Search Results for author:
Found 7 papers, 2 papers with code
Reducing Spatial Discretization Error on Coarse CFD Simulations Using an OpenFOAM-Embedded Deep Learning Framework
We propose a method for reducing the spatial discretization error of coarse computational fluid dynamics (CFD) problems by enhancing the quality of low-resolution simulations using a deep learning model fed with high-quality data.
Residual-based Attention Physics-informed Neural Networks for Efficient Spatio-Temporal Lifetime Assessment of Transformers Operated in Renewable Power Plants
PINN based oil temperature predictions are used to estimate spatio-temporal transformer winding temperature values, which are validated through PDE resolution models and fiber optic sensor measurements, respectively.
Machine Learning Discovery of Optimal Quadrature Rules for Isogeometric Analysis
The quadrature rule search is posed as an optimization problem and solved by a machine learning strategy based on gradient-descent.
A Deep Double Ritz Method (D$^2$RM) for solving Partial Differential Equations using Neural Networks
To overcome this, we reformulate the residual minimization as an equivalent minimization of a Ritz functional fed by optimal test functions computed from another Ritz functional minimization.
$r-$Adaptive Deep Learning Method for Solving Partial Differential Equations
We introduce an $r-$adaptive algorithm to solve Partial Differential Equations using a Deep Neural Network.
Deep-Learning Inversion Method for the Interpretation of Noisy Logging-While-Drilling Resistivity Measurements
Deep Learning (DL) inversion is a promising method for real time interpretation of logging while drilling (LWD) resistivity measurements for well navigation applications.
Modeling extra-deep electromagnetic logs using a deep neural network
We present a methodology to construct a deep neural network (DNN) model trained to reproduce a full set of extra-deep EM logs consisting of 22 measurements per logging position.
