Can I read Neural Network Approach to Response Surface Development for Reaction Model Optimization and Uncertainty Minimization on EtoBox?

Neural Network Approach to Response Surface Development for Reaction Model Optimization and Uncertainty Minimization by Yue Zhang; Wendi Dong; Laurien A. Vandewalle; Rui Xu; Gregory P. Smith; Hai Wang is a Engineering article available to read on EtoBox.

What is Neural Network Approach to Response Surface Development for Reaction Model Optimization and Uncertainty Minimization about?

We examine the state-of-the-art neural network (NN) approach and its flexible implementations in combustion reaction model uncertainty quantification (UQ), optimization, and uncertainty minimization (UM). The work is motivated by addressing the problem of limited scalability of the traditional polynomial response surface methodology in handling large size of rate parameters and target data sets. Features of the NN training, accuracy, and trade-offs in several key aspects of the NN application are discussed. We show that for high-dimensional reaction model optimization and UM, a shallow NN with only one hidden layer is more robust and accurate than the polynomial response methodology. Further, we demonstrate that NN allows for adaptive training. New neural networks that augment new input parameters or updates in a trial reaction model can be adapted from the existing networks with much smaller training efforts. In addition, deep neural networks are capable of covering functional dependencies of initial thermodynamic conditions and boundary conditions, thus yielding generalized response surfaces with rate parameters and thermodynamic/mixture conditions as the input for a given combus

Who reads Neural Network Approach to Response Surface Development for Reaction Model Optimization and Uncertainty Minimization?

Author

Yue Zhang; Wendi Dong; Laurien A. Vandewalle; Rui Xu; Gregory P. Smith; Hai Wang

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)