Non-intrusive reduced-order modeling using uncertainty-aware deep neural networks and proper orthogonal decomposition: application to flood modeling
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Publication:2123910
DOI10.1016/j.jcp.2020.109854OpenAlexW3032875641MaRDI QIDQ2123910
Pierre Jacquier, Vincent Delmas, Azzeddine Soulaimani, Azzedine Abdedou
Publication date: 14 April 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2005.13506
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Cites Work
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- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- Calibrated reduced-order POD-Galerkin system for fluid flow modelling
- A POD-based reduced-order model for free surface shallow water flows over real bathymetries for Monte-Carlo-type applications
- Non-intrusive reduced order modeling of nonlinear problems using neural networks
- Machine learning of linear differential equations using Gaussian processes
- Machine learning design of volume of fluid schemes for compressible flows
- Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem
- Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems
- Machine Learning Methods in the Environmental Sciences
- Turbulence and the dynamics of coherent structures. I. Coherent structures
- Taylor expansion of the accumulated rounding error
- Data-Driven Science and Engineering
- Deep learning of vortex-induced vibrations
- Probable networks and plausible predictions — a review of practical Bayesian methods for supervised neural networks
- Learning representations by back-propagating errors
- Deep learning in fluid dynamics
- Centroidal Voronoi Tessellation-Based Reduced-Order Modeling of Complex Systems
