Pages that link to "Item:Q2133484"
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The following pages link to Solving and learning nonlinear PDEs with Gaussian processes (Q2133484):
Displaying 39 items.
- Learning ``best'' kernels from data in Gaussian process regression. With application to aerodynamics (Q2083686) (← links)
- One-shot learning of stochastic differential equations with data adapted kernels (Q2111726) (← links)
- Do ideas have shape? Idea registration as the continuous limit of artificial neural networks (Q2111734) (← links)
- Numerical methods for mean field games based on Gaussian processes and Fourier features (Q2137987) (← links)
- Physics-informed Karhunen-Loéve and neural network approximations for solving inverse differential equation problems (Q2671323) (← links)
- Learning dynamical systems from data: a simple cross-validation perspective. III: Irregularly-sampled time series (Q2677775) (← links)
- Long-time integration of parametric evolution equations with physics-informed DeepONets (Q2683074) (← links)
- Data-driven control of agent-based models: an equation/variable-free machine learning approach (Q2687520) (← links)
- A note on microlocal kernel design for some slow-fast stochastic differential equations with critical transitions and application to EEG signals (Q2700697) (← links)
- A framework for machine learning of model error in dynamical systems (Q6076655) (← links)
- Covariance models and Gaussian process regression for the wave equation. Application to related inverse problems (Q6087939) (← links)
- Manifold-constrained Gaussian process inference for time-varying parameters in dynamic systems (Q6089199) (← links)
- A framework based on symbolic regression coupled with eXtended physics-informed neural networks for gray-box learning of equations of motion from data (Q6096490) (← links)
- Physics-informed radial basis network (PIRBN): a local approximating neural network for solving nonlinear partial differential equations (Q6096508) (← links)
- Learning dynamical systems from data: a simple cross-validation perspective. IV: Case with partial observations (Q6096532) (← links)
- Localized Model Reduction for Nonlinear Elliptic Partial Differential Equations: Localized Training, Partition of Unity, and Adaptive Enrichment (Q6108165) (← links)
- Learning black- and gray-box chemotactic PDEs/closures from agent based Monte Carlo simulation data (Q6110192) (← links)
- Gaussian process hydrodynamics (Q6132295) (← links)
- Sobolev regularity of Gaussian random fields (Q6144348) (← links)
- Physics-informed information field theory for modeling physical systems with uncertainty quantification (Q6147081) (← 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)
- Kernel methods are competitive for operator learning (Q6202132) (← links)
- Solving and Learning Nonlinear PDEs with Gaussian Processes (Q6363591) (← links)
- Learning dynamical systems from data: a simple cross-validation perspective. V: Sparse kernel flows for 132 chaotic dynamical systems (Q6496480) (← links)
- A kernel framework for learning differential equations and their solution operators (Q6496499) (← links)
- Learning particle swarming models from data with Gaussian processes (Q6562843) (← links)
- Learning about structural errors in models of complex dynamical systems (Q6572173) (← links)
- One-shot learning of surrogates in PDE-constrained optimization under uncertainty (Q6587616) (← links)
- A solution to the ill-conditioning of gradient-enhanced covariance matrices for Gaussian processes (Q6589332) (← links)
- Gaussian process learning of nonlinear dynamics (Q6590926) (← links)
- Extending error bounds for radial basis function interpolation to measuring the error in higher order Sobolev norms (Q6622394) (← links)
- Characterization of the second order random fields subject to linear distributional PDE constraints (Q6635739) (← links)
- Parameter inference based on Gaussian processes informed by nonlinear partial differential equations (Q6645125) (← links)
- Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learning (Q6645133) (← links)
- A Kronecker product accelerated efficient sparse Gaussian process (E-SGP) for flow emulation (Q6648366) (← links)
- Error analysis of kernel/GP methods for nonlinear and parametric PDEs (Q6648398) (← links)
- The ADMM-PINNs algorithmic framework for nonsmooth PDE-constrained optimization: a deep learning approach (Q6649881) (← links)
- Operator learning with Gaussian processes (Q6669069) (← links)
