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Predictive Maintenance of Floating Offshore Wind Turbine Mooring Lines using Deep Neural Networks
(2022)
The recent massive deployment of onshore wind farms has caused controversy to arise mainly around the issues of land occupation, noise and visual pollution and impact on wildlife. Fixed offshore turbines, albeit beneficial ...
Error Control and Loss Functions for the Deep Learning Inversion of Borehole Resistivity Measurements
(2020-11)
Deep learning (DL) is a numerical method that approximates functions. Recently, its use has become attractive for the simulation and inversion of multiple problems in computational mechanics, including the inversion of ...
Design of Loss Functions for Solving Inverse Problems using Deep Learning
(2020-05)
Solving inverse problems is a crucial task in several applications that strongly a ffect our daily lives, including multiple engineering fields, military operations, and/or energy production. There exist different methods ...
Error Control and Loss Functions for the Deep Learning Inversion of Borehole Resistivity Measurements
(2020-05)
Deep learning (DL) is a numerical method that approximates functions. Recently, its use has become attractive for the simulation and inversion of multiple problems in computational mechanics, including the inversion of ...
A Deep Learning Approach to the Inversion of Borehole Resistivity Measurements
(2020-04)
Borehole resistivity measurements are routinely employed to measure the electrical properties of rocks penetrated by a well and to quantify the hydrocarbon pore volume of a reservoir. Depending on the degree of geometrical ...
A Deep Neural Network as Surrogate Model for Forward Simulation of Borehole Resistivity Measurements
(2020-01)
Inverse problems appear in multiple industrial applications. Solving such inverse problems require the repeated solution of the forward problem. This is the most time-consuming stage when employing inversion techniques, ...