Building healthy Lagrangian theories with machine learning

DOI10.1142/S0218271821500851zbMath1490.83070arXiv2002.00049OpenAlexW3184631163MaRDI QIDQ5068312

Fotios K. Anagnostopoulos, Emmanuel N. Saridakis, Christos Valelis, Spyros Basilakos

Publication date: 6 April 2022

Published in: International Journal of Modern Physics D (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2002.00049

zbMATH Keywords

neural networksmachine learningmodified gravityLagrangianshigher-derivatives

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Small world graphs, complex networks (graph-theoretic aspects) (05C82) Learning and adaptive systems in artificial intelligence (68T05) Nonlinear higher-order PDEs (35G20) Relativistic gravitational theories other than Einstein's, including asymmetric field theories (83D05) Initial value problems for linear higher-order PDEs (35G10) Lagrangian formalism and Hamiltonian formalism in mechanics of particles and systems (70S05) Lagrange's equations (70H03) Propagation of singularities; initial value problems on manifolds (58J47)

Cites Work

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• Computational analysis and understanding of natural languages: principles, methods and applications
• Cyclic cosmology from Lagrange-multiplier modified gravity
• A deep learning approach to cosmological dark energy models

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