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Machine-learning hidden symmetries
v1v2 (latest)

Machine-learning hidden symmetries

20 September 2021
Ziming Liu
Max Tegmark
ArXiv (abs)PDFHTML

Papers citing "Machine-learning hidden symmetries"

18 / 18 papers shown
Uncertainties in Physics-informed Inverse Problems: The Hidden Risk in Scientific AI
Uncertainties in Physics-informed Inverse Problems: The Hidden Risk in Scientific AI
Yoh-ichi Mototake
Makoto Sasaki
PINNAI4CE
364
0
0
06 Nov 2025
Deep Learning based discovery of Integrable Systems
Deep Learning based discovery of Integrable Systems
Shailesh Lal
Suvajit Majumder
E. Sobko
271
3
0
13 Mar 2025
KAN 2.0: Kolmogorov-Arnold Networks Meet Science
KAN 2.0: Kolmogorov-Arnold Networks Meet Science
Ziming Liu
Pingchuan Ma
Yixuan Wang
Wojciech Matusik
Max Tegmark
463
177
0
19 Aug 2024
Representation Learning in a Decomposed Encoder Design for Bio-inspired Hebbian Learning
Representation Learning in a Decomposed Encoder Design for Bio-inspired Hebbian Learning
Achref Jaziri
Sina Ditzel
Iuliia Pliushch
Visvanathan Ramesh
SSL
426
2
0
22 Nov 2023
Identifying the Group-Theoretic Structure of Machine-Learned Symmetries
Identifying the Group-Theoretic Structure of Machine-Learned SymmetriesPhysics Letters B (Phys. Lett. B), 2023
Roy T. Forestano
Konstantin T. Matchev
Katia Matcheva
Alexander Roman
Eyup B. Unlu
Sarunas Verner
270
6
0
14 Sep 2023
Accelerated Discovery of Machine-Learned Symmetries: Deriving the
  Exceptional Lie Groups G2, F4 and E6
Accelerated Discovery of Machine-Learned Symmetries: Deriving the Exceptional Lie Groups G2, F4 and E6Physics Letters B (Phys. Lett. B), 2023
Roy T. Forestano
Konstantin T. Matchev
Katia Matcheva
Alexander Roman
Eyup B. Unlu
Sarunas Verner
271
7
0
10 Jul 2023
Discovering Sparse Representations of Lie Groups with Machine Learning
Discovering Sparse Representations of Lie Groups with Machine LearningPhysics Letters B (Phys. Lett. B), 2023
Roy T. Forestano
Konstantin T. Matchev
Katia Matcheva
Alexander Roman
Eyup B. Unlu
Sarunas Verner
249
10
0
10 Feb 2023
Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras
  from First Principles
Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principles
Roy T. Forestano
Konstantin T. Matchev
Katia Matcheva
Alexander Roman
Eyup B. Unlu
Sarunas Verner
AI4CE
291
26
0
13 Jan 2023
Metalearning generalizable dynamics from trajectories
Metalearning generalizable dynamics from trajectoriesPhysical Review Letters (PRL), 2023
Qiaofeng Li
Tianyi Wang
V. Roychowdhury
M. Jawed
AI4CE
356
13
0
03 Jan 2023
Data Science and Machine Learning in Education
Data Science and Machine Learning in Education
G. Benelli
Thomas Y. Chen
Javier Mauricio Duarte
Matthew Feickert
Matthew Graham
...
K. Terao
S. Thais
A. Roy
J. Vlimant
G. Chachamis
AI4CE
178
5
0
19 Jul 2022
Simplifying Polylogarithms with Machine Learning
Simplifying Polylogarithms with Machine Learning
Aurélien Dersy
M. Schwartz
Xiao-Yan Zhang
AI4CE
418
22
0
08 Jun 2022
Learning Linear Symmetries in Data Using Moment Matching
Learning Linear Symmetries in Data Using Moment Matching
Colin Hagemeyer
160
1
0
04 Apr 2022
Learning Spatiotemporal Chaos Using Next-Generation Reservoir Computing
Learning Spatiotemporal Chaos Using Next-Generation Reservoir ComputingChaos (Chaos), 2022
W. A. S. Barbosa
D. Gauthier
339
44
0
24 Mar 2022
AI Poincaré 2.0: Machine Learning Conservation Laws from
  Differential Equations
AI Poincaré 2.0: Machine Learning Conservation Laws from Differential EquationsPhysical Review E (Phys. Rev. E), 2022
Ziming Liu
Varun Madhavan
M. Tegmark
PINN
316
34
0
23 Mar 2022
Machine-Learning the Classification of Spacetimes
Machine-Learning the Classification of SpacetimesPhysics Letters B (Phys. Lett. B), 2022
Yang-Hui He
J. M. Ipiña
215
5
0
05 Jan 2022
Learn one size to infer all: Exploiting translational symmetries in
  delay-dynamical and spatio-temporal systems using scalable neural networks
Learn one size to infer all: Exploiting translational symmetries in delay-dynamical and spatio-temporal systems using scalable neural networks
Mirko Goldmann
C. Mirasso
Ingo Fischer
Miguel C. Soriano
AI4CE
317
10
0
05 Nov 2021
Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed
  Learning
Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning
Ziming Liu
Yunyue Chen
Yuanqi Du
Max Tegmark
PINNAI4CE
182
23
0
28 Sep 2021
Sampling using $SU(N)$ gauge equivariant flows
Sampling using SU(N)SU(N)SU(N) gauge equivariant flows
D. Boyda
G. Kanwar
S. Racanière
Danilo Jimenez Rezende
M. S. Albergo
Kyle Cranmer
D. Hackett
P. Shanahan
448
142
0
12 Aug 2020
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