Surrogate modeling of parameterized multi-dimensional premixed combustion with physics-informed neural networks for rapid exploration of design space
Authors:
- Kai LiuKLKai Liu
- Kun LuoKun Luo
- Yuzhou ChengYCYuzhou Cheng
- Anxiong LiuALAnxiong Liu
- Haochen LiHLHaochen Li
- Jianren FanJFJianren Fan
- S. BalachandarSBS. Balachandar
- 29 September 2023
- journal article
- research article
- Published by Elsevier in Combustion and Flame
- Vol. 258
Abstract
No abstract availableKeywords
This publication has 50 references indexed in Scilit:
- Combustion modeling using principal component analysisProceedings of the Combustion Institute, 2009
- Optimization of Combustion Chamber for Diesel Engine Using Kriging ModelJournal of Fluid Science and Technology, 2006
- Combustion dynamics and control: Progress and challengesProceedings of the Combustion Institute, 2002
- Experimental analysis and large eddy simulation to determine the response of non-premixed flames submitted to acoustic forcingProceedings of the Combustion Institute, 2002
- Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimatesMathematics and Computers in Simulation, 2001
- Modelling the Temporal Evolution of a Reduced Combustion Chemical System With an Artificial Neural NetworkCombustion and Flame, 1998
- Multilayer feedforward networks are universal approximatorsNeural Networks, 1989
- Vortex-driven acoustically coupled combustion instabilitiesJournal of Fluid Mechanics, 1987
- Direct numerical simulations of a reacting mixing layer with chemical heat releaseAIAA Journal, 1986
- LII. The viscosity of gases and molecular forceJournal of Computers in Education, 1893