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Rediscovering orbital mechanics with machine learning

Rediscovering orbital mechanics with machine learning

4 February 2022
Pablo Lemos
N. Jeffrey
M. Cranmer
S. Ho
Peter W. Battaglia
    PINNAI4CE
ArXiv (abs)PDFHTML

Papers citing "Rediscovering orbital mechanics with machine learning"

38 / 38 papers shown
Rediscovering the Lunar Equation of the Centre with AI Feynman via Embedded Physical Biases
Rediscovering the Lunar Equation of the Centre with AI Feynman via Embedded Physical Biases
Saumya Shah
Zi-Yu Khoo
Abel Yang
Stéphane Bressan
113
0
0
13 Nov 2025
Improving Monte Carlo Tree Search for Symbolic Regression
Improving Monte Carlo Tree Search for Symbolic Regression
Zhengyao Huang
Daniel Zhengyu Huang
Tiannan Xiao
Dina Ma
Zhenyu Ming
Hao Shi
Yuanhui Wen
179
1
0
19 Sep 2025
Automated discovery of finite volume schemes using Graph Neural Networks
Automated discovery of finite volume schemes using Graph Neural Networks
Paul Garnier
J. Viquerat
E. Hachem
165
0
0
26 Aug 2025
Zobrist Hash-based Duplicate Detection in Symbolic Regression
Zobrist Hash-based Duplicate Detection in Symbolic Regression
Bogdan Burlacu
126
0
0
19 Aug 2025
Symbolic Quantile Regression for the Interpretable Prediction of Conditional Quantiles
Symbolic Quantile Regression for the Interpretable Prediction of Conditional Quantiles
Cas Oude Hoekstra
Floris den Hengst
158
0
0
11 Aug 2025
How Should We Meta-Learn Reinforcement Learning Algorithms?
How Should We Meta-Learn Reinforcement Learning Algorithms?
Alexander David Goldie
Zilin Wang
Jakob Foerster
Jakob N. Foerster
Shimon Whiteson
OffRL
335
5
0
23 Jul 2025
What Has a Foundation Model Found? Using Inductive Bias to Probe for World Models
What Has a Foundation Model Found? Using Inductive Bias to Probe for World Models
Keyon Vafa
Peter G. Chang
Ashesh Rambachan
S. Mullainathan
764
28
0
09 Jul 2025
STFlow: Data-Coupled Flow Matching for Geometric Trajectory Simulation
STFlow: Data-Coupled Flow Matching for Geometric Trajectory Simulation
Kiet Bennema ten Brinke
Koen Minartz
Vlado Menkovski
AI4CE
288
2
0
24 May 2025
On the definition and importance of interpretability in scientific machine learning
On the definition and importance of interpretability in scientific machine learning
Conor Rowan
Alireza Doostan
AI4CE
405
4
0
16 May 2025
Interpretable Machine Learning in Physics: A Review
Interpretable Machine Learning in Physics: A Review
Sebastian Johann Wetzel
Seungwoong Ha
Raban Iten
Miriam Klopotek
Ziming Liu
AI4CE
436
16
0
30 Mar 2025
A Perspective on Symbolic Machine Learning in Physical Sciences
A Perspective on Symbolic Machine Learning in Physical Sciences
N. Makke
Sanjay Chawla
AI4CE
287
4
0
25 Feb 2025
SyMANTIC: An Efficient Symbolic Regression Method for Interpretable and Parsimonious Model Discovery in Science and Beyond
SyMANTIC: An Efficient Symbolic Regression Method for Interpretable and Parsimonious Model Discovery in Science and Beyond
Madhav Muthyala
Farshud Sorourifar
You Peng
J. Paulson
581
9
0
05 Feb 2025
Gravity-Bench-v1: A Benchmark on Gravitational Physics Discovery for Agents
Gravity-Bench-v1: A Benchmark on Gravitational Physics Discovery for Agents
Nolan Koblischke
Hyunseok Jang
Kristen Menou
M. Ali-Dib
354
6
0
30 Jan 2025
Inferring Interpretable Models of Fragmentation Functions using Symbolic Regression
Inferring Interpretable Models of Fragmentation Functions using Symbolic Regression
N. Makke
Sanjay Chawla
401
2
0
13 Jan 2025
Ab Initio Nonparametric Variable Selection for Scalable Symbolic Regression with Large $p$
Ab Initio Nonparametric Variable Selection for Scalable Symbolic Regression with Large ppp
Shengbin Ye
Meng Li
438
3
0
17 Oct 2024
Symbolic Regression with a Learned Concept Library
Symbolic Regression with a Learned Concept LibraryNeural Information Processing Systems (NeurIPS), 2024
Arya Grayeli
Atharva Sehgal
Omar Costilla-Reyes
Miles Cranmer
Swarat Chaudhuri
309
54
0
14 Sep 2024
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
471
186
0
19 Aug 2024
Decomposing heterogeneous dynamical systems with graph neural networks
Decomposing heterogeneous dynamical systems with graph neural networks
Cédric Allier
Magdalena C. Schneider
Michael Innerberger
Larissa Heinrich
J. Bogovic
S. Saalfeld
AI4CECML
322
1
0
27 Jul 2024
Discovering interpretable models of scientific image data with deep
  learning
Discovering interpretable models of scientific image data with deep learning
Christopher J. Soelistyo
Alan R. Lowe
252
8
0
05 Feb 2024
SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compression
SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compression
Ho Fung Tsoi
Vladimir Loncar
S. Dasu
Philip C. Harris
513
13
0
18 Jan 2024
Towards Explainable Artificial Intelligence (XAI): A Data Mining
  Perspective
Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective
Haoyi Xiong
Xuhong Li
Xiaofei Zhang
Jiamin Chen
Xinhao Sun
Yuchen Li
Zeyi Sun
Jundong Li
XAI
420
15
0
09 Jan 2024
Learning About Structural Errors in Models of Complex Dynamical Systems
Learning About Structural Errors in Models of Complex Dynamical SystemsJournal of Computational Physics (JCP), 2023
Jin-Long Wu
Matthew E. Levine
Tapio Schneider
Andrew M. Stuart
AI4CE
516
29
0
29 Dec 2023
Seeking Truth and Beauty in Flavor Physics with Machine Learning
Seeking Truth and Beauty in Flavor Physics with Machine Learning
Konstantin T. Matchev
Katia Matcheva
Pierre Ramond
Sarunas Verner
AI4CE
335
2
0
31 Oct 2023
Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural
  Networks
Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks
Daniel Levy
Sekouba Kaba
Carmelo Gonzales
Santiago Miret
Siamak Ravanbakhsh
216
9
0
06 Sep 2023
Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems
Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems
Stefan Kramer
Mattia Cerrato
Jannis Brugger
Sašo Džeroski
Ross King
430
18
0
03 May 2023
Machine learning for discovering laws of nature
Machine learning for discovering laws of nature
Lizhi Xin
Kevin Xin
H. Xin
AI4CE
226
0
0
18 Mar 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
312
26
0
13 Jan 2023
Exhaustive Symbolic Regression
Exhaustive Symbolic RegressionIEEE Transactions on Evolutionary Computation (TEVC), 2022
Deaglan J. Bartlett
Harry Desmond
Pedro G. Ferreira
271
47
0
21 Nov 2022
Interpretable Scientific Discovery with Symbolic Regression: A Review
Interpretable Scientific Discovery with Symbolic Regression: A ReviewArtificial Intelligence Review (Artif Intell Rev), 2022
N. Makke
Sanjay Chawla
365
261
0
20 Nov 2022
Is the Machine Smarter than the Theorist: Deriving Formulas for Particle
  Kinematics with Symbolic Regression
Is the Machine Smarter than the Theorist: Deriving Formulas for Particle Kinematics with Symbolic Regression
Zhongtian Dong
K. Kong
Konstantin T. Matchev
Katia Matcheva
213
20
0
15 Nov 2022
Microscopy is All You Need
Microscopy is All You Need
Sergei V. Kalinin
Rama K Vasudevan
Yongtao Liu
Ayana Ghosh
Kevin M. Roccapriore
M. Ziatdinov
208
1
0
12 Oct 2022
The Cosmic Graph: Optimal Information Extraction from Large-Scale
  Structure using Catalogues
The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using CataloguesThe Open Journal of Astrophysics (JOA), 2022
T. Lucas Makinen
Tom Charnock
Pablo Lemos
Natalia Porqueres
A. Heavens
Benjamin Dan Wandelt
318
37
0
11 Jul 2022
Towards Learning Self-Organized Criticality of Rydberg Atoms using Graph
  Neural Networks
Towards Learning Self-Organized Criticality of Rydberg Atoms using Graph Neural Networks
Simon Ohler
Daniel Brady
Winfried Lotzsch
M. Fleischhauer
Johannes Otterbach
AI4CE
175
1
0
05 Jul 2022
Symbolic Regression is NP-hard
Symbolic Regression is NP-hard
M. Virgolin
S. Pissis
537
95
0
03 Jul 2022
SYMBA: Symbolic Computation of Squared Amplitudes in High Energy Physics
  with Machine Learning
SYMBA: Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine Learning
Abdulhakim Alnuqaydan
S. Gleyzer
Harrison B. Prosper
390
22
0
17 Jun 2022
On scientific understanding with artificial intelligence
On scientific understanding with artificial intelligenceNature Reviews Physics (Nat. Rev. Phys.), 2022
Mario Krenn
R. Pollice
S. Guo
Matteo Aldeghi
Alba Cervera-Lierta
...
Florian Hase
A. Jinich
AkshatKumar Nigam
Zhenpeng Yao
Alán Aspuru-Guzik
323
284
0
04 Apr 2022
A World-Self Model Towards Understanding Intelligence
A World-Self Model Towards Understanding IntelligenceIEEE Access (IEEE Access), 2022
Yutao Yue
307
3
0
25 Mar 2022
Machine Learning and Cosmology
Machine Learning and Cosmology
C. Dvorkin
S. Mishra-Sharma
Brian D. Nord
V. A. Villar
Camille Avestruz
...
A. Ćiprijanović
Andrew J. Connolly
L. Garrison
G. Narayan
F. Villaescusa-Navarro
AI4CE
489
19
0
15 Mar 2022
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