The development of models that allow computers to simulate the evolution of artificial and natural systems is central to advancing knowledge across scientific and engineering disciplines.

In recent decades, growing computational power has greatly expanded the scope of such methodologies, enabling their application in research, industry, and the quantitative study of complex systems. This progress has fostered both the extensive use of numerical methods for solving differential equation systems (e.g., FEM, FDM, PIC) and the exploration of alternative paradigms such as Cellular Automata, Genetic Algorithms, Neural Networks, and Swarm Intelligence. These complementary approaches have proven particularly effective when conventional simulation techniques are constrained by spatial or temporal limitations, opening new possibilities for modelling complex phenomena. 

Now in its twelfth edition at PDP, the HPCMS Workshop aims to serve as a platform for a multidisciplinary community of scholars, researchers, developers, educators, practitioners, and experts from leading universities, institutions, agencies, and companies in Computational Science. Its mission is to foster the exchange of perspectives on trends, challenges, and state-of-the-art advances in computational problems and high-performance computing, with applications spanning engineering, physics, chemistry, biology, geology, medicine, ecology, sociology, traffic control, economics, and beyond.

Topics of interest include, but are not limited to, the following:

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

- Complex systems modelling and simulation

- Cellular Automata, Genetic Algorithms, Neural networks, Swarm Intelligence implementations

- Integrated approach to optimization and simulation

- MPI, OpenMP, Sycl and CUDA applications in Computational Science

- Optimization algorithms, modelling techniques related to optimization in Computational Science

- High-performance Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems

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

- Hardware approaches (e.g., FPGAs, Neuromorphic computing, etc) of high performance computing in modeling and simulation

- HPC Applications in Quantum Computing (e.g., Optimization and Scheduling, Simulation, etc)