ML models.
This track covers the application of computational methods for Artificial Intelligence and Machine Learning models.

Biomedical and Bioinformatics Challenges for Computer Science – BBC

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: (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.

Classifier Learning from Difficult Data – CLD2

Contact: Michal Wozniak,
Description: 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

Complex Social Systems through the Lens of Computational Science.

Web Address: Coming soon.
Contact: Debraj Roy,
Description: The majority of humans today live in complex societies, which exists on the basis of extensive cooperation among large numbers of individuals. Recent studies in the social sciences have focused on interconnectivity in social relationships and the emergence of new properties within society. As a theoretical tool, social complexity theory serves as a basis for the emergence of macro-level (or meso-level) social phenomena, providing a theoretical platform for hypothesis formation addressing micro-macro dynamics. For e.g., cities are the quintessential example of such complex social systems that emerge not dictated in a top-down manner. Rapid urbanization and increasing interconnectivity have threatened the sustainable development of urban areas. Viewing and analyzing cities as complex systems should therefore lead to new perspectives.
The aim of the workshop is to stimulate interdisciplinary research and cooperation to develop complex systems-based approaches aimed at understanding social systems. In general, the workshop will focus on the following questions: What are the patterns in social systems that cannot be explained by the existing theories and data (e.g. social innovation)? What type of models are needed to capture the mechanisms underlying the empirical patterns? What kinds of data are needed to better inform such models? How sensitive are these models for different implementations of theoretical concepts? What new modeling techniques and methods need to be developed? Are modelling frameworks that integrate different behavioral theories (e.g. needs, decision making, social networks) a necessity in modelling particular patterns/social dynamics in urban areas? How can these models contribute to policy making?
One of the immediate outputs of the workshop will be publication of a special issue in a scientific journal to be determined.
Broad topics in the workshop include:

  • Modelling social phenomena such as (but not limited to) – population dynamics, migration patterns, social innovations in city planning, armed conflicts, diffusion of new technologies, political movements, natural disasters, etc. – and the many possible arrangements of relationships between these discrete phenomena.
  • Modelling for sustainable development goals (poverty, well-being, food security, water, energy etc.) in rapidly growing urban complex societies. Focusing on the role of modelling in policy and urban-planning.
  • Emergent phenomena of complex social systems: self-organization, regime shifts, tipping points and resilience.
  • Empirical calibration and validation of computational models for complex social systems.
  • New modeling techniques in computational social science.
  • Novel (curated) datasets for understanding complex social systems. The Social Complexity of Digital Data (new metatheory, complex realism in social research).
  • Computational analysis of complex social systems, e.g., network analysis, social media, big data etc.

Computational Health – CompHealth

Contact: Sergey V. Kovalchuk,
Description: The field of computational science application in healthcare and medicine (H&M) is rapidly growing. Modeling and simulation, data and process mining, numerical methods, intelligent technologies provide new insights, support decision making, policy elaboration, etc. Moreover, this area gives quantitative support to emerging concepts in the area like P4-medicine (personalized, predictive, preventive, and participatory), value-based healthcare, and others. This workshop is aimed to bring together research in computational science and intelligent technologies applied in H&M in all the diversity of scales and aspects.
The scope of the workshop includes (but not limited to) the following areas: simulation and modeling in H&M; complex processes and systems 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; etc.

Computational Methods for Emerging Problems in (dis-)Information Analysis – DisA

Contact: Michal Choras,

Description: Information analysis is nowadays crucial for societies, single citizens in their everyday life (e.g. while travelling, shopping, browsing, communication etc.) as well for businesses and overall economy. The right to be informed is one of fundamental requirements allowing for taking right decisions in a small and large scale (e.g. elections).
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
  • 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 (, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825477 and will be technically endorsement by IEEE SMC TC on Big Data Computing.

Computational Optimization, Modelling and Simulation – COMS

Web Address: Coming soon.
Contact: Xin-She Yang,

Description: The 11th workshop “Computational Optimization, Modelling and Simulation (COMS 2020)” will be a part of the International Conference on Computational Science (ICCS 2020). This will be the 11th 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, China and Portugal, now back to Amsterdam again in 2020. COMS 2020 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.
COMS2020 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

Contact: Vaidy Sunderam,
Description:The 2nd workshop on “Computational Science in IoT and Smart Systems” addresses applications and tools suitable for Internet of Things and Smart 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.

The objective of this workshop is to bring together researchers and practitioners in IoT and Smart Systems to exchange new ideas and experiences, as well as future challenges, in the role of IoTSS in omputational science.

Topics of interest include IoT computing models, applications of IoTSS in advancing computational science, data management in IoT systems, architectures and tools for IoTSS, and related issues.

Computer Graphics, Image Processing and Artificial Intelligence – CGIPAI

Web Address: Coming soon.
Contact: Andres Iglesias,
Description: Computer Graphics, Image Processing and Artificial Intelligence are three of the most popular, exciting and hot domains in Computational Sciences. The three areas share a broad range of applications in many different fields, and new impressive developments are arising every year. This workshop is aimed at providing a forum for discussion about new techniques, algorithms, methods, and technologies in any of those areas as well as their applications to science, engineering, industry, education, health, and entertainment. The interplay between any two of these areas is also of interest for this workshop. The workshop is part of the activities of the Horizon 2020 European project PDE-GIR, but it is open to researchers and practitioners working in these areas. We invite prospective authors to submit their contributions for fruitful interdisciplinary cooperation and exchange of new ideas and experiences, as well as to identify new issues and challenges, and to shape future directions, trends for research.
Potential topics include, but are not limited to:
Topics include (but not limited to):

  • Geometric and Solid Modeling & Processing, CAD/CAM/CAE, Curve/Surface Reconstruction
  • Computer Graphics Techniques, Algorithms, Software and Hardware
  • Computer Animation and Video Games
  • Virtual and Augmented Reality, Virtual Environments and Autonomous Agents
  • Computer Graphics Applications (Science, Engineering, Education, Health, Industry, Entertainment)
  • Image Processing techniques
  • Image Processing processes (e.g., image denoising, image deblurring, image segmentation, image reconstruction, depth estimation, 3D surface restoration)
  • Image Processing applications
  • Evolutionary and Nature-Inspired Algorithms (evolutionary programming, genetic algorithms)
  • Neural Networks, Machine Learning, Deep Learning and Data Mining
  • Swarm Intelligence and Swarm Robotics
  • Bio-informatics and bio-engineering
  • Natural Computing, Soft Computing and Evolutionary Computing
  • Artificial Intelligence Applications
  • Interplay among some of the previous areas.

Data-Driven Computational Sciences – DDCS

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.

Machine Learning and Data Assimilation for Dynamical Systems – MLDADS

Contact: Rossella Arcucci,
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 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 in Computational Sciences – MESHFREE

Contact: Vaclav Skala,
Description: Meshfree methods are a hot topic in computational sciences and numerical mathematics.
Standard computational methods used across many application fields require tessellation in 2D or 3D using triangular or tetrahedral meshes. Tessellation itself is computationally expensive especially in higher dimensions and the result of that computation is again discrete, and physical phenomena are not smoothly interpolated. 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 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 2020 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.The accepted and presented papers are expected to be published in the ICCS 2020 conference proceedings.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
  • Scatted data interpolation and approximation methods

Multiscale Modelling and Simulation – MMS

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 sciences and socio-economic domains. Most of the real-life systems encompass interactions within and between a wide range of space and time 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 ecosystems.

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.

Key topics of the MMS workshop include:

  • Simulation and modelling of multiscale systems.
  • Challenging applications in science, industry or society (e.g. in computational biology).
  • Verification, validation and uncertainty quantification in a multiscale simulation/modelling context.
  • New approaches for coupling and scale bridging, to combine different models and scales in one application.
  • Advanced numerical methods for solving multiscale problems.
  • Software approaches for simulating multiscale systems, and handling the complex workloads accompanying it.
  • Executing multiscale models on advanced computational infrastructures (distributed, HPC, cloud, etc.).
  • Performance analysis of multiscale applications and/or tools.

Quantum Computing Workshop – QCW

Contact: Katarzyna Rycerz,

Description: Quantum computing is a new paradigm that exploits fundamental principles of quantum mechanics to solve problems in various fields of science that are beyond possibilities of classical computing infrastructures. Despite increasing activity in both theoretical research and hardware implementations, reaching the state of useful quantum supremacy is still an open question. This workshop aims to provide a forum for computational scientists, software developers, computer scientists, physicists and quantum hardware providers to understand and discuss research on current problems in quantum informatics.

Keywords: quantum computing; quantum algorithms; quantum software engineering; quantum simulators; quantum network

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

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 international workshop on “Simulations of Flow and Transport: Modeling, Algorithms and Computation” (SOFTMAC) has been held 8 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

Contact: Pedro Jorge Sequeira Cardoso,
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’20 brings together computer vision, sensor networks, machine learning algorithms, and applications to solve present and 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.

Software Engineering for Computational Science – SE4Science

Contact: Jeffrey Carver,
Description: This is a time of great growth at the intersection of software engineering and computational science. There is a need for members of these communities to share experiences, identify problems, and enumerate common goals to form the basis for an ongoing research agenda. The goal of this workshop is to provide a unique venue for the presentation of results and to facilitate interaction between software engineers and computational scientists. To address this goal, we seek contributions from members of both communities that describe perspectives, research outcomes, and lessons learned (positive or negative) from the development of computational science software. Specifically, we are interested in the software development and software engineering challenges and enablers relating to: (1) Computational science software applications solve complex software- or data-intensive research problems. These applications range from large parallel models/simulations of the physical world using HPC systems to smaller scale simulations developed by a single scientist or engineer on a desktop machine or a small cluster. (2) Applications domains ranging from humanities to engineering to science. (3) Applications that support scientific research and experiments at scale. Such applications include, but are not limited to, systems for managing and/or manipulating large amounts of data and systems that provide infrastructure for scientific or engineering applications such as libraries or HPC/Cloud software. (4) The process for building, reusing, and publishing software and data used in scientific experiments or engineering innovations. Among others, these processes include agile approaches, open source/open data issues, testing scientific software, and managing software or data repositories for publishing goals. (5) This workshop will build upon previous SE4Science workshops. Similar to the format of the previous workshops, in addition to presentation and discussion of the accepted papers, we plan to devote significant time during the workshop to discussing important topics that arise from the paper presentations. The goal of these discussions is to (1) develop a joint research plan that can be conducted collectively by workshop participants and (2) development of ideas/draft of position statements to be published externally.

Solving Problems with Uncertainties – SPU

Contact: Vassil Alexandrov,
Description: 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 Exascale Computing and Big Data Analytics and scalable AI larger and larger problems, often requiring hybrid approaches, have to be tackled in a systematic way at scale 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, short and full papers presenting Domain Applications and Case studies in the context of the Computational Science/ HPC and Big Data Analysis and AI 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, advanced Data Science/AI/Computational Science) 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 including tackling problems at scale.

Teaching Computational Science – WTCS

Contact: Angela B. Shiflet,
Description: The twelfth Workshop on Teaching Computational Science (WTCS 2020) 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.

Towards the Future of Marine Computation for Sustainable Development – MarineComp

Contact: Flávio Augusto Bastos da Cruz Martins,

Description: Our future is intimately linked to the future of the seas, oceans and coasts. They provide multiple resources and ecosystem services, they influence climate and provide many economic opportunities. To fully profit from the seas and oceans in the future, we need to preserve those valuable resources and ensure that their exploitation is sustainable. Computational marine tools are a key factor on the fulfillment of those objectives, explaining why they are recognized as a central piece on UN Decade of Ocean Sciences. After the success of the 2019 edition, MarineComp2020 will focus on the use of computational methods for the simulation of every aspect of the marine environment: currents, waves, pollutant transport, among others, as well as every scale, from local studies in estuaries and coastal systems to global ocean studies. Integrated studies blending different scales and/or processes are encouraged. As the atmospheric system is influenced by and influences the marine environment, coupled atmospheric ocean studies are welcome. Although papers dealing with advancements in numerical algorithms and improvements in computational performance are accepted, we aim specifically at papers advancing the human capacity in dealing with the sustainable development goals as defined by the UN, as well as the human capacity to harvest the economic potential of the ocean, according to the blue growth model. Accepted themes include, but are not limited to:

  • Assimilation of new sensor and data in Marine Modelling, such as low cost sensors, IoT platforms, Low cost drifting buoys, Gliders and AUV, New remote observation platforms, etc.;
  • Downscale and upscale methodologies leading to high resolution and extended coverage modelling systems;
  • Integration of inland, coastal and oceanic modelling systems;
  • Coupled Atmosphere-Ocean models and/or climate change studies;
  • New emerging ocean industries, processes and threats;
  • Marine modelling applications for the end users, such as Apps for citizens; for ocean literacy, specific industry applications, safety and hazard oriented tools among others.

Uncertainty Quantification for Computational Models – UNEQUIvOCAL

Contact: Wouter Edeling,
Description: Given that uncertainty is unavoidable in almost all scientific fields, due to e.g. unknown parameters or simplifying modelling assumptions, uncertainty quantification is an indispensable part in state-of-the-art computational models. In order to build confidence in their results, it is therefore crucial that these models carry their own measure of uncertainty, especially when they are extrapolated beyond the domain in which they were originally calibrated. Also, the oncoming Exascale Computing resources will open up the possibility of solving problems with increased complexity and computational burden, exacerbating the importance (and demands) of reliable uncertainty quantification methods. This thematic track aims to attract research that focuses on new methods, which outperform existing techniques, as well as uncertainty quantification applications to complex problems. Topics of interest include but are not limited to the following:

  • Forward and inverse uncertainty quantification;
  • Model inadequancy;
  • Sensitivity analysis;
  • Dimension reduction;
  • Surrogate modelling (including machine-learning techniques and reduced-order modelling);
  • Case studies showing efficient uncertainty quantification methods;
  • Software for uncertainty quantification.