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Data Driven Computational Mechanics at EXascale

Project description

Inching a step closer to the exascale computing era

The EU-funded DComEX project plans to develop numerical methods enhanced by artificial intelligence as well as a scalable software framework that enables exascale computing. A key innovation of DComEX is the development of AI-Solve, a novel scalable library of AI-enhanced algorithms for solving large-scale sparse linear systems, which are fundamental to computational mechanics. Researchers will 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 offer scientists and engineers unprecedented capabilities for conducting predictive simulations of mechanical systems in applications ranging from bioengineering to manufacturing.

Objective

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.

Coordinator

ETHNICON METSOVION POLYTECHNION
Net EU contribution
€ 465 625,00
Address
HEROON POLYTECHNIOU 9 ZOGRAPHOU CAMPUS
157 80 ATHINA
Greece

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Region
Αττική Aττική Κεντρικός Τομέας Αθηνών
Activity type
Higher or Secondary Education Establishments
Links
Total cost
€ 993 750,00

Participants (4)