Morten Hjorth-Jensen

Morten Hjorth-Jensen

Michigan State University

H-index: 63

North America-United States

About Morten Hjorth-Jensen

Morten Hjorth-Jensen, With an exceptional h-index of 63 and a recent h-index of 31 (since 2020), a distinguished researcher at Michigan State University, specializes in the field of Computational Physics, Nuclear Physics, Many-body Physics.

His recent articles reflect a diverse array of research interests and contributions to the field:

Accelerating the Convergence of Coupled Cluster Calculations of the Homogeneous Electron Gas Using Bayesian Ridge Regression

Training Convolutional Neural Networks with Artificial Data to Classify Scintillator Data

Parametric Matrix Models

Efficient solutions of fermionic systems using artificial neural networks

Using Convolutional Neural Networks to Classify Scintillator Data

Mapping out the thermodynamic stability of a QCD equation of state with a critical point using active learning

Neural-network quantum states for ultra-cold Fermi gases

Quantum information science and technology for nuclear physics. Input into US Long-Range Planning, 2023

Morten Hjorth-Jensen Information

University

Position

___

Citations(all)

14743

Citations(since 2020)

4196

Cited By

12471

hIndex(all)

63

hIndex(since 2020)

31

i10Index(all)

148

i10Index(since 2020)

74

Email

University Profile Page

Michigan State University

Google Scholar

View Google Scholar Profile

Morten Hjorth-Jensen Skills & Research Interests

Computational Physics

Nuclear Physics

Many-body Physics

Top articles of Morten Hjorth-Jensen

Title

Journal

Author(s)

Publication Date

Accelerating the Convergence of Coupled Cluster Calculations of the Homogeneous Electron Gas Using Bayesian Ridge Regression

arXiv preprint arXiv:2403.04645

Julie Butler

Morten Hjorth-Jensen

Justin Lietz

2024/3/7

Training Convolutional Neural Networks with Artificial Data to Classify Scintillator Data

Bulletin of the American Physical Society

Adam Hartley

Sean Liddick

Geir Ulvik

Morten Hjorth-Jensen

Aaron Chester

2024/3/5

Parametric Matrix Models

arXiv preprint arXiv:2401.11694

Patrick Cook

Danny Jammooa

Morten Hjorth-Jensen

Daniel D Lee

Dean Lee

2024/1/22

Efficient solutions of fermionic systems using artificial neural networks

Frontiers in Physics

Even M Nordhagen

Jane M Kim

Bryce Fore

Alessandro Lovato

Morten Hjorth-Jensen

2023/6/26

Using Convolutional Neural Networks to Classify Scintillator Data

APS Meeting Abstracts

Adam Hartley

Sean Liddick

Geir Ulvik

Morten Hjorth-Jensen

Aaron Chester

2023

Mapping out the thermodynamic stability of a QCD equation of state with a critical point using active learning

Physical Review C

D Mroczek

M Hjorth-Jensen

J Noronha-Hostler

P Parotto

C Ratti

...

2023/5/18

Neural-network quantum states for ultra-cold Fermi gases

arXiv preprint arXiv:2305.08831

Jane Kim

Gabriel Pescia

Bryce Fore

Jannes Nys

Giuseppe Carleo

...

2023/5/15

Quantum information science and technology for nuclear physics. Input into US Long-Range Planning, 2023

arXiv preprint arXiv:2303.00113

Douglas Beck

Joseph Carlson

Zohreh Davoudi

Joseph Formaggio

Sofia Quaglioni

...

2023/2/28

Coulomb interaction-driven entanglement of electrons on helium

arXiv preprint arXiv:2310.04927

Niyaz R Beysengulov

Johannes Pollanen

Øyvind S Schøyen

Stian D Bilek

Jonas B Flaten

...

2023/10/7

Dilute neutron star matter from neural-network quantum states

Physical Review Research

Bryce Fore

Jane M Kim

Giuseppe Carleo

Morten Hjorth-Jensen

Alessandro Lovato

...

2023/7/31

Solving the nuclear pairing model with neural network quantum states

Physical Review E

Mauro Rigo

Benjamin Hall

Morten Hjorth-Jensen

Alessandro Lovato

Francesco Pederiva

2023/2/28

Better Support for Students in Classical Mechanics Through a Flipped Classroom Approach

Bulletin of the American Physical Society

Julie Butler

Morten Hjorth-Jensen

2022/3/4

Application of Machine Learning to Many-Body Studies of Infinite Nuclear Matter

Bulletin of the American Physical Society

Julie Butler

Morten Hjorth-Jensen

2022/10/30

Neural Network Ansätze for Infinite Matter

Bulletin of the American Physical Society

Jane Kim

Bryce Fore

Alessandro Lovato

Morten Hjorth-Jensen

2022/10/30

Neural Network Extrapolation of Many Body Methods on Infinite Matter Systems

Bulletin of the American Physical Society

Bailey Knight

Morten Hjorth-Jensen

Julie Butler

2022/10/29

Predicting solid state material platforms for quantum technologies

npj Computational Materials

Oliver Lerstøl Hebnes

Marianne Etzelmüller Bathen

Øyvind Sigmundson Schøyen

Sebastian G Winther-Larsen

Lasse Vines

...

2022/9/28

Colloquium: Machine learning in nuclear physics

Amber Boehnlein

Markus Diefenthaler

Nobuo Sato

Malachi Schram

Veronique Ziegler

...

2022/9/8

Applying Machine Learning to Many-Body Studies of Infinite Nuclear Matter

APS Division of Nuclear Physics Meeting Abstracts

Julie Butler

Morten Hjorth-Jensen

2021

Machine learning in nuclear physics

arXiv: 2112.02309

Amber Boehnlein

Markus Diefenthaler

Cristiano Fanelli

Morten Hjorth-Jensen

Tanja Horn

...

2021/12/4

Unsupervised learning for identifying events in active target experiments

Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment

Robert Solli

Daniel Bazin

Morten Hjorth-Jensen

Michelle P Kuchera

Ryan R Strauss

2021/9/11

See List of Professors in Morten Hjorth-Jensen University(Michigan State University)

Co-Authors

H-index: 110
Witold Nazarewicz

Witold Nazarewicz

Michigan State University

H-index: 109
B Alex Brown

B Alex Brown

Michigan State University

H-index: 72
Piotr Piecuch

Piotr Piecuch

Michigan State University

H-index: 69
Jouni Suhonen

Jouni Suhonen

Jyväskylän yliopisto

H-index: 54
Ruprecht Machleidt

Ruprecht Machleidt

University of Idaho

H-index: 39
Christian Forssén

Christian Forssén

Chalmers tekniska högskola

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