SOURCE-LINKED INTELLIGENCE

AI-based Learning for Physical Simulation

CORDIS · observation · Publication date unknown

ional 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

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recordType

award

status

SIGNED

region

EU

value

1315000

unit

EUR