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.
We will be adding several more tracks in the coming weeks.

If you are interested in organizing a thematic track at ICCS 2021, you can find all necessary details on the Call for Tracks webpage.

  1. Advances in High-Performance Computational Earth Sciences: Applications and Frameworks – IHPCES
  2. Applications of Computational Methods in Artificial Intelligence and Machine Learning – ACMAIML
  3. Architecture, Languages, Compilation and Hardware support for EMerging and Heterogeneous sYstems – ALCHEMY
  4. Artificial Intelligence and High-Performance Computing for Advanced Simulations – AIHPC4AS
  5. Biomedical and Bioinformatics Challenges for Computer Science – BBC
  6. Classifier Learning from Difficult Data – CLD2
  7. Computational Analysis of Complex Social Systems – CSOC
  8. Computational Collective Intelligence – CCI
  9. Computational Health – CompHealth
  10. Computational Methods for Emerging Problems in (dis-)Information Analysis – DisA
  11. Computational Methods in Smart Agriculture – CMSA
  12. Computational Optimization, Modelling and Simulation – COMS
  13. Computational Science in IoT and Smart Systems – IoTSS
  14. Computer Graphics, Image Processing and Artificial Intelligence – CGIPAI
  15. Data-Driven Computational Sciences – DDCS
  16. Machine Learning and Data Assimilation for Dynamical Systems – MLDADS
  17. MeshFree Methods and Radial Basis Functions in Computational Sciences – MESHFREE
  18. Multiscale Modelling and Simulation – MMS
  19. Quantum Computing Workshop – QCW
  20. Simulations of Flow and Transport: Modeling, Algorithms and Computation – SOFTMAC
  21. Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning – SmartSys
  22. Software Engineering for Computational Science – SE4Science
  23. Solving Problems with Uncertainty – SPU
  24. Teaching Computational Science – WTCS
  25. Uncertainty Quantification for Computational Models – UNEQUIvOCAL

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

Contact: Takashi Shimokawabe, shimokawabe@cc.u-tokyo.ac.jp
Description: The IHPCES workshop provides a forum for presentation and discussion of state-of-the-art research in high performance computational earth sciences. The emphasis of the eleventh 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 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.

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

Contact: Kourosh Modarresi, kouroshm@alumni.stanford.edu
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.

Architecture, Languages, Compilation and Hardware support for EMerging and Heterogeneous sYstems – ALCHEMY

Contact: Loïc Cudennec, loic.cudennec@def.gouv.fr
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.

Artificial Intelligence and High-Performance Computing for Advanced Simulations – AIHPC4AS

Contact: Maciej Paszynski , paszynsk@agh.edu.pl
Description: This workshop aims to integrate knowledge in computer science, computational science, and mathematics. We invite papers oriented toward the applications of artificial intelligence (AI) and high-performance computing (HPC) in simulations, either in continuous simulations (e.g., finite element simulations of stationary problems using AI adaptive algorithms, HPC isogeometric analysis simulations of time-dependent problems, or application of deep learning for stabilization of space-time finite element simulations), as well as in discrete event simulations of complex-systems consisting of interacting individuals (e.g., HPC multi-agent simulations of the disease spread, or AI matching cellular automata parameters for the simulations of tumor growth).

Biomedical and Bioinformatics Challenges for Computer Science – BBC

Contact: Mario Cannataro, cannataro@unicz.it
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.
A special session will be devoted to bioinformatics and computer science methods to fight the COVID-19 pandemics.

Classifier Learning from Difficult Data – CLD2

Contact: Michał Woźniak, michal.wozniak@pwr.edu.pl
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

Computational Analysis of Complex Social Systems – CSOC

Web Address: coming soon
Contact: Debraj Roy, D.Roy@uva.nl
Description: The majority of humans today live in complex societies, which exist on the basis of extensive cooperation among large numbers of individuals. Recent studies in sociology have focussed 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.
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? What kinds of data are needed to better inform the models? What new modeling techniques and methods need to be developed?
One of the immediate outputs of the workshop will be the publication of a special issue in a scientific journal to be determined.
Broad topics in social complexity:
  1. Modeling social phenomena such as (but not limited to) – population dynamics, migration patterns, armed conflicts, political movements, natural disasters, etc – and the many possible arrangements of relationships between these discrete phenomena.
  2. Modeling for sustainable development goals (poverty, well-being, food security, water, energy, etc.) in rapidly growing urban complex societies. Focusing on the role of modeling in policy and urban planning.
  3. Modeling individual and group behavior. New modeling techniques in computational social science.
  4. Emergent phenomena of complex social systems: self-organization, regime shifts, tipping points, and resilience.
  5. Empirical calibration and validation of computational models for complex social systems.
  6. Novel (curated) datasets for understanding complex social systems. The Social Complexity of Digital Data (new metatheory, complex realism in social research).
  7. Computational analysis of complex social systems, e.g., network analysis, social media, big data, etc.

Computational Collective Intelligence – CCI

Contact: Ngoc Thanh Nguyen, ngoc-thanh.nguyen@pwr.edu.pl
Description: This special session during ICCS deals with the problem of Collective Intelligence, which is most often understood as an AI sub-field dealing with soft computing methods which enable making group decisions or processing knowledge among autonomous units acting in distributed environments. Web-based systems, social networks and multi-agent systems very often need these tools for working out consistent knowledge states, resolving conflicts and making decisions, which is an important part of modern and future computing.
The session is organized by the IEEE Computational Collective Intelligence technical committee, but welcomes all interested parties. The goal is to provide an internationally respected forum for scientific research in the computer-based methods of collective intelligence and their applications in (but not limited to) such fields as semantic web, social networks and multi-agent systems.
The topics of interest include, but are not limited to:
  • Group decision making
  • Consensus computing
  • Collective action coordination
  • Inconsistent knowledge processing
  • Ontology mapping and merging
  • Collaborative ontology
  • Ontology development in social networks
  • Semantic social networks
  • Semantic and knowledge grids
  • Semantic annotation of web data resources
  • Group web services (service description, discovery, composition)
  • Automatic metadata generation
  • Semantic web inference schemes
  • Reasoning in the semantic web
  • Knowledge portals
  • Knowledge discovery
  • Information retrieval
  • Advanced analysis for social networks dynamics
  • Social networks and semantic communication
  • Cooperative distributed problem solving
  • Multiagent planning
  • Negotiation protocols
  • Multiagent learning

Computational Health – CompHealth

Contact: Sergey Kovalchuk, sergey.v.kovalchuk@gmail.com
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; networks in H&M; uncertainty management in H&M; numerical methods in H&M; data & process mining, ML & AI in H&M; knowledge and data processing in H&M; decision support and recommending systems in H&M; advanced medical information systems; etc.

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

Contact: Michal Choras, chorasm@utp.edu.pl
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
  • deepfakes analysis
  • 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 (Socialtruth.eu), which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825477 and will be technically endorsed by IEEE SMC TC on Big Data Computing.

Computational Methods in Smart Agriculture – CMSA

Web Address: coming soon.
Contact: Andrew Lewis, a.lewis@griffith.edu.au
Description: 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. In a rapidly changing world, 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

The area presents significant and challenging difficulties in developing robust predictive tools subject to uncertainties in climate and ecosystem responses, with recommendations needed that are relevant, productive and accurate over multiple decades. Furthermore, expertise in agronomy and economic factors needs to be integrated with modelling and simulation to allow analysis of “whole system” dynamics. Current research efforts are proceeding in algorithm development and innovative computational methods to solve these problems, and deliver outcomes. In the context of an interconnected world – the research has the potential to contribute to climate change adaptation and food security in a global context. Research and tools are needed for decision support for policy responses at a range of levels of responsibility: individual, regional, national and international.

Computational Optimization, Modelling and Simulation – COMS

Contact: Xin-She Yang, x.yang@mdx.ac.uk
Description: The 12th workshop “Computational Optimization, Modelling and Simulation (COMS 2021)” will be a part of the International Conference on Computational Science (ICCS 2021). This will be the 12th 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, Portugal and Netherland. COMS 2021 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.
COMS2021 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, vss@emory.edu
Description: The proposed workshop on “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. The workshop will also include Computational Science applications that may be enabled or enhanced through IoT and Smart Systems platforms.

Computer Graphics, Image Processing and Artificial Intelligence – CGIPAI

Web Address: coming soon.
Contact: Andres Iglesias, iglesias@unican.es
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, now in its second edition, is part of the activities of the Horizon 2020 European project PDE-GIR, but it is open to any researcher and practitioner 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:
  • 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, 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.
A data-driven computational system is the integration of a simulation with dynamically and intelligently assimilated data, multiscale modeling, computation, and a two way interaction between the model execution and the data acquisition methods (see the DDDAS Scientific Community Web Site, http://www.dddas.org). The workshop will present opportunities as well as challenges and approaches in technology needed to enable Data-Driven Computational Science capabilities in applications, relevant algorithms, and software systems. All related areas in Data-Driven Sciences are included in this workshop, including CyberPhysical Systems like HealthKit on iPhones and iPads as well as similar systems developed by Intel, Google, and Microsoft for phones and tablets, Internet of Things (IoT), Cloud of Things (CoT), and Data Intensive Scientific Discovery (DISD).
A recent example is a tranformative way of landing airplanes on time and reduce delays and cancellations is a process known as Time Based Flow Systems (TBFS) [UKNATS]. It spaces planes by space instead of by time. The first of these systems was developed for Heathrow Airport by Lockheed Martin for the British National Air Traffic Services and fully deployed in May, 2015. It has reduced flight cancellations due to wind by exactly 100% and flight delays by approximately 40% during the period of May – August, 2015.

Machine Learning and Data Assimilation for Dynamical Systems – MLDADS

Contact: Rossella Arcucci, r.arcucci@imperial.ac.uk
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 and Radial Basis Functions in Computational Sciences – MESHFREE

Contact: Vaclav Skala, skala@kiv.zcu.cz
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 a 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 2021 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.
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
  • Scattered data interpolation and approximation methods
  • Radial Basis Functions for a mesh morphing and data mapping
  • Meshfŕee methods for corrupted image reconstruction and inpainting removal
  • Meshfree methods applications in general

Multiscale Modelling and Simulation – MMS

Contact: Derek Groen, derek.groen@brunel.ac.uk
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 ecosystems.
The multiscale modelling and simulation (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, kzajac@agh.edu.pl
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.

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

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 9 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, pcardoso@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’21 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 computational science problems.

Software Engineering for Computational Science – SE4Science

Contact: Jeffrey Carver, carver@cs.ua.edu
Description: This is a time of great growth at the intersection of software engineering and computational science, increasingly manifested in the emerging discipline of Research Software. 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.
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 Uncertainty – SPU

Web Address: coming soon
Contact: Vassil Alexandrov, vassil.alexandrov@stfc.ac.uk
Description: Problems with uncertainty need to be tackled in an increasing variety of areas, from physics, chemistry, engineering, computational biology and environmental sciences to decision making in economics and social sciences. Uncertainty is unavoidable in almost all systems analysis, risk analysis, 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, big data analytics and scalable AI, larger and larger problems – often requiring hybrid approaches – have to be tackled in a systematic way at scale. The variety of methods and approaches of solving such problems with uncertainties and quantifying the uncertainties become even more important due to the variety and scale of uncertainties in such problems.
The focus of the workshop will be on methods and algorithms for solving problems with uncertainties, stochastic methods and algorithms for solving problems with uncertainties, methods and algorithms for quantifying uncertainties such as dealing with data input and missing data, sensitivity analysis (local and global), dealing with model inadequacy, model validation and averaging, software fault-tolerance and resilience and more.

Teaching Computational Science – WTCS

Contact: Angela B. Shiflet, shifletab@wofford.edu
Description: The Workshop on Teaching Computational Science (WTCS 2021) 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.

Uncertainty Quantification for Computational Models – UNEQUIvOCAL

Contact: Wouter Edeling, wouter.edeling@cwi.nl
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.