48 citations · 78 across the 8 of their papers we have counts for
10 papers
Bayesian inference of flame impulse responses
Matthew Yoko, Wolfgang Polifke
The impulse response of a flame to acoustic velocity perturbations is a key quantity for predicting thermoacoustic stability, but its identification from sparse, noisy observations…
Global linear stability analysis of a slit flame subject to intrinsic thermoacoustic instability
Grégoire Varillon, Philipp Brokof, Wolfgang Polifke
The present study makes use of the adjoint modes of the Linearized Reactive Flow (LRF) equations to investigate the Intrinsic Thermoacoustic (ITA) feedback loop of a laminar premix…
Modelling the response of a turbulent jet flame to acoustic forcing in a linearized framework using an active flame approach
Thomas Ludwig Kaiser, Gregoire Varillon, Wolfgang Polifke +4
This study performs a linear analysis of a turbulent reacting methane-air jet flame, with the goal of predicting the response of the reacting flow to upstream acoustic actuation. A…
Categorization of Thermoacoustic Modes in an Ideal Resonator with Phasor Diagrams
Kah Joon Yong, Camilo F. Silva, Guillaume J. J. Fournier +1
A recent study (Yong, Silva, and Polifke, Combust. Flame 228 (2021)) proposed the use of phasor diagrams to categorize marginally stable modes in an ideal resonator with a compact,…
Short- and long-term prediction of a chaotic flow: A physics-constrained reservoir computing approach
Nguyen Anh Khoa Doan, Wolfgang Polifke, Luca Magri
We propose a physics-constrained machine learning method-based on reservoir computing- to time-accurately predict extreme events and long-term velocity statistics in a model of tur…
Auto-Encoded Reservoir Computing for Turbulence Learning
Nguyen Anh Khoa Doan, Wolfgang Polifke, Luca Magri
We present an Auto-Encoded Reservoir-Computing (AE-RC) approach to learn the dynamics of a 2D turbulent flow. The AE-RC consists of an Autoencoder, which discovers an efficient man…
Physics-Informed Echo State Networks
Nguyen Anh Khoa Doan, Wolfgang Polifke, Luca Magri
We propose a physics-informed Echo State Network (ESN) to predict the evolution of chaotic systems. Compared to conventional ESNs, the physics-informed ESNs are trained to solve su…
Learning Hidden States in a Chaotic System: A Physics-Informed Echo State Network Approach
Nguyen Anh Khoa Doan, Wolfgang Polifke, Luca Magri
We extend the Physics-Informed Echo State Network (PI-ESN) framework to reconstruct the evolution of an unmeasured state (hidden state) in a chaotic system. The PI-ESN is trained b…
A physics-aware machine to predict extreme events in turbulence
Nguyen Anh Khoa Doan, Wolfgang Polifke, Luca Magri
We propose a physics-aware machine learning method to time-accurately predict extreme events in a turbulent flow. The method combines two radically different approaches: empirical…
Physics-Informed Echo State Networks for Chaotic Systems Forecasting
Nguyen Anh Khoa Doan, Wolfgang Polifke, Luca Magri
We propose a physics-informed Echo State Network (ESN) to predict the evolution of chaotic systems. Compared to conventional ESNs, the physics-informed ESNs are trained to solve su…