Machine learning applied to asteroid dynamics

DOI10.1007/S10569-022-10088-2OpenAlexW4286905251WikidataQ115007358 ScholiaQ115007358MaRDI QIDQ2675566

W. Barletta, V. Carruba, S. Aljbaae, M. Huaman, R. C. Domingos

Publication date: 24 September 2022

Published in: Celestial Mechanics and Dynamical Astronomy (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2110.06611

zbMATH Keywords

chaotic motionsasteroid beltstatistical methodscelestial mechanics

Mathematics Subject Classification ID

Computer science (68-XX) Mechanics of particles and systems (70-XX)

Related Items (1)

Old perturbative methods for a new problem in celestial mechanics: the space debris dynamics

Uses Software

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• Scikit
• AdaBoost.MH
• Julia
• Python
• TensorFlow
• Keras
• XGBoost
• Statsmodels
• Tensor2Tensor
• IsolationForest

Cites Work

• BART: Bayesian additive regression trees
• Cognitive computing: theory and applications
• A decision-theoretic generalization of on-line learning and an application to boosting
• Support-vector networks
• Large margin classification using the perceptron algorithm
• Identification of asteroid groups in the \(z_1\) and \(z_2\) nonlinear secular resonances through genetic algorithms
• Julia: A Fresh Approach to Numerical Computing
• Distribution of the Estimators for Autoregressive Time Series With a Unit Root
• DATA MINING AND MACHINE LEARNING IN ASTRONOMY
• Hands-On Machine Learning with R
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

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