Workshops

Web Address: https://www.cspp.cc.u-tokyo.ac.jp/IHPCES2025/

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 fifteenth 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: https://sites.google.com/unicz.it/aixna/

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

Description: The “Artificial Intelligence Approaches for Network Analysis” workshop aims to bring together researchers and practitioners working on various aspects of network analysis, with a particular focus on the intersection of AI methods and network-based problems in bioinformatics and other relevant fields. The workshop will cover a wide range of topics related to AI-driven network analysis, including both theoretical advancements and practical applications. We encourage submissions that address novel methods, models, and applications of Artificial Intelligence in understanding and analyzing complex networks, with particular interest in the following areas:

1. AI Methods in Network Analysis: Machine learning and deep learning approaches for network data.; Graph neural networks and their applications in large-scale network analysis; Reinforcement learning in dynamic and adaptive networks; Probabilistic models and uncertainty quantification in network data; Evolutionary algorithms for network optimization problems.
2. Network Analysis in Bioinformatics: AI approaches for biological network modeling and analysis; Protein-protein interaction networks; Gene regulatory and metabolic networks; Network-based approaches for drug discovery and repurposing; Disease-gene association studies using network methods.
3. Network Geometry: Geometrical approaches to network structure and dynamics; Hyperbolic embeddings and their role in network inference; Geometric deep learning applied to network data; Applications of network geometry in biological and social networks.
4. Complex Network Theory and Applications: AI techniques for the analysis of social, biological, and technological networks; Community detection, influence propagation, and link prediction. • Dynamics on and of networks, including diffusion and epidemics; Multilayer and temporal network analysis.
5. Scalable AI for Network Analysis: Efficient AI algorithms for large-scale and high-dimensional network data; Parallel and distributed AI techniques for network computation; Handling sparse, noisy, and incomplete network data.
6. Applications in Real-World Domains: Applications of AI in bioinformatics, neuroscience, healthcare, and epidemiology; Network-based AI solutions in infrastructure, communication, and transportation systems; AI for social media, recommendation systems, and e-commerce networks.

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/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: http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=182799

Contact: Xin-She Yang, Middlesex University London, United Kingdom, email

Description: The 16th workshop “Computational Optimization, Modelling and Simulation (COMS 2025)” will be a part of the Workshops on Computational Science (WCS 2025), which are co-organized with the International Conference on Computational Science (ICCS 2025). This will be the 16th 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 and Spain. COMS 2025 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.
COMS2025 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://sites.google.com/view/rossella-arcucci/home/7th-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://mms.computationalscience.nl/

Contact: Derek Groen, 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. 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. 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/naca2025/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 2025 (NACA 2025) 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, King Abdullah University of Science and Technology (KAUST), Saudi Arabia, 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 13 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. (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/2025/

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’25 – 7th 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 augmented reality, human-computer interaction, user experience, Internet of Things/Everything, energy management systems, smart grids, vehicle and person tracking systems, 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.