4 citations · 12 across the 6 of their papers we have counts for
6 papers
Robust Variational Physics-Informed Neural Networks
Sergio Rojas, Paweł Maczuga, Judit Muñoz-Matute +2
We introduce a Robust version of the Variational Physics-Informed Neural Networks method (RVPINNs). As in VPINNs, we define the quadratic loss functional in terms of a Petrov-Galer…
Deep Fourier Residual method for solving time-harmonic Maxwell's equations
Jamie M. Taylor, Manuela Bastidas, David Pardo +1
Solving PDEs with machine learning techniques has become a popular alternative to conventional methods. In this context, Neural networks (NNs) are among the most commonly used mach…
Machine Learning Discovery of Optimal Quadrature Rules for Isogeometric Analysis
Tomas Teijeiro, Jamie M. Taylor, Ali Hashemian +1
We propose the use of machine learning techniques to find optimal quadrature rules for the construction of stiffness and mass matrices in isogeometric analysis (IGA). We initially…
A Deep Fourier Residual Method for solving PDEs using Neural Networks
Jamie M. Taylor, David Pardo, Ignacio Muga
When using Neural Networks as trial functions to numerically solve PDEs, a key choice to be made is the loss function to be minimised, which should ideally correspond to a norm of…
Automated machine learning for borehole resistivity measurements
M. Shahriari, D. Pardo, S. Kargaran +1
Deep neural networks (DNNs) offer a real-time solution for the inversion of borehole resistivity measurements to approximate forward and inverse operators. It is possible to use ex…
Database Generation for Deep Learning Inversion of 2.5D Borehole Electromagnetic Measurements using Refined Isogeometric Analysis
Ali Hashemian, Daniel Garcia, Jon Ander Rivera +1
Borehole resistivity measurements are routinely inverted in real-time during geosteering operations. The inversion process can be efficiently performed with the help of advanced ar…