Synthesis of recurrent neural networks for dynamical system simulation

DOI10.1016/j.neunet.2016.04.001zbMath1415.37102DBLPjournals/nn/TrischlerD16arXiv1512.05702OpenAlexW2278773995WikidataQ38835195 ScholiaQ38835195MaRDI QIDQ2418198

Adam P. Trischler, Gabriele M. T. D'Eleuterio

Publication date: 3 June 2019

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1512.05702

zbMATH Keywords

chaosrecurrent neural networkapproximationdynamical systemattractornonautonomous system

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Simulation of dynamical systems (37M05)

Related Items (9)

Stimulus-Driven and Spontaneous Dynamics in Excitatory-Inhibitory Recurrent Neural Networks for Sequence Representation ⋮ Data-Driven Discovery of Closure Models ⋮ Measurement and control of non-linear data using ARMA based artificial neural network ⋮ Physics-incorporated convolutional recurrent neural networks for source identification and forecasting of dynamical systems ⋮ Multibody dynamics and control using machine learning ⋮ Exploring weight initialization, diversity of solutions, and degradation in recurrent neural networks trained for temporal and decision-making tasks ⋮ Bayesian framework for simulation of dynamical systems from multidimensional data using recurrent neural network ⋮ Deep learning algorithm for data-driven simulation of noisy dynamical system ⋮ Data-driven kinematics-consistent model-order reduction of fluid–structure interaction problems: application to deformable microcapsules in a Stokes flow

Uses Software

• Adam

Cites Work

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• HETEROCLINIC CONTOURS IN NEURAL ENSEMBLES AND THE WINNERLESS COMPETITION PRINCIPLE
• A Unified Approach to Building and Controlling Spiking Attractor Networks
• Neural networks and physical systems with emergent collective computational abilities.
• Chaotic itinerancy

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