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 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: Coming soon.

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://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://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.