Periodic Reporting for period 2 - ARIA (Accurate Roms for Industrial Applications)
Reporting period: 2022-12-01 to 2024-11-30
Problem/Issue Being Addressed: The ARIA project aims to address the challenge of accurately modeling nonlinear dynamics of unsteady multi-scale problems and related phenomena. This includes applications in industrial flow control, optimization, and computer-assisted surgery.
Importance for Society: Accurate modeling of these phenomena can lead to significant advancements in various industries, such as greener terrestrial vehicles, more efficient wind farms, and improved medical devices for patient-specific therapies. These advancements have the potential to benefit society by improving environmental sustainability and healthcare outcomes.
Overall Objectives: The overarching objective of the ARIA project is to advance the state-of-the-art in projection-based reduced-order models (ROMs) and enhance data-driven modeling techniques. This includes leveraging ideas from large eddy simulation (LES), manifold learning, solution classification and clustering, and adaptive sampling.
Importance for Society: Accurate modeling of these phenomena can lead to significant advancements in various industries, such as greener terrestrial vehicles, more efficient wind farms, and improved medical devices for patient-specific therapies. These advancements have the potential to benefit society by improving environmental sustainability and healthcare outcomes.
Overall Objectives: The overarching objective of the ARIA project is to advance the state-of-the-art in projection-based reduced-order models (ROMs) and enhance data-driven modeling techniques. This includes leveraging ideas from large eddy simulation (LES), manifold learning, solution classification and clustering, and adaptive sampling.
Work Performed: The project has involved collaborative research between different countries (France, Germany, Italy, Spain, US) and sectors. Participants have exchanged skills and knowledge through secondments and thematic workshops. The project has focused on advancing ROMs and integrating them into multi-fidelity model chains.
Main Results Achieved: The project has led to major advances in understanding the nonlinear dynamics of unsteady multi-scale problems. Participants have developed new skills in data-driven physical modeling and have been exposed to new research environments. The project has also fostered applications in industrial flow control and optimization, and computer-assisted surgery planning.
Main Results Achieved: The project has led to major advances in understanding the nonlinear dynamics of unsteady multi-scale problems. Participants have developed new skills in data-driven physical modeling and have been exposed to new research environments. The project has also fostered applications in industrial flow control and optimization, and computer-assisted surgery planning.
Progress Beyond the State of the Art: The ARIA project has made significant progress in advancing ROMs and enhancing data-driven modeling techniques. This includes the development of new approaches to projection-based ROMs and the integration of ROMs into multi-fidelity model chains.
Expected Results: The project is expected to lead to further advancements in modeling the nonlinear dynamics of complex phenomena, including biological systems and financial systems. The results have the potential to impact various industries and contribute to societal benefits, such as improved environmental sustainability and healthcare outcomes.
Potential Impacts: The project has the potential to create market opportunities for non-academic participants, such as greener terrestrial vehicles, more efficient wind farms, revolutionary cost-efficient prediction software, and decision-making support tools for diagnostic and prognostic of vascular diseases, with a significant benefit for European society.
Expected Results: The project is expected to lead to further advancements in modeling the nonlinear dynamics of complex phenomena, including biological systems and financial systems. The results have the potential to impact various industries and contribute to societal benefits, such as improved environmental sustainability and healthcare outcomes.
Potential Impacts: The project has the potential to create market opportunities for non-academic participants, such as greener terrestrial vehicles, more efficient wind farms, revolutionary cost-efficient prediction software, and decision-making support tools for diagnostic and prognostic of vascular diseases, with a significant benefit for European society.