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ALEPH

Financiado

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... ver más

28/02/2025

UIBK

184K€

Presupuesto del proyecto: 184K€

Líder del proyecto

UNIVERSITAET INNSBRUCK No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-06-09

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

HORIZON-MSCA-2021-PF-01-01: MSCA Postdoctoral Fellowships 2021

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UIBK

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Últimas noticias

01-12-2024: REDES En las últimas 48 horas el Organismo REDES ha otorgado 1337 concesiones

01-12-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

01-12-2024: AEI En las últimas 48 horas el Organismo AEI ha otorgado 23 concesiones

Duración del proyecto: 32 meses Fecha Inicio: 2022-06-09
Fecha Fin: 2025-02-28

Líder del proyecto

UNIVERSITAET INNSBRUCK

TRL 4-5

Presupuesto del proyecto 184K€

Descripción del proyecto 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 networks (GNN) and hidden variable disentanglement. PS provides interpretable RL agents that can be trained for a variety of tasks, from the construction of quantum experiments, via skill acquisition in robotics, to the modelling of honeybee colonies. By enhancing their learning power and interpretability with GNNs and variable disentanglement, we will extract the hidden features of the systems the RL agents have interacted with and ultimately, the physical laws governing them. In particular, we will tackle problems in the field of condensed matter, where particles diffuse either passively or actively, to reach a target state. Moreover, we will consider ensembles of RL agents, so as to analyze not only the physical properties of the systems, but also their interactions and communication dynamics in the quest of a common target. The originality of the proposal is directly related to: 1) the methods that will be developed; 2) the systems of study; 3) most importantly, the information we will access and discover with the interpretable RL agents.

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