This result brings together advanced HPC simulation services with customizable workflow templates, developed within the CAELESTIS project, to revolutionize scientific computing and industrial research. The solution simplifies the deployment and management of parallel simulations on high-performance computing infrastructures, empowering users to efficiently run complex scientific algorithms without needing specialist HPC expertise. Leveraging open-source technologies, it integrates modular templates that can be adapted to diverse research domains, supporting scalable, reproducible, and user-friendly workflows. The core services are built on PyCOMPSs, with compatibility for established simulators such as Alya, PAM-RTM, PAM-DISTORSION, and Twinkle. By fostering interoperability and reducing technical barriers, our combined platform accelerates the development, testing, and optimization of innovative AI-driven processes in advanced manufacturing, materials science, and beyond. The approach is ideally suited for RTOs and industry partners aiming to enhance R&D capabilities, while promoting further development under Horizon Europe missions for digitalisation and process industries.
Addressing target audiences and expressing needs
- Business partners – SMEs, Entrepreneurs, Large Corporations
- Technology Transfer Expertise
- Business plan development
We are actively seeking pilot users for our workflow templates, as well as collaborators interested in R&D scale-up. Feedback from users on the usability and deployment of our solutions in diverse scientific and industrial domains is particularly valuable to us.
- Public or private funding institutions
- Other Actors who can help us fulfil our market potential
- Research and Technology Organisations
R&D, Technology and Innovation aspects
Currently HPC simulation service and workflow templates validated in pilot research and made available under an open-source license. Next steps include expanding deployment to new partners, refining functionalities based on feedback, and scaling up for adoption in manufacture and AI-driven industry. Future development will focus on integrating additional simulators and enhancing interoperability.
