scholarly journals Precessing numerical relativity waveform surrogate model for binary black holes: A Gaussian process regression approach

2020 ◽  
Vol 101 (6) ◽  
Author(s):  
D. Williams ◽  
I. S. Heng ◽  
J. Gair ◽  
J. A. Clark ◽  
B. Khamesra
Keyword(s):  
Black Holes ◽  
Gaussian Process ◽  
Surrogate Model ◽  
Numerical Relativity ◽  
Gaussian Process Regression ◽  
Binary Black Holes ◽  
Regression Approach

scholarly journals Up-down instability of binary black holes in numerical relativity

2021 ◽  
Vol 103 (6) ◽  
Author(s):  
Vijay Varma ◽  
Matthew Mould ◽  
Davide Gerosa ◽  
Mark A. Scheel ◽  
Lawrence E. Kidder ◽  
...  
Keyword(s):  
Black Holes ◽  
Numerical Relativity ◽  
Binary Black Holes

scholarly journals A blackbox yield estimation workflow with Gaussian process regression applied to the design of electromagnetic devices

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mona Fuhrländer ◽  
Sebastian Schöps
Keyword(s):  
Gaussian Process ◽  
Surrogate Model ◽  
Hybrid Approach ◽  
Gaussian Process Regression ◽  
Benchmark Problems ◽  
Yield Estimation ◽  
Electromagnetic Devices ◽  
Main Challenge ◽  
Sample Points ◽  
Stochastic Yield

Abstract In this paper an efficient and reliable method for stochastic yield estimation is presented. Since one main challenge of uncertainty quantification is the computational feasibility, we propose a hybrid approach where most of the Monte Carlo sample points are evaluated with a surrogate model, and only a few sample points are reevaluated with the original high fidelity model. Gaussian process regression is a non-intrusive method which is used to build the surrogate model. Without many prerequisites, this gives us not only an approximation of the function value, but also an error indicator that we can use to decide whether a sample point should be reevaluated or not. For two benchmark problems, a dielectrical waveguide and a lowpass filter, the proposed methods outperform classic approaches.

scholarly journals A Gaussian Process Regression approach within a data-driven POD framework for engineering problems in fluid dynamics

2021 ◽  
Vol 4 (3) ◽  
pp. 1-16
Author(s):  
Giulio Ortali ◽  
◽  
Nicola Demo ◽  
Gianluigi Rozza ◽  
Keyword(s):  
Gaussian Process ◽  
Stokes Problem ◽  
Gaussian Process Regression ◽  
Engineering Problem ◽  
Data Driven ◽  
Regression Approach ◽  
Data Driven Approach ◽  
Naval Engineering ◽  
Proper Orthogonal ◽  
Industrial Problem

<abstract><p>This work describes the implementation of a data-driven approach for the reduction of the complexity of parametrical partial differential equations (PDEs) employing Proper Orthogonal Decomposition (POD) and Gaussian Process Regression (GPR). This approach is applied initially to a literature case, the simulation of the Stokes problem, and in the following to a real-world industrial problem, within a shape optimization pipeline for a naval engineering problem.</p></abstract>

scholarly journals Binary black holes, gravitational waves, and numerical relativity

2007 ◽  
Vol 78 ◽  
pp. 012010 ◽  
Author(s):  
Joan M Centrella ◽  
John G Baker ◽  
William D Boggs ◽  
Bernard J Kelly ◽  
Sean T McWilliams ◽  
...  
Keyword(s):  
Black Holes ◽  
Gravitational Waves ◽  
Numerical Relativity ◽  
Binary Black Holes
Sign in / Sign up

Export Citation Format

Share Document