Exploring active subspace for neural network prediction of oscillating combustion

DOI10.1080/13647830.2021.1915500OpenAlexW3156847105MaRDI QIDQ5030781

Long Zhang, Jieli Wei, Zhuyin Ren, Na-Na Wang

Publication date: 17 February 2022

Published in: Combustion Theory and Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/13647830.2021.1915500

zbMATH Keywords

neural networkactive controldimension reductionactive subspacecombustion instability

Mathematics Subject Classification ID

Statistics (62-XX) General systems theory (93Axx) Hypersonic flows (76Kxx)

Related Items (1)

Sliding mode control for longitudinal oscillating combustion

Cites Work

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• Neural networks for control systems - a survey
• Forward and backward uncertainty quantification with active subspaces: application to hypersonic flows around a cylinder
• Exploiting active subspaces to quantify uncertainty in the numerical simulation of the hyshot II scramjet
• Active Subspaces
• Input-output parametric models for non-linear systems Part I: deterministic non-linear systems
• A Stable and Efficient Algorithm for Nonlinear Orthogonal Distance Regression
• Representations of non-linear systems: the NARMAX model
• Nonlinear self-excited oscillations of a ducted flame
• Discovering an active subspace in a single‐diode solar cell model

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