Search Results for author:
Found 10 papers, 5 papers with code
Surrogate-data-enriched Physics-Aware Neural Networks
Neural networks can be used as surrogates for PDE models.
Universality and Optimality of Structured Deep Kernel Networks
In particular, we show that the use of special types of kernels yield models reminiscent of neural networks that are founded in the same theoretical framework of classical kernel methods, while enjoying many computational properties of deep neural networks.
Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows
Standard kernel methods for machine learning usually struggle when dealing with large datasets.
Kernel methods for center manifold approximation and a data-based version of the Center Manifold Theorem
We then use an apposite data-based kernel method to construct a suitable approximation of the manifold close to the equilibrium, which is compatible with our general error theory.
Biomechanical surrogate modelling using stabilized vectorial greedy kernel methods
Greedy kernel approximation algorithms are successful techniques for sparse and accurate data-based modelling and function approximation.
A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability & uniform point distribution
Since the computation of an optimal selection of sampling points may be an infeasible task, one promising option is to use greedy methods.
Numerical Analysis Numerical Analysis
Deep recurrent Gaussian process with variational Sparse Spectrum approximation
In this paper we introduce several new Deep recurrent Gaussian process (DRGP) models based on the Sparse Spectrum Gaussian process (SSGP) and the improved version, called variational Sparse Spectrum Gaussian process (VSSGP).
Kernel Methods for Surrogate Modeling
Second, if a function is available only via measurements or a few function evaluation samples, kernel approximation techniques can provide function surrogates that allow global evaluation.
Numerical Analysis Numerical Analysis
Greedy regularized kernel interpolation
Kernel based regularized interpolation is a well known technique to approximate a continuous multivariate function using a set of scattered data points and the corresponding function evaluations, or data values.
Numerical Analysis
Deep Recurrent Gaussian Process with Variational Sparse Spectrum Approximation
Modeling sequential data has become more and more important in practice.
