Workshops

Web Address: coming soon.

Contact: Takashi Shimokawabe, The University of Tokyo, Japan, email

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 sixteenth 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:

• Numerical methods for computational fluid dynamics (CFD) and continuum mechanics as a basis for simulations in atmospheric science, ocean science, solid earth science, space & planetary science, and other earth sciences.
• 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 or GPU accelerators.
• 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.

Web Address: http://home.agh.edu.pl/iacs

Contact: Maciej Paszynski, AGH University of Krakow, Poland, email

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).
We invite papers oriented toward the applications of AI and HPC in advanced simulations of phenomena often governed by either of the following:

• Partial Differential Equations (PDEs): linear, non-linear, stationary, and time-dependent.
• Complex systems consisting of very large collections of interacting individual elements. These systems may include molecules in a material, cells in the human body, interacting species in an ecosystem, and individuals transmitting an infectious disease within a group.

Likewise, we also encourage papers focused on applications and analysis of such advanced simulation methods, including the development of advanced inversion methods. The topics of this workshop include, but are not limited, to the following::

• artificial intelligence including soft computing for simulation and inversion of PDEs or/and complex systems
• efficient adaptive algorithms for large problems
• low computational cost adaptive solvers
• artificial intelligence in Isogeometric Analysis and Petrov-Galerkin methods
• model reduction techniques for large problems
• memetic algorithm
• multi-agent systems
• supermodeling techniques
• advanced parallelization techniques
• high-performance computing
• computational and mathematical analysis of advanced simulation methods
• advanced methods applied to inverse problems
• applications of advanced simulation methods

Web Address: https://sites.google.com/unicz.it/aixna/

Contact: Marianna Milano, University Magna Graecia of Catanzaro, Italy, email

Description: The AI4NetBio Workshop – Artificial Intelligence for Network Analysis in Biology and Beyond aims to bring together researchers and practitioners working at the intersection of AI methods and network analysis, with a special focus on bioinformatics and biomedical applications.
We welcome contributions that present novel algorithms, models, and applications of AI to understand, model, and analyze complex networks across multiple domains. Both theoretical advances and practical implementations are encouraged.
Topics of interest include, but are not limited to:

• AI Methods for Network Analysis:
  Machine and deep learning for network data; graph neural networks (GNNs); reinforcement learning in dynamic networks; probabilistic and evolutionary models; uncertainty quantification.
• Network Analysis in Bioinformatics:
  Modeling of protein–protein interaction, gene regulatory and metabolic networks; disease–gene association studies; network-based approaches for drug discovery and repurposing.
• Network Geometry & Representation Learning:
  Hyperbolic embeddings; geometric deep learning; applications in biological and social networks.
• Complex Network Theory and Applications:
  Community detection, link prediction, influence propagation; dynamics on and of networks (diffusion, epidemics); multilayer and temporal networks.
• Scalable AI for Large Networks:
  Parallel and distributed AI techniques; handling high-dimensional, sparse, or noisy data.
• Applications in Real-World Domains:
  Bioinformatics, neuroscience, and healthcare; network-based AI in infrastructure, communication, and transportation systems; AI for social media and recommendation networks.

Why attend?
Network science has become a cornerstone for studying complex biological, biomedical, and clinical systems. Combined with AI, it enables integrative modeling from genomics to connectomics, supports drug repurposing, and enhances computational medicine through interpretable and causal machine learning.
The workshop aims to foster interdisciplinary dialogue between the communities of AI in healthcare and network/pathway analysis, promoting transparency, trust, and innovation in computational biology and medicine.
We invite the submission of original research papers, case studies, and tool presentations on these topics.

Web Address: https://sites.google.com/view/comp-health-iccs/

Contact: Sergey Kovalchuk, Independent Researcher, Russian Federation, email

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. Key topics include (but not limited to):

• Simulation and modeling in healthcare and medicine (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

Web Address: Coming soon.

Contact: Tanzhe Tang, University of Amsterdam, The Netherlands, email

Description: As a successor to its first edition held in Singapore, the 2nd Computational Modeling and Artificial Intelligence for Social Systems (CMAISS) workshop aims to bring together researchers and practitioners working on the development and application of computational methods for studying various social systems, such as cities, traffic, cooperation, collective decision-making, opinion dynamics, and social contagion. These systems are typically stochastic, complex, and often out of equilibrium, requiring innovative methodologies and interdisciplinary perspectives to study. Computational modeling techniques such as agent-based modeling and complex network modeling have proven to be exceptionally powerful for this task, but also face challenges such as validation. Meanwhile, the advances in Artificial Intelligence have attracted growing attention from scholars seeking either to employ AI techniques to study social systems, or to study the social systems in the era of AI. CMAISS seeks to explore opportunities for methodological innovation in computational modeling, AI, and their integration, by fostering interdisciplinary discussions among scientists of network science, game theory, agent-based modeling, machine learning, and large language models. We welcome papers on, but not limited to, the following topics:

• Integration of AI in social system modeling
• Social systems in the era of AI
• Social simulation, multi-agent simulation or agent-based modeling applied to social systems
• Using machine learning and large language models in social simulation methods
• Complex network modeling and analysis
• Modeling evolution of cooperation in social systems
• Modeling opinion dynamics and misinformation
• Modeling collective decision making, aggregation and deliberation

Web Address: http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=190059

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

Web Address: https://ComPsy.computationalscience.nl

Contact:
Valeria Epelbaum, University of Amsterdam, The Netherlands, email
Sophie Engels, Northeastern University, USA, email

Description: The workshop aims to advance interdisciplinary research, leveraging computational methods to understand the mechanisms underlying psychological phenomena, predict and improve mental health. It focuses on the development and application of data-driven and knowledge-driven multiscale models, agent-based simulations, and network approaches for psychological dynamics and urban mental health. By integrating computational models and advanced AI techniques, the workshop fosters novel strategies for modelling symptom networks and the influence of urbanity and social connections on mental health. Uncertainty quantification and digital phenotyping are also emphasized to improve prediction accuracy and effective intervention policies.

Web Address: https://www.eecis.udel.edu/~uschill/cdmdd/

Contact: Ulf Schiller, University of Delaware, USA, email

Description: Computing, advanced data analysis, and machine learning are transforming materials discovery and design for engineering and manufacturing applications. An enabling factor of this success is the increased performance of simulation and machine learning methods that can be achieved on modern supercomputing systems. However, further advances in computational science are needed to fully exploit the potential of computing and data. Materials design requires to solve the inverse problem of identifying product formulations and processes that meet desired properties and performance characteristics. The ultimate goal is to create cyber-physical systems for materials discovery that combine computation and physical experiments by implementing an autonomous feedback loop of design, characterization, and optimization. Tackling this grand challenge requires a combination of physics-based simulation and data-driven approaches including machine learning and AI. For example, there is a need for approaches that extend algorithmic predictions beyond the bounds of the known feature space to enable extrapolation rather than interpolation. Such approaches need to reconcile probability and uncertainty of real-world or synthetic data with the fundamental laws of physics in order to establish reliable models and predictions.
The CDMDD workshop brings together experts from materials science, physics, chemistry, computer science, data science, and artificial intelligence. It provides a forum to synergize interdisciplinary perspectives and accelerate the advancement of computing and data-driven materials design in research and industry.

Web Address: https://mms.computationalscience.nl/

Contact: Diana Suleimenova, Brunel University London, United Kingdom, email

Description: Credible 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. Moreover, 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. This includes their execution on advanced computational resources, their validation against experimental data and more widely, the efforts required to make these approaches credible for real-world use.

Web Address: https://sites.google.com/view/rossella-arcucci/home/8th-edition-machine-learning-and-data-assimilation-for-dynamical-systems

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.

Web Address: https://iccs.comet.edu.pl/

Contact: Wojciech Sałabun, National Institute of Telecommunications, Poland, email

Description: The workshop “Multi-Criteria Decision-Making: Methods, Applications, and Innovations (MCDM 2026)” will be a part of the Workshops on Computational Science (WCS 2026), co-organized with the International Conference on Computational Science (ICCS 2026). This workshop will focus on advancing the field of Multi-Criteria Decision-Making (MCDM) by bringing together researchers and practitioners to discuss new methodologies, innovative applications, and interdisciplinary approaches.
The scope of the workshop includes, but is not limited to, the following topics:

1. Development and application of MCDM methods (AHP, TOPSIS, RANCOM, PROMETHEE, ELECTRE, COMET, SPOTIS etc.)
2. Temporal decision-making approaches
3. Integration of MCDM with machine learning and artificial intelligence
4. Dealing with uncertainty: fuzzy, stochastic, and interval-based approaches
5. Sustainable decision-making and environmental applications
6. MCDM in the context of Big Data and large-scale problems
7. Multi-objective optimization and trade-off analysis
8. Weight determination and aggregation techniques
9. Group decision-making and consensus models
10. Real-world case studies in engineering, healthcare, logistics, and more
11. Hybrid MCDM methods and their applications
12. Tools, software, and platforms supporting MCDM processes
13. Other similar topics

Web Address: TBA

Contact: Yani Xue, Brunel University London, UK, email

Description: The workshop on “Navigating Trustworthy and Autonomous Modelling of Complex Systems: AI Meets Computational Science (TAMCS)” addresses the emerging need for reliable, transparent, and adaptive modelling frameworks for complex systems. With the growing integration of AI into simulation of energy networks, transportation systems, climate and environmental systems, new computational challenges arise around model credibility, autonomous decision-making, and scalable performance.
This workshop aims to bring together researchers from AI, optimisation, computational science, and engineering to discuss how to enhance the accuracy, efficiency, and robustness of complex-system simulations while upholding trustworthiness and autonomy. We invite submissions on advances, methods, and applications related to trustworthiness, autonomy, and computation in complex-system simulation, including but not limited to:

1. Trustworthy AI for simulations, focusing on scalable and high-dimensional uncertainty quantification, model verification, and explainability to ensure reliable and interpretable AI-assisted frameworks.
2. Agentic AI for large-scale, multi-agent simulations, from practical use cases such as AI-assisted simulation setup and automated data preparation, to advanced methods for autonomous goal-setting, adaptive multi-objective decision-making, and self-reflective learning.
3. Scalable, high-fidelity simulations with multi-objective optimisation and network methods (including network dynamics) to enhance adaptive, resilient, and robust behaviour in complex systems.
4. Applications and case studies demonstrating AI-driven simulations that improve predictive accuracy, computational efficiency, and system robustness.

Web Address: https://sites.google.com/view/naca-2026/strona-g%C5%82%C3%B3wna

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.

Web Address: Coming soon.

Contact: Shuyu Sun, Tongji University, China, email

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 14 years since 2011 within the International Conference on Computational Science (ICCS). The aim of this symposium 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 describing the recent advances in mathematical modeling, computer simulation, numerical analysis, and other computational aspects of flow and transport phenomena of flow and transport.
Potential topics include, but are not limited to:

1. advanced numerical methods for the simulation of subsurface and surface flow and transport, and associated aspects such as discretization, gridding, upscaling, multiscale algorithms, optimization, data assimilation, uncertainty assessment, and high performance parallel and grid computing;
2. spatial discretization schemes based on advanced finite element, finite volume, and finite different methods; schemes that preserve local mass conservation (such as mixed finite element methods and discontinuous Galerkin methods) are of particular interest;
3. decomposition methods for improved efficiency and accuracy in treating flow and transport problems; decomposition methods for nonlinear differential equations and dynamical systems arising in flow and transport; temporal discretization schemes for flow and transport;
4. a-priori and a-posteriori error estimates in discretizations and decompositions; numerical convergence study; adaptive algorithms and implementation;
5. modeling and simulation of single-phase and multi-phase flow in porous media or in free space, and its applications to earth sciences and engineering;
6. modeling and simulation of subsurface and surface transport and geochemistry, and its application to environmental sciences and engineering;
7. computational thermodynamics of fluids, especially hydrocarbon and other oil reservoir fluids, and its interaction with flow and transport;
8. computational modeling of flow and transport in other fields, such as geological flow/transport in crust and mantle, material flow in supply chain networks, separation processes in chemical engineering, information flow, biotransport, and intracellular protein trafficking, will also be considered.

Web Address: https://smartsys.ualg.pt/2026/

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.

Web Address: https://www.hartree.stfc.ac.uk/events/solving-problems-with-uncertainty/

Contact: Vassil Alexandrov, Hartree Centre – STFC, United Kingdom, email

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 is especially important when tackling complex systems.
With exascale computing now being a reality, and the advances of big data analytics and scalable AI, larger and larger problems – often requiring hybrid approaches – have to be solved in a systematic way at scale. The problem of solving such problems with uncertainties and quantifying the uncertainties becomes even more important, especially in the case of complex systems, 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, AI and hybrid approaches dealing 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.

Web Address: https://www.hartree.stfc.ac.uk/events/teaching-computational-science-workshop-2/

Contact: Evguenia Alexandrova, Hartree Centre – STFC, United Kingdom, email

Description: In the context of rapidly growing applications off AI and Quantum Computing and the entrance of the exascale computing systems in the technological landscape, computational scientists and research technical personal are faced with the pressure to rapidly update their skills in order to benefit from the novel developments of technology. The challenge in front of educators from academia and training is to assess which are the core competences and how to expose the community to them in the most efficient way.
The focus of this workshop is through innovation 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 to support the researchers in their career development through upskilling. 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 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 workforce, and flexible learning, how complex technological and research topics can be communicated effectively to diverse audiences to encourage upskilling and their adoption.