Descrizione del progetto
Un passo avanti verso l’era del calcolo su scala exa
Il progetto DComEX, finanziato dall’UE, prevede di sviluppare metodi numerici migliorati dall’intelligenza artificiale nonché un quadro software scalabile che permetta il calcolo su scala exa. Un’innovazione chiave di DComEX è lo sviluppo di AI-Solve, una nuova libreria scalabile di algoritmi potenziati da IA per risolvere sistemi lineari sparsi su larga scala, fondamentali per la meccanica computazionale. I ricercatori fonderanno l’apprendimento automatico vincolato alla fisica con efficienti metodi a blocchi iterativi e incorporeranno dati sperimentali a più livelli di fedeltà per quantificare le incertezze del modello. L’implementazione efficiente di questi metodi nei supercomputer su scala exa offrirà a scienziati e ingegneri capacità senza precedenti per condurre simulazioni predittive di sistemi meccanici in applicazioni che vanno dalla bioingegneria alla produzione.
Obiettivo
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.
Campo scientifico
- 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
Parole chiave
Programma(i)
Meccanismo di finanziamento
RIA - Research and Innovation actionCoordinatore
157 80 ATHINA
Grecia