Non-intrusive reduced-order modeling using uncertainty-aware Deep Neural Networks and Proper Orthogonal Decomposition: Application to flood modeling

  title={Non-intrusive reduced-order modeling using uncertainty-aware Deep Neural Networks and Proper Orthogonal Decomposition: Application to flood modeling},
  author={Pierre Jacquier and Azzedine Abdedou and Vincent Delmas and Azzeddine Soula{\"i}mani},
  journal={J. Comput. Phys.},
Deep Learning Methods for Flood Mapping: A Review of Existing Applications and Future Research Directions
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Predicting Physics in Mesh-reduced Space with Temporal Attention
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Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning
BT-AE framework, a unified data-driven reduced order model (ROM) that bridges the performance gap between linear and nonlinear manifold approaches, relies on the combination of an autoencoder and Barlow Twins self-supervised learning, where BT maximizes the information content of the embedding with the latent space through a joint embedding architecture.
Reply on RC1
Such kind of study is not novel, and it could be argued that the authors chose to perform a study that was doomed to failure since it has been long known that macroalgal d13C is widely variable due
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Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
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  • 2017
Although neural networks have been applied previously to complex fluid flows, the article featured here is the first to apply a true DNN architecture, specifically to Reynolds averaged Navier Stokes turbulence models, suggesting that DNNs may play a critically enabling role in the future of modelling complex flows.
Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data
This work attempts to quantify uncertainty in a more formal framework while maintaining the forecast accuracy that makes these models appealing, by presenting a Bayesian RNN model for nonlinear spatio-temporal forecasting.
Ensemble learning in Bayesian neural networks
This chapter shows how the ensemble learning approach can be extended to full-covariance Gaussian distributions while remaining computationally tractable, and extends the framework to deal with hyperparameters, leading to a simple re-estimation procedure.
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Application of deep learning based multi-fidelity surrogate model to robust aerodynamic design optimization
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