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Autonomous learning agents in Physics

CORDIS · observation · Publication date unknown

Autonomous learning agents in Physics In recent years, the use of machine learning (ML) for the study of physics has experienced a strong boost. However, most of the machines used are black boxes, and the causal relation between inputs and outputs is often impossible to extract. Nonetheless, a critical aspect when dealing with physical systems is not only to make correct predictions, but to understand the physical laws which underlie these assessments. Recently, an increasing number of works aim at developing interpretable ML methods, from which such hidden laws can be extracted. However, their application to physics has been often limited to supervised and unsupervised learning approaches. The aim of this project is: 1) construct an interpretable reinforcement learning method; 2) extract hidden rules and features in timely and paramount problems in physics. The method combines three well-established concepts of ML: projective simulation, graph neural

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recordType

award

status

SIGNED

region

EU

value

183600.96

unit

EUR