Kernel‐based active subspaces with application to computational fluid dynamics parametric problems using the discontinuous Galerkin method
From MaRDI portal
Publication:6092233
DOI10.1002/nme.7099arXiv2008.12083OpenAlexW3080980059MaRDI QIDQ6092233
Andrea Lario, Unnamed Author, Marco Tezzele, Gianluigi Rozza
Publication date: 23 November 2023
Published in: International Journal for Numerical Methods in Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2008.12083
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A kernel-based method for data-driven Koopman spectral analysis
- Reduced order methods for modeling and computational reduction
- Supervised principal component analysis: visualization, classification and regression on subspaces and submanifolds
- Gaussian processes with built-in dimensionality reduction: applications to high-dimensional uncertainty propagation
- An implicit high-order hybridizable discontinuous Galerkin method for linear convection-diffusion equations
- Time-differencing schemes and transform methods
- An adjoint method for a high-order discretization of deforming domain conservation laws for optimization of flow problems
- Stage-parallel fully implicit Runge-Kutta solvers for discontinuous Galerkin fluid simulations
- An explicit expression for the penalty parameter of the interior penalty method
- Reduced order modeling for nonlinear structural analysis using Gaussian process regression
- An integrated data-driven computational pipeline with model order reduction for industrial and applied mathematics
- Generalized bounds for active subspaces
- Exploiting active subspaces to quantify uncertainty in the numerical simulation of the hyshot II scramjet
- A modified SEIR model for the spread of Ebola in western Africa and metrics for resource allocation
- Active Subspaces
- Certified Reduced Basis Methods for Parametrized Partial Differential Equations
- An Interior Penalty Finite Element Method with Discontinuous Elements
- Sliced Inverse Regression for Dimension Reduction
- Data-Driven Science and Engineering
- A Supervised Learning Approach Involving Active Subspaces for an Efficient Genetic Algorithm in High-Dimensional Optimization Problems
- Active Subspace of Neural Networks: Structural Analysis and Universal Attacks
- Gradient-Based Dimension Reduction of Multivariate Vector-Valued Functions
- Sparse sufficient dimension reduction
- Sufficient Dimension Reduction via Inverse Regression
This page was built for publication: Kernel‐based active subspaces with application to computational fluid dynamics parametric problems using the discontinuous Galerkin method
