Adaptive experimental design for multi‐fidelity surrogate modeling of multi‐disciplinary systems
From MaRDI portal
Publication:6069984
DOI10.1002/nme.6958OpenAlexW4214818144MaRDI QIDQ6069984
Lorenzo Tamellini, Unnamed Author, Unnamed Author, Unnamed Author, Michael S. Eldred, John D. Jakeman
Publication date: 17 November 2023
Published in: International Journal for Numerical Methods in Engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1002/nme.6958
Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx) Numerical methods for partial differential equations, boundary value problems (65Nxx) Probabilistic methods, stochastic differential equations (65Cxx)
Cites Work
- Unnamed Item
- Unnamed Item
- Tensor-Train Decomposition
- Convergence of quasi-optimal sparse-grid approximation of Hilbert-space-valued functions: Application to random elliptic PDEs
- A flexible uncertainty quantification method for linearly coupled multi-physics systems
- High-dimensional adaptive sparse polynomial interpolation and applications to parametric PDEs
- Comparison between reduced basis and stochastic collocation methods for elliptic problems
- Multi-index stochastic collocation convergence rates for random PDEs with parametric regularity
- Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations. Application to transport and continuum mechanics.
- Numerical approach for quantification of epistemic uncertainty
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
- Systems of Gaussian process models for directed chains of solvers
- IGA-based multi-index stochastic collocation for random PDEs on arbitrary domains
- Multi-index stochastic collocation for random PDEs
- Gradient-based optimization for regression in the functional tensor-train format
- A flexible numerical approach for quantification of epistemic uncertainty
- Non-intrusive low-rank separated approximation of high-dimensional stochastic models
- Optimal Local Approximation Spaces for Component-Based Static Condensation Procedures
- Nonlinear model order reduction based on local reduced-order bases
- Measure transformation and efficient quadrature in reduced-dimensional stochastic modeling of coupled problems
- Efficient uncertainty propagation for network multiphysics systems
- A decomposition‐based approach to uncertainty analysis of feed‐forward multicomponent systems
- Parallel Domain Decomposition Strategies for Stochastic Elliptic Equations Part B: Accelerated Monte Carlo Sampling with Local PC Expansions
- Local Polynomial Chaos Expansion for Linear Differential Equations with High Dimensional Random Inputs
- A Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data
- Coupling Computer Models through Linking Their Statistical Emulators
- The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
- Efficient sequential experimental design for surrogate modeling of nested codes
- DEEP LEARNING OF PARAMETERIZED EQUATIONS WITH APPLICATIONS TO UNCERTAINTY QUANTIFICATION
- On the Convergence of Adaptive Stochastic Collocation for Elliptic Partial Differential Equations with Affine Diffusion
- Cholesky-Based Experimental Design for Gaussian Process and Kernel-Based Emulation and Calibration
- Adaptive Leja Sparse Grid Constructions for Stochastic Collocation and High-Dimensional Approximation
- A Domain Decomposition Model Reduction Method for Linear Convection-Diffusion Equations with Random Coefficients
- Sparse grids
- Reduced Basis Collocation Methods for Partial Differential Equations with Random Coefficients
- Localized Discrete Empirical Interpolation Method
- High-Order Collocation Methods for Differential Equations with Random Inputs
- A Flexible Uncertainty Propagation Framework for General Multiphysics Systems
- Accurate Solution of Bayesian Inverse Uncertainty Quantification Problems Combining Reduced Basis Methods and Reduction Error Models