Autonomous learning agents in Physics

Informations projet

ALEPH

N° de convention de subvention: 101063794

DOI

10.3030/101063794

Projet clôturé

Date de signature de la CE 9 Juin 2022

Date de début 1 Mars 2023

Date de fin 28 Février 2025

Financé au titre de

• Marie Skłodowska-Curie Actions (MSCA)

Coût total

Aucune donnée

Contribution de l’UE

€ 183 600,96

Coordonné par

UNIVERSITAET INNSBRUCK
Austria

Objectif

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.

Champ scientifique (EuroSciVoc)

• sciences naturelles › sciences biologiques › zoologie › entomologie › apidologie
• ingénierie et technologie › génie électrique, génie électronique, génie de l’information › ingénierie électronique › robotique
• sciences sociales › droit

Mots‑clés

• Machine Learning
• Autonomous scientific discovery

Programme(s)

• HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) PROGRAMME PRINCIPAL
  

Thème(s)

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

Régime de financement

HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

Appel à propositions

HORIZON-MSCA-2021-PF-01

Coordinateur

UNIVERSITAET INNSBRUCK

• Contribution nette de l'UE: € 183 600,96
• Adresse: INNRAIN 52 ; 6020 Innsbruck ; Autriche
• Région: Westösterreich › Tirol › Innsbruck
• Type d’activité: Higher or Secondary Education Establishments
• Coût total: Aucune donnée