Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression
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Publication:2083714
DOI10.1016/j.physd.2022.133454OpenAlexW4286436535WikidataQ114141878 ScholiaQ114141878MaRDI QIDQ2083714
Masaki Morimoto, Romit Maulik, Kai Fukami, Koji Fukagata, Ricardo Vinuesa
Publication date: 11 October 2022
Published in: Physica D (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2109.08248
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Cites Work
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- Reliable classification: learning classifiers that distinguish aleatoric and epistemic uncertainty
- Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: a data-driven, physics-informed Bayesian approach
- An introduction to MCMC for machine learning
- Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
- Deep learning of the spanwise-averaged Navier-Stokes equations
- A long short-term memory embedding for hybrid uplifted reduced order models
- Prediction of aerodynamic flow fields using convolutional neural networks
- Reducing the Dimensionality of Data with Neural Networks
- Machine Learning for Fluid Mechanics
- Acceleration of Stochastic Approximation by Averaging
- Subgrid modelling for two-dimensional turbulence using neural networks
- Deep learning of mixing by two ‘atoms’ of stratified turbulence
- Prediction of turbulent heat transfer using convolutional neural networks
- Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
- Sparse identification of nonlinear dynamics with low-dimensionalized flow representations
- Shallow neural networks for fluid flow reconstruction with limited sensors
- Online in situ prediction of 3-D flame evolution from its history 2-D projections via deep learning
- A neural network approach for the blind deconvolution of turbulent flows
- Nonlinear mode decomposition with convolutional neural networks for fluid dynamics
- State estimation in wall-bounded flow systems. Part 2. Turbulent flows
- Learning representations by back-propagating errors
- Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
- Turbulence Modeling in the Age of Data
- Sub-grid scale model classification and blending through deep learning
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