Data Assimilation Using Heteroscedastic Bayesian Neural Network Ensembles for Reduced-Order Flame Models

Croci, Maximilian L.; Sengupta, Ushnish; Juniper, Matthew P.

doi:10.1007/978-3-030-77977-1_33

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…In such systems, an accurate initial condition is of the utmost importance for predictive dynamical models to provide accurate forecasts. DA is critical in various areas of engineering and sciences, such as weather prediction [7,8,9], environmental and geophysical flows [10,11,12], combustion systems [13,14,15], aeronautics [16], hydrology [17], acoustics [18], and fluid mechanics [19,20].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Enhanced Ensemble-Based Data Assimilation for High-Dimensional Nonlinear Dynamical Systems

et al. 2022

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Enhanced Ensemble-Based Data Assimilation for High-Dimensional Nonlinear Dynamical Systems

et al. 2022

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Machine Learning for Thermoacoustics

Juniper

2023

Lecture Notes in Energy

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This chapter demonstrates three promising ways to combine machine learning with physics-based modelling in order to model, forecast, and avoid thermoacoustic instability. The first method assimilates experimental data into candidate physics-based models and is demonstrated on a Rijke tube. This uses Bayesian inference to select the most likely model. This turns qualitatively-accurate models into quantitatively-accurate models that can extrapolate, which can be combined powerfully with automated design. The second method assimilates experimental data into level set numerical simulations of a premixed bunsen flame and a bluff-body stabilized flame. This uses either an Ensemble Kalman filter, which requires no prior simulation but is slow, or a Bayesian Neural Network Ensemble, which is fast but requires prior simulation. This method deduces the simulations’ parameters that best reproduce the data and quantifies their uncertainties. The third method recognises precursors of thermoacoustic instability from pressure measurements. It is demonstrated on a turbulent bunsen flame, an industrial fuel spray nozzle, and full scale aeroplane engines. With this method, Bayesian Neural Network Ensembles determine how far each system is from instability. The trained BayNNEs out-perform physics-based methods on a given system. This method will be useful for practical avoidance of thermoacoustic instability.

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Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems

Chattopadhyay

Nabizadeh

Bach

et al. 2023

Journal of Computational Physics

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Data Assimilation Using Heteroscedastic Bayesian Neural Network Ensembles for Reduced-Order Flame Models

Cited by 3 publications

References 12 publications

Deep Learning-Enhanced Ensemble-Based Data Assimilation for High-Dimensional Nonlinear Dynamical Systems

Deep Learning-Enhanced Ensemble-Based Data Assimilation for High-Dimensional Nonlinear Dynamical Systems

Machine Learning for Thermoacoustics

Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems

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