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/bbc-2025

Contact: Mario Cannataro, University “Magna Graecia” of Catanzaro, Italy, email

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; (vi) Artificial Intelligence in bioinformatics and medicine, with special focus on main challenges such as explainability, bias, ethics, dataset shift.

Web Address: Coming soon.

Contact: Andres Iglesias, University of Cantabria, Spain, email

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 started under the umbrella of the Horizon 2020 European project PDE-GIR, but it has always been opened to all researchers and practitioners 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 and 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
• 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
• 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.

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: The Computational Modeling and Artificial Intelligence for Social Systems (CMAISS) workshop aims to bring together researchers and practitioners working on the application of computational methods to the investigation of various social systems, such as cities, traffic, cooperation, collective decision making, opinion dynamics, social contagion, among others. CMAISS welcomes papers that explore innovative methodologies and interdisciplinary perspectives, with a particular interest in the integration of artificial intelligence to computational modeling and simulation of social systems. The workshop will foster discussion on methodological innovations that can enhance our understanding of social dynamics by bringing together scientists of network science, game theory, agent-based modeling, machine learning and large language models. Potential topics include, but are not limited to:

• Integration of AI in social system modeling
• Social simulation, multi-agent simulation or agent-based modeling
• Using machine learning and large language models in social simulation methods
• Social network analysis
• Evolution of cooperation in social systems
• Opinion dynamics and misinformation
• Collective decision making, aggregation and deliberation

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: Coming soon.

Contact: Ilkay Altintas, University of California, San Diego, USA, email

Description: This workshop explores innovative approaches to solving pressing societal challenges using the combined power of computational science and artificial intelligence. Participants will discuss real-world case studies and methodologies that emphasize equitable solutions, ensuring that advancements in technology and science benefit all communities fairly.
Key topics include:

• Data-Driven Decision Making: Harnessing large-scale data for actionable insights in areas like climate change, public health, and social justice
• AI for Equity: Designing AI systems that prioritize fairness, inclusivity, and ethical considerations
• Computational Modeling: Developing dynamic models to address complex, interconnected societal systems
• Convergence Research: Methodologies for bridging disciplines to identify needs and create sustainable and scalable integrated solutions for multifaceted societal problems
• Collaborative Innovation: Strategies for interdisciplinary collaboration between technologists, policymakers, and community stakeholders

Through interactive short and long paper presentations, invited talks, and expert-led panels, attendees will form a community to drive positive, sustainable, and equitable outcomes in their fields. Attendees are expected to include cross-disciplinary cross-sector experts in communities of research and practice.

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: Coming soon.

Contact: Wei Li, National University of Singapore, Singapore, email

Description: This workshop focuses on the pivotal roles of large language models (LLMs) and intelligent decision-making in addressing the challenges of the digital economy. LLMs, powered by advanced machine learning and natural language processing, excel in processing and analyzing vast unstructured data, enhancing capabilities in market forecasting, resource optimization, and risk management. When combined with computational science, LLMs and intelligent decision-making offer innovative and efficient solutions to tackle complex economic systems.
Key topics include applications of LLMs in financial forecasting and market dynamics, risk assessment and management in complex systems, intelligent resource allocation in the digital economy, and building LLM-based frameworks for economic analysis and decision-making. This workshop aims to explore how LLMs and intelligent decision-making work together to solve complex problems, providing fresh insights and methodologies for academia 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: Coming soon.

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

Description: The workshop “Multi-Criteria Decision-Making: Methods, Applications, and Innovations (MCDM 2025)” will be a part of the Workshops on Computational Science (WCS 2025), co-organized with the International Conference on Computational Science (ICCS 2025). This inaugural 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 workshop aims to foster collaboration and knowledge exchange within the international MCDM community. The accepted papers will be published in Springer’s LNCS Series.
MCDM 2025 will focus on cutting-edge research, trends, and challenges in MCDM, including temporal decision-making, data-driven approaches, and the integration of decision-support systems with computational science. The workshop will highlight novel algorithms, innovative frameworks, and real-world applications in various domains such as engineering, economics, sustainability, and artificial intelligence.
Topics include (but are not limited to):

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

MCDM 2025 aims to bridge the gap between theoretical advancements and practical implementations, providing a unique opportunity for participants to share insights and explore future directions in the field.

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: http://qcw2025.agh.edu.pl/

Contact: Katarzyna Rycerz, AGH University of Krakow, Poland, email

Description: Quantum computing is a paradigm that exploits the fundamental principles of quantum mechanics to solve problems in various fields of science that are beyond the possibilities of classical computing infrastructures. Despite the 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 applications of quantum informatics to the field of computational science.

Web Address: http://home.agh.edu.pl/~rag

Contact: Aleksander Smywiński-Pohl, AGH University of Krakow, Poland, email

Description: Retrieval-Augmented Generation (RAG) has emerged as a critical and rapidly evolving theme in the fields of Artificial Intelligence (AI) and Natural Language Processing (NLP). RAG systems address a significant limitation of Large Language Models (LLMs): their inability to incorporate real-time updates and private knowledge efficiently, as their knowledge is static and embedded within the model’s weights, making updates costly and time-intensive. By integrating traditional and modern information retrieval (IR) techniques with the generative capabilities of LLMs, RAG systems offer a dynamic solution that allows models to access and utilize the most up-to-date and domain-specific information. This combination makes RAG a promising approach for building systems that are not only powerful but also accurate, efficient, and adaptable to specialized contexts.

This workshop aims to bring together researchers and practitioners working on RAG systems, particularly those focused on complex and high-stakes domains such as law, biology, physics, and medicine, where accuracy and reliability are paramount. Our goal is to foster discussions, share novel approaches, and identify emerging challenges in the development and application of RAG systems.

Topics of Interest
We invite high-quality submissions addressing various aspects of RAG systems, including but not limited to:
1. Core Improvements and Architectures
– Innovations in information retrieval models.
– New architectures and frameworks for RAG systems.
– Enhanced techniques for vector representation and storage.
2. Efficiency and Scalability
– Methods to improve processing speed and reduce memory consumption in RAG pipelines.
– Quantization techniques for efficient vector storage and retrieval.
– Scalable solutions for large-scale domain-specific datasets.
3. Domain-Specific Applications
– Development of RAG systems tailored for specialized fields such as legal, biomedical, and scientific domains.
– Strategies for adapting LLMs to retrieval tasks in niche contexts.
4. Fusion and Retrieval Optimization
– Models for combining results from diverse retrieval systems.
– Novel fusion techniques to enhance relevance and accuracy.
5. Evaluation and Robustness
– Creation of new evaluation datasets and benchmarks.
– Metrics and methodologies for assessing RAG performance.
– Techniques for controlling hallucinations in generated outputs.
6. Broader Applications and Challenges
– Ethical considerations and biases in RAG systems.
– Cross-lingual or multilingual RAG applications.
– Use of RAG systems in low-resource or under-represented domains.

Who Should Attend?
This workshop is designed for researchers, industry professionals, and students interested in the intersection of retrieval systems and generative models. Whether your focus is on improving foundational technologies, developing novel applications, or tackling real-world challenges in specialized domains, we invite you to join us in advancing the field of RAG.

We look forward to your contributions and to stimulating discussions that will shape the future of Retrieval-Augmented Generation!

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.

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

Contact: Vassil AlexandrovSTFC Hartree Centre, 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, 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, STFC Hartree Centre, 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 equally rapidly to update their skills in order to benefit from the novel developments in 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.