Workshops

ICCS is co-organised with the Workshops on Computational Science (WCS), a set of thematic workshops organized by experts in a particular area of Computational Science. These workshops are intended to provide a forum for the discussion of novel and more focused topics in the field of Computational Science among an international group of researchers.

If you are interested in organizing a workshop at ICCS 2025, you can find all necessary details on the Call for Workshops webpage.

The list of accepted workshops is available below. Please click through for each workshop’s scope, dedicated web address, and chair contact details.
We will be adding many more workshops over the coming weeks.

  1. Computational Optimization, Modelling and Simulation – COMS
  2. Machine Learning and Data Assimilation for Dynamical Systems – MLDADS
  3. Numerical Algorithms and Computer Arithmetic for Computational Science – NACA
  4. Smart Systems: Bringing Together Computer Vision, Sensor Networks and Artificial Intelligence – SmartSys

Computational Optimization, Modelling and Simulation – COMS

Contact: Xin-She Yang, Middlesex University London, United Kingdom, email
Description: The 17th workshop “Computational Optimization, Modelling and Simulation (COMS 2026)” will be a part of the International Conference on Computational Science (ICCS 2026) and Workshops on Computational Science (WCS 2026). This will be the 17th 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, Netherlands, Poland, UK, Czech, Spain and Singapore. COMS 2026 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.
COMS2026 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

Machine Learning and Data Assimilation for Dynamical Systems – MLDADS

Contact: Rossella Arcucci, Imperial College London, United Kingdom, email
Description: The primary aim of this workshop is to convene researchers from data assimilation, machine learning and dynamical systems to bridge the gaps between these fields. By exploring how these complementary disciplines can accelerate research outcomes and impact, the workshop seeks to foster collaboration, share cutting-edge advancements, and tackle the computational challenges that have limited the application of data assimilation and fusion in high-dimensional complex systems as well as the application of machine learning for dynamical systems.
The MLDADS workshop brings together contributions from the fields data assimilation, machine learning and dynamical systems to fill the gap between these theories in the following directions:
  1. Machine Learning for Dynamical Systems: how to analyse dynamical systems on the basis of observed data rather than attempt to study them analytically; explore the role of generative models.
  2. 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.
  3. Machine Learning for Data Assimilation: how machine learning techniques—such as deep learning, reinforcement learning, and transfer learning—can improve the efficiency and scalability of data assimilation in dynamical system modelling.
  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.

Numerical Algorithms and Computer Arithmetic for Computational Science – NACA

Contact: Pawel Gepner, Warsaw Technical University, Poland, email
Description: The Workshop on Numerical Algorithms and Computer Arithmetic for Computational Science 2026 (NACA 2026) welcomes submissions that showcase advancements in numerical algorithms and computer arithmetic across various fields of computational science. This includes modelling and simulation, as well as high-performance computing and data-intensive algorithms for scientific applications. While submissions can explore a wide array of topics, the following areas are particularly of interests:
  • Foundations of computer arithmetic: emerging number systems and their applications
  • Novel arithmetic algorithms, their analysis, and applications
  • Efficient, high-performance, novel implementations of computer arithmetic in software and hardware
  • Integer or floating-point operations, elementary and special functions, multiple-precision computing, interval arithmetic
  • New algorithms and properties of floating-point arithmetic in emerging domains and applications
Computer arithmetic has always been at the core of the digital age and is currently driving innovation in domains such as artificial intelligence, high-performance computing, signal processing and security. Scientists across diverse fields heavily rely on numerical algorithms that require computer arithmetic awareness to avoid oversolving. There is a specific demand for scalable tools that are highly efficient and achieve high user-productivity while solving large-scale problems on massively parallel systems. This workshop focuses on numerical algorithms and computer arithmetic, giving special attention to the latest scientific trends and challenges related to implementing numerical software libraries. The objective is to bring together researchers from different institutes, enabling the exchange of experiences and fostering research collaborations.

Smart Systems: Bringing Together Computer Vision, Sensor Networks and Artificial Intelligence – SmartSys

Contact: Pedro J. S. Cardoso, University of Algarve & NOVA LINCS, Portugal, email
Description: Smart Systems incorporate sensing, actuation, and intelligent control to analyze, describe, and resolve situations, making decisions based on available data in a predictive or adaptive manner. Designed for computer scientists, mathematicians, and researchers from diverse application areas, SmartSys’26 – 8th edition – brings together pioneering computational methods from distinct research fields including space, physics, chemistry, life sciences, economics, security, engineering, and arts.
This workshop integrates computer vision, sensor networks, artificial intelligence, and data science to solve computational science problems. The workshop also welcomes contributions from related areas such as affective computing, augmented reality, human-computer interaction, user experience, Internet of Things/Everything, energy management systems, smart grids, operational research, evolutionary computation, time-series analysis, and information systems. All submissions must focus on computational science challenges, using smart systems as modeling, simulation, and optimization tools.