The development of computational models capable of simulating the behaviour and evolution of natural and engineered systems remains a cornerstone of modern science and engineering. Today, this paradigm is undergoing a profound transformation driven by the convergence of High Performance Computing (HPC), data-intensive science, and artificial intelligence.

The rapid emergence of heterogeneous and exascale architectures, combined with advances in GPU computing and distributed workflows, is reshaping how simulations are designed, executed, and integrated with data. Traditional numerical methods for solving differential systems, such as Finite Element Methods (FEM), Finite Difference Methods (FDM), and Particle-In-Cell (PIC) approaches, are increasingly complemented by data-driven and hybrid techniques, including machine learning, physics-informed neural networks (PINNs), surrogate modelling, and evolutionary and swarm-based algorithms. These approaches are enabling new capabilities for tackling multi-scale and multi-physics problems, overcoming computational and data constraints, and supporting the development of digital twins and real-time simulation frameworks.

Within this evolving landscape, the HPCMS Workshop at eScience 2026 aims to bring together a multidisciplinary community of researchers, developers, and practitioners working at the intersection of modelling, simulation, and advanced computing. The workshop will provide a platform to discuss emerging methodologies, software ecosystems, and best practices for scalable, portable, and sustainable computational science.

By fostering dialogue across disciplines - including engineering, physics, life sciences, environmental sciences, and socio-economic systems - the workshop seeks to highlight innovative solutions to complex real-world challenges and to promote the next generation of computational science methodologies.

Topics of interest include, but are not limited to:

• High Performance Computing in computational science: intra-disciplinary and multi-disciplinary research applications

• Integration of HPC and AI for scientific discovery

• Scalable and heterogeneous computing (CPU–GPU, accelerators, exascale systems)

• Data-driven modelling and simulation workflows

• Digital twins and real-time simulation

• energy-efficient and sustainable HPC

• MPI, OpenMP, SYCL, and CUDA applications in Computational Science

• Optimization algorithms and modelling techniques related to optimization in Computational Science

• Implementations of Cellular Automata, Genetic Algorithms, Neural Networks, and Swarm Intelligence

• HPC applications in Quantum Computing (e.g., optimization and scheduling, simulation, etc.)

• Performance models and their integration into the design of efficient parallel algorithms for heterogeneous platforms

• Hardware approaches to HPC in modelling and simulation (e.g., FPGAs, neuromorphic computing, etc.)

• High-performance software designed to address scientific (e.g., biological, physical, social), engineering, medical, and humanities problems