Gaussian process hydrodynamics
DOI10.1007/s10483-023-2990-9zbMath1516.35310arXiv2209.10707OpenAlexW4366449378MaRDI QIDQ6132295
No author found.
Publication date: 14 July 2023
Published in: AMM. Applied Mathematics and Mechanics. (English Edition) (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2209.10707
Gaussian processes (60G15) Probabilistic methods, particle methods, etc. for boundary value problems involving PDEs (65N75) Spectral, collocation and related methods for boundary value problems involving PDEs (65N35) Nonlinear boundary value problems for ordinary differential equations (34B15) Navier-Stokes equations for incompressible viscous fluids (76D05) Navier-Stokes equations (35Q30) Spline approximation (41A15) Kernel operators (47B34) Probabilistic methods, particle methods, etc. for initial value and initial-boundary value problems involving PDEs (65M75)
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Compression, inversion, and approximate PCA of dense kernel matrices at near-linear computational complexity
- Stability of two-dimensional Navier-Stokes motions in the periodic case
- Gamblets for opening the complexity-bottleneck of implicit schemes for hyperbolic and parabolic ODEs/PDEs with rough coefficients
- Reproducing kernel Hilbert spaces associated with analytic translation-invariant Mercer kernels
- Smoothed particle hydrodynamics (SPH): an overview and recent developments
- Vortex methods for flow simulation
- The anisotropic Lagrangian averaged Euler and Navier-Stokes equations
- Learning dynamical systems from data: a simple cross-validation perspective. I: Parametric kernel flows
- Learning ``best kernels from data in Gaussian process regression. With application to aerodynamics
- One-shot learning of stochastic differential equations with data adapted kernels
- Do ideas have shape? Idea registration as the continuous limit of artificial neural networks
- Solving and learning nonlinear PDEs with Gaussian processes
- Kernel flows: from learning kernels from data into the abyss
- De-noising by thresholding operator adapted wavelets
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Computational graph completion
- Learning dynamical systems from data: a simple cross-validation perspective. III: Irregularly-sampled time series
- Kernels for Vector-Valued Functions: A Review
- Two-Dimensional Turbulence
- Multigrid with Rough Coefficients and Multiresolution Operator Decomposition from Hierarchical Information Games
- Vortex Methods
- Smoothed particle hydrodynamics: theory and application to non-spherical stars
- Sixth problem of the millennium: Navier-Stokes equations, existence and smoothness
- Can the atmospheric kinetic energy spectrum be explained by two-dimensional turbulence?
- Can a finite element method perform arbitrarily badly?
- Consistency of empirical Bayes and kernel flow for hierarchical parameter estimation
- Kernel Mode Decomposition and the Programming of Kernels
- Operator-Adapted Wavelets, Fast Solvers, and Numerical Homogenization
- Sparse Cholesky Factorization by Kullback--Leibler Minimization
- Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
- Statistical Numerical Approximation
- Bayesian Numerical Homogenization
- Scattered Data Approximation
This page was built for publication: Gaussian process hydrodynamics
