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ALPS

Financiado

AI-based Learning for Physical Simulation

Computational physical modeling is a key resource to complement theoretical and experimental methods in modern scientific research and engineering. While access to large amount of data has favored the use of Artificial Intelligenc... ver más

31/08/2027

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

AARHUS UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 2 Participantes

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-05-31

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

ERC-2021-STG: ERC STARTING GRANTS

I+D

Cerrada hace 3 años

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2 Participantes

1.29M€ | Lider

SSSA

28.04K€ | Participante

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Últimas noticias

29-11-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

29-11-2024: ECE En las últimas 48 horas el Organismo ECE ha otorgado 2 concesiones

29-11-2024: CMVAMADRID En las últimas 48 horas el Organismo CMVAMADRID ha otorgado 37 concesiones

Duración del proyecto: 63 meses Fecha Inicio: 2022-05-31
Fecha Fin: 2027-08-31

Líder del proyecto

AARHUS UNIVERSITET

TRL 4-5

Presupuesto del proyecto 1M€

Descripción del proyecto Computational physical modeling is a key resource to complement theoretical and experimental methods in modern scientific research and engineering. While access to large amount of data has favored the use of Artificial Intelligence and Machine Learning (ML) techniques to enhance physical simulations, limitations of purely data-driven methods have emerged as concerns their generalization capability and their intelligibility. In particular, the latter feature promotes understanding, a fundamental driver for scientific and technical progress, and possibly allows to rigorously investigate the reliability of the models and the safety of the systems based on those models. To overcome these limitations, I propose a hybrid approach that originally combines ML methods and equation-based modeling to significantly improve generalization in small-data scenarios, while guaranteeing the intelligibility of the physical models through the use of symbolic representations. Core of the methodology are learning algorithms that reconstruct models with controllable complexity from data by consistently combining building blocks, which derive from a unifying mathematical framework for physical theories. The system will also incorporate novel human-inspired strategies for knowledge distillation, accumulation and reuse, which are missing in state-of-the-art physical model learning algorithms. To efficiently handle the computational cost associated with the proposed methods, I will implement them in a new software platform that seamlessly integrates automated model learning and high-performance simulation. Thanks to their general-purpose nature, the methods and algorithms developed in this project may be employed in all scientific disciplines and in engineering workflows. In particular, I plan to use them to advance biology and soft robotics by solving challenging modeling tasks.

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