• Bridge damage identification under varying environmental and operational conditions combining Deep Learning and numerical simulations 

  Fernandez-Navamuel, A.; Pardo, D.; Garcia-Sanchez, D.; Zamora-Sánchez, D.; Magalhães, F.; Omella, Ángel J. (2023-10)

  This work proposes a novel supervised learning approach to identify damage in operating bridge structures. We propose a method to introduce the effect of environmental and operational conditions into the synthetic damage ...

• Memory-Based Monte Carlo Integration for Solving Partial Differential Equations Using Neural Networks 

  Uriarte, C.; Taylor, J.M.; Pardo, D.; Rodríguez, O.A.; Vega (2023)

  Monte Carlo integration is a widely used quadrature rule to solve Partial Differential Equations with neural networks due to its ability to guarantee overfitting-free solutions and high-dimensional scalability. However, ...

• Constant probe orientation for fast contact-based inspection of 3D free-form surfaces using (3+2)-axis inspection machines 

  Sliusarenko, O.; Gomez Escudero, G.; Gonzalez, H.; Calleja, A.; Barton, M.; Ortega, N.; López de Lacalle, L.N. (2023)

  A new probe optimization method for contact based (3+2)-axis inspection machines is proposed. Given an inspection path of a stylus on a free-form surface, an optimal orientation of the stylus is computed such that (i) the ...

• Solving Boundary Value Problems Via the Nyström Method Using Spline Gauss Rules 

  Hashemian, A.; Sliusarenko, H.; Remogna, S.; Barrera, D.; Barton, M. (2022)

  We propose to use spline Gauss quadrature rules for solving boundary value problems (BVPs) using the Nyström method. When solving BVPs, one converts the corresponding partial differential equation inside a domain into the ...

• Physics-guided deep-learning inversion method for the interpretation of noisy logging-while-drilling resistivity measurements 

  Noh, K.; Pardo, D.; Torres-Verdín, C. (2023-10)

  Deep learning (DL) inversion is a promising method for real-Time interpretation of logging-while-drilling (LWD) resistivity measurements for well-navigation applications. In this context, measurement noise may significantly ...

• A MULTIDIRECTIONAL DEEP NEURAL NETWORK FOR SELF-SUPERVISED RECONSTRUCTION OF SEISMIC DATA 

  Abedi, M.M.; Pardo, D. (2021)

  Seismic studies exhibit gaps in the recorded data due to surface obstacles. To fill in the gaps with self-supervised deep learning, the network learns to predict different events from the recorded parts of data and then ...

• An exponential integration generalized multiscale finite element method for parabolic problems 

  Contreras, L. F.; Pardo, D.; Abreu, E.; Muñoz-Matute, J.; Diaz, C.; Galvis, J. (2023-04-15)

  We consider linear and semilinear parabolic problems posed in high-contrast multiscale media in two dimensions. The presence of high-contrast multiscale media adversely affects the accuracy, stability, and overall efficiency ...

• Machine learning discovery of optimal quadrature rules for isogeometric analysis 

  Teijeiro, T.; Taylor, J.M.; Hashemian, A.; Pardo, D. (2023-11-01)

  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 consider 1D spline spaces of arbitrary ...

• An Error-Based Approximation Sensing Circuit for Event-Triggered Low-Power Wearable Sensors 

  Zanoli, S.; Ponzina, F.; Teijeiro, T.; Levisse, A.; Atienza, D. (2023-04-24)

  Event-based sensors have the potential to optimize energy consumption at every stage in the signal processing pipeline, including data acquisition, transmission, processing, and storage. However, almost all state-of-the-art ...

• Diagnosis of the health status of mooring systems for floating offshore wind turbines using autoencoders 

  Gorostidi, N.; Pardo, D.; Nava, V. (2023-11-01)

  Floating offshore wind turbines (FOWTs) show promise in terms of energy production, availability, and sustainability, but remain unprofitable due to high maintenance costs. This work proposes a deep learning algorithm to ...

• Predictive Maintenance of Floating Offshore Wind Turbine Mooring Lines using Deep Neural Networks 

  Gorostidi, N.; Nava, V.; Aristondo, A.; Pardo, D. (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 ...

• Geometric Hermite interpolation by rational curves of constant width 

  Arnal, A.; Beltran, J. V.; Monterde, J.; Rochera, D. (2023-09-28)

  A constructive characterization of the support function for a rationally parameterized curve of constant width is given. In addition, a Hermite interpolation problem for such kind of curves is solved, which yields a method ...

• A Deep Fourier Residual Method for solving PDEs using Neural Networks 

  Taylor, J.M.; Pardo, D.; Muga, I. (2022)

  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 the error. In multiple problems, this ...

• Cloud benchmarking and performance analysis of an HPC application in Amazon EC2 

  Dancheva, T.; Alonso, U.; Barton, M. (2023)

  Cloud computing platforms have been continuously evolving. Features such as the Elastic Fabric Adapter (EFA) in the Amazon Web Services (AWS) platform have brought yet another revolution in the High Performance Computing ...

• On inverse construction of isoptics and isochordal-viewed curves 

  Rochera, D.; Barton, M. (2023)

  Given a regular closed curve α in the plane, a $\phi$-isoptic of $\alpha$ is a locus of points from which pairs of tangent lines to $\alpha$ span a fixed angle $\phi$. If, in addition, the chord that connects the two points ...

• Constant probe orientation for fast contact-based inspection of 3D free-form surfaces using (3+2)-axis inspection machines 

  Sliusarenko, O.; Gomez Escudero, G.; Gonzalez, H.; Calleja, A.; Barton, M.; Ortega, N.; Lopez de Lacalle, N. (2023)

  A new probe optimization method for contact based (3+2)-axis inspection machines is proposed. Given an inspection path of a stylus on a free-form surface, an optimal orientation of the stylus is computed such that (i) the ...

• Towards $G^1$-continuous multi-strip path-planning for 5-axis flank CNC machining of free-form surfaces using conical cutting tools 

  Rajain, K.; Bizzarri, M.; Lavicka, M.; Kosinka, J.; Barton, M. (2023-06)

  Existing flank milling path-planning methods typically lead to tiny gaps or overlaps between neighboring paths, which causes artifacts and imperfections in the workpiece. We propose a new multi-strip path-planning method ...

• Numerical quadrature for Gregory quads 

  Zhou, J.; Barendrecht, P.; Barton, M.; Kosinka, J. (2023)

  We investigate quadrature rules in the context of quadrilateral Gregory patches, in short Gregory quads. We provide numerical and where possible symbolic quadrature rules for the space spanned by the twenty polynomial/rational ...

• Solving boundary value problems via the Nyström method using spline Gauss rules 

  Hashemian, A.; Sliusarenko, H.; Remogna, S.; Barrera, D.; Barton, M. (2023-08-01)

  We propose to use spline Gauss quadrature rules for solving boundary value problems (BVPs) using the Nyström method. When solving BVPs, one converts the corresponding partial differential equation inside a domain into the ...

• A Multidirectional Deep Neural Network for Self-Supervised Reconstruction of Seismic Data 

  Abedi, M.M.; Pardo, D. (2022-12-06)

  Seismic studies exhibit gaps in the recorded data due to surface obstacles. To fill in the gaps with self-supervised deep learning, the network learns to predict different events from the recorded parts of data and then ...

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