The following pages link to Bayesian Numerical Homogenization (Q5266245):
Displaying 34 items.
- Random Sampling and Efficient Algorithms for Multiscale PDEs (Q5132000) (← links)
- A Multiscale Reduced Basis Method for the Schrödinger Equation With Multiscale and Random Potentials (Q5137941) (← links)
- Computing Eigenvalues and Eigenfunctions of Schrödinger Equations Using a Model Reduction Approach (Q5160503) (← links)
- Multifidelity Data Fusion via Gradient-Enhanced Gaussian Process Regression (Q5162361) (← links)
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations (Q5162369) (← links)
- A Model Reduction Method for Multiscale Elliptic Pdes with Random Coefficients Using an Optimization Approach (Q5197631) (← links)
- Bayesian Probabilistic Numerical Methods in Time-Dependent State Estimation for Industrial Hydrocyclone Equipment (Q5208054) (← links)
- A Multiscale Finite Element Method for the Schrödinger Equation with Multiscale Potentials (Q5240799) (← links)
- Bayesian Probabilistic Numerical Methods (Q5243179) (← links)
- A Priori Error Analysis of a Numerical Stochastic Homogenization Method (Q5855636) (← links)
- Generalized Rough Polyharmonic Splines for Multiscale PDEs with Rough Coefficients (Q5864755) (← links)
- Numerical homogenization beyond scale separation (Q5887826) (← links)
- Optimal Control for Multiscale Elliptic Equations with Rough Coefficients (Q6049031) (← links)
- Covariance models and Gaussian process regression for the wave equation. Application to related inverse problems (Q6087939) (← links)
- Learning dynamical systems from data: a simple cross-validation perspective. IV: Case with partial observations (Q6096532) (← links)
- Randomized Quasi-Optimal Local Approximation Spaces in Time (Q6097875) (← links)
- Solving multi-material problems in solid mechanics using physics-informed neural networks based on domain decomposition technology (Q6099225) (← links)
- Localized Model Reduction for Nonlinear Elliptic Partial Differential Equations: Localized Training, Partition of Unity, and Adaptive Enrichment (Q6108165) (← links)
- Gaussian process hydrodynamics (Q6132295) (← links)
- Sobolev regularity of Gaussian random fields (Q6144348) (← links)
- Learning the nonlinear flux function of a hidden scalar conservation law from data (Q6145277) (← links)
- Subspace decomposition based DNN algorithm for elliptic type multi-scale PDEs (Q6162913) (← links)
- Sparse Gaussian processes for solving nonlinear PDEs (Q6173368) (← links)
- Stochastic PDE representation of random fields for large-scale Gaussian process regression and statistical finite element analysis (Q6187654) (← links)
- Theoretical Guarantees for the Statistical Finite Element Method (Q6188693) (← links)
- Kernel methods are competitive for operator learning (Q6202132) (← links)
- Bridging algorithmic information theory and machine learning: a new approach to kernel learning (Q6558847) (← links)
- Exponentially convergent multiscale finite element method (Q6575284) (← links)
- Inf-sup neural networks for high-dimensional elliptic PDE problems (Q6589859) (← links)
- A Bayesian approach to modeling finite element discretization error (Q6606971) (← links)
- Optimal artificial boundary conditions based on second-order correctors for three dimensional random elliptic media (Q6618180) (← links)
- Characterization of the second order random fields subject to linear distributional PDE constraints (Q6635739) (← links)
- Error analysis of kernel/GP methods for nonlinear and parametric PDEs (Q6648398) (← links)
- Operator learning with Gaussian processes (Q6669069) (← links)
