# Machine learning and the physical sciences

@article{Carleo2019MachineLA, title={Machine learning and the physical sciences}, author={Giuseppe Carleo and Ignacio I. Cirac and Kyle Cranmer and Laurent Daudet and Maria Schuld and Naftali Tishby and Leslie Vogt-Maranto and Lenka Zdeborov'a}, journal={Reviews of Modern Physics}, year={2019}, volume={91}, pages={045002} }

Machine learning (ML) encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. This article reviews in a selective way the recent research on the interface between machine learning and the physical sciences. This includes conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization…

## 557 Citations

Teaching Machine Learning for the Physical Sciences: A summary of lessons learned and challenges

- Physics
- 2021

This paper summarizes some challenges encountered and best practices established in several years of teaching Machine Learning for the Physical Sciences at the undergraduate and graduate level. I…

Deep integration of machine learning into computational chemistry and materials science

- 2021

Machine learning (ML) methods are being used in almost every conceivable area of electronic structure theory and molecular simulation. In particular, ML has become firmly established in the…

Active learning algorithm for computational physics

- Computer Science, PhysicsPhysical Review Research
- 2020

The basic idea is to fit a multi-dimensional function by neural networks, and the key point is to make the query of labeled data economically by using a stratagem called "query by committee", which explains the mechanism for the efficiency of the algorithm.

Mean-field inference methods for neural networks

- Physics, Computer ScienceArXiv
- 2019

A selection of classical mean-field methods and recent progress relevant for inference in neural networks are reviewed, and the principles of derivations of high-temperature expansions, the replica method and message passing algorithms are reminded, highlighting their equivalences and complementarities.

Machine learning approach to muon spectroscopy analysis

- Physics, Materials ScienceJournal of physics. Condensed matter : an Institute of Physics journal
- 2021

It is discovered that the PCA method works well in detecting phase transitions in muon spectroscopy experiments and can serve as an alternative to current analysis, especially if the physics of the studied material are not entirely known.

Researchers probe a machine-learning model as it solves physics problems in order to understand how such models

- 2020

M achine-learning models based on neural networks are behind many recent technological advances, including high-accuracy translations of text and self-driving cars. They are also increasingly used by…

A Living Review of Machine Learning for Particle Physics

- Computer Science, PhysicsArXiv
- 2021

This living review is a nearly comprehensive list of citations for those developing and applying deep learning approaches to experimental, phenomenological, or theoretical analyses, and will be updated as often as possible to incorporate the latest developments.

Machine and deep learning applications in particle physics

- Physics, Mathematics
- 2019

The many ways in which machine and deep learning are transforming the analysis and simulation of data in particle physics are reviewed. The main methods based on boosted decision trees and various…

Sign Structure of Many-Body Wavefunctions and Machine Learning

- 2019

Following fascinating success in image and speech recognition tasks, machine learning (ML) methods have recently proven themselves to be very useful in physical sciences. For example, ML has been…

Statistical Physics for Medical Diagnostics: Learning, Inference, and Optimization Algorithms

- Computer Science, MedicineDiagnostics
- 2020

How recent developments in statistical physics, machine learning and inference algorithms are coming together to improve current medical diagnostic approaches is discussed.

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