Machine learning based prediction of phase ordering dynamics

DOI10.1063/5.0156611zbMATH Open1544.37075MaRDI QIDQ6548678

M. D. Shrimali, Swati Chauhan, V. K. Yadav, Madhu Priya, Swarnendu Mandal, Prabhat K. Jaiswal

Publication date: 1 June 2024

Published in: Chaos (Search for Journal in Brave)

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05) Approximation methods and numerical treatment of dynamical systems (37M99)

Cites Work

• Reservoir computing approaches to recurrent neural network training
• Achieving criticality for reservoir computing using environment-induced explosive death
• Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations
• Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data
• Host–parasite coevolution: Role of selection, mutation, and asexual reproduction on evolvability
• Echo State Property Linked to an Input: Exploring a Fundamental Characteristic of Recurrent Neural Networks
• Phase Transition Dynamics
• Model-free prediction of multistability using echo state network
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