Descripción del proyecto
Un paso más hacia la era de la computación a exaescala
El objetivo del proyecto DComEX, financiado con fondos europeos, es desarrollar métodos matemáticos mejorados por la inteligencia artificial (IA, o AI por sus siglas en inglés), así como un marco de «software» escalable que permita la computación a exaescala. Una de las principales innovaciones de DComEX es la creación de AI-Solve, una nueva biblioteca escalable de algoritmos mejorados por IA para resolver sistemas lineales dispersos a gran escala, que son fundamentales para la mecánica computacional. Los investigadores del proyecto combinarán el aprendizaje automático basado en la física con métodos eficaces de iteración de bloques e incorporarán datos experimentales en múltiples niveles de fidelidad para cuantificar las incertidumbres del modelo. La implementación eficaz de estos métodos en superordenadores a exaescala proporcionará a los científicos e ingenieros capacidades sin parangón para realizar simulaciones predictivas de sistemas mecánicos en aplicaciones que van desde la bioingeniería a la fabricación.
Objetivo
DCoMEX aims to provide unprecedented advances to the field of Computational Mechanics by developing novel numerical methods enhanced by Artificial Intelligence, along with a scalable software framework that enables exascale computing. A key innovation of our project is the development of AI-Solve, a novel scalable library of AI-enhanced algorithms for the solution of large scale sparse linear system that are the core of computational mechanics. Our methods fuse physics-constrained machine learning with efficient block-iterative methods and incorporate experimental data at multiple levels of fidelity to quantify model uncertainties. Efficient deployment of these methods in exascale supercomputers will provide scientists and engineers with unprecedented capabilities for predictive simulations of mechanical systems in applications ranging from bioengineering to manufacturing. DCoMEX exploits the computational power of modern exascale architectures, to provide a robust and user friendly framework that can be adopted in many applications. This framework is comprised of AI-Solve library integrated in two complementary computational mechanics HPC libraries. The first is a general-purpose multiphysics engine and the second a Bayesian uncertainty quantification and optimisation platform. We will demonstrate DCoMEX potential by detailed simulations in two case studies: (i) patient-specific optimization of cancer immunotherapy treatment, and (ii) design of advanced composite materials and structures at multiple scales. We envision that software and methods developed in this project can be further customized and also facilitate developments in critical European industrial sectors like medicine, infrastructure, materials, automotive and aeronautics design.
Ámbito científico
- natural sciencescomputer and information sciencesartificial intelligence
- natural sciencescomputer and information sciencessoftware
- natural sciencescomputer and information sciencescomputational science
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringcomputer hardwaresupercomputers
- natural sciencesmathematicsapplied mathematicsnumerical analysis
Palabras clave
Programa(s)
Régimen de financiación
RIA - Research and Innovation actionCoordinador
157 80 ATHINA
Grecia