Learning new physics from an imperfect machine

to related objects

Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 1434-6052

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: Learning new physics from an imperfect machine ; volume:82 ; number:3 ; day:30 ; month:3 ; year:2022 ; pages:1-37 ; date:3.2022
The European physical journal / C. C, Particles and fields ; 82, Heft 3 (30.3.2022), 1-37, 3.2022

Creator: D’Agnolo, Raffaele Tito
Grosso, Gaia
Pierini, Maurizio
Wulzer, Andrea
Zanetti, Marco

Contributor: SpringerLink (Online service)

DOI: 10.1140/epjc/s10052-022-10226-y

URN: urn:nbn:de:101:1-2022070222031843559063

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:37 AM CEST

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Show original at data provider

Associated

D’Agnolo, Raffaele Tito
Grosso, Gaia
Pierini, Maurizio
Wulzer, Andrea
Zanetti, Marco
SpringerLink (Online service)

Other Objects (12)

Machine learning in solar physics

3D multi-physics uncertainty quantification using physics-based machine learning

High-energy nuclear physics meets machine learning

Physics-Enhanced Machine Learning for Chemical Kinetics

Physics meets machine learning : theory and application

Learning multivariate new physics

Special issue: Machine learning methods in plasma physics

Predicting glass structure by physics-informed machine learning

Physics-guided Machine Learning for Small Data Sets

Generative Machine Learning for Fast Particle Physics Simulations

Physics-driven machine learning : from biomolecules to crystals

Machine learning in solar physics

3D multi-physics uncertainty quantification using physics-based machine learning

High-energy nuclear physics meets machine learning

Physics-Enhanced Machine Learning for Chemical Kinetics

Physics meets machine learning : theory and application

Learning multivariate new physics

Special issue: Machine learning methods in plasma physics

Predicting glass structure by physics-informed machine learning

Physics-guided Machine Learning for Small Data Sets

Generative Machine Learning for Fast Particle Physics Simulations

Physics-driven machine learning : from biomolecules to crystals

Machine learning in solar physics

3D multi-physics uncertainty quantification using physics-based machine learning

High-energy nuclear physics meets machine learning

Physics-Enhanced Machine Learning for Chemical Kinetics

Physics meets machine learning : theory and application

Learning multivariate new physics

Special issue: Machine learning methods in plasma physics

Predicting glass structure by physics-informed machine learning

Physics-guided Machine Learning for Small Data Sets

Generative Machine Learning for Fast Particle Physics Simulations

Physics-driven machine learning : from biomolecules to crystals

Cultural heritage institutions wishing to register will find more information here.

Fields marked * need to be filled in.

Username*

Please enter your username

Email*

Please enter your email address

Please do not fill this field

First name

Last name

Password*

Please enter your password

Confirm password*

Please enter the same password

I have read the terms of use and the privacy policy for the collection of personal data and accept them. *

This field is required.

I would like to subscribe to the newsletter of the Deutsche Digitale Bibliothek. See newsletter subscription info.

Account created

Your "My DDB" account has been successfully created. Before you can log in to your account, you must click the confirmation link in the message we just sent to the email address you provided.

Learning new physics from an imperfect machine

Object Details

References and Relationships

Contributors, Places and Time

Further information

Data provider

Associated

Other Objects (12)

Machine learning in solar physics

3D multi-physics uncertainty quantification using physics-based machine learning

3D multi-physics uncertainty quantification using physics-based machine learning

High-energy nuclear physics meets machine learning

Physics-Enhanced Machine Learning for Chemical Kinetics

Physics meets machine learning : theory and application

Learning multivariate new physics

Special issue: Machine learning methods in plasma physics

Predicting glass structure by physics-informed machine learning

Physics-guided Machine Learning for Small Data Sets

Generative Machine Learning for Fast Particle Physics Simulations

Physics-driven machine learning : from biomolecules to crystals

Machine learning in solar physics

3D multi-physics uncertainty quantification using physics-based machine learning

3D multi-physics uncertainty quantification using physics-based machine learning

High-energy nuclear physics meets machine learning

Physics-Enhanced Machine Learning for Chemical Kinetics

Physics meets machine learning : theory and application

Learning multivariate new physics

Special issue: Machine learning methods in plasma physics

Predicting glass structure by physics-informed machine learning

Physics-guided Machine Learning for Small Data Sets

Generative Machine Learning for Fast Particle Physics Simulations

Physics-driven machine learning : from biomolecules to crystals

Machine learning in solar physics

3D multi-physics uncertainty quantification using physics-based machine learning

3D multi-physics uncertainty quantification using physics-based machine learning

High-energy nuclear physics meets machine learning

Physics-Enhanced Machine Learning for Chemical Kinetics

Physics meets machine learning : theory and application

Learning multivariate new physics

Special issue: Machine learning methods in plasma physics

Predicting glass structure by physics-informed machine learning

Physics-guided Machine Learning for Small Data Sets

Generative Machine Learning for Fast Particle Physics Simulations

Physics-driven machine learning : from biomolecules to crystals

Related objects

Reset password