• Time-adaptive Adomian decomposition-based numerical scheme for Euler equations 

  Garcia, I.; Remaki, L. (2022-04-21)

  Time efficiency is one of the more critical concerns in computational fluid dynamics simulations of industrial applications. Extensive research has been conducted to improve the underlying numerical schemes to achieve time ...

• An implicit symplectic solver for high-precision long term integrations of the Solar System 

  Antoñana, M.; Alberdi, E.; Makazaga, J.; Murua, A. (2022)

  We present FCIRK16, a 16th-order implicit symplectic integrator for long-term high precision Solar System simulations. Our integrator takes advantage of the near-Keplerian motion of the planets around the Sun by ...

• Efficient 5-axis CNC trochoidal flank milling of 3D cavities using custom-shaped cutting tools 

  Bo, P.; Fan, H.; Barton, M. (2022-05)

  A novel method for trochoidal flank milling of 3D cavities bounded by free-form surfaces is proposed. Existing 3D trochoidal milling methods use on-market milling tools whose shape is typically cylindrical or conical, and ...

• Nonhyperbolic normal moveout stretch correction with deep learning automation 

  Abedi, M.M.; Pardo, D. (2022-02-15)

  Normal-moveout (NMO) correction is a fundamental step in seismic data processing. It consists of mapping seismic data from recorded traveltimes to corresponding zero-offset times. This process produces wavelet stretching ...

• Exploiting the Kronecker product structure of φ−functions in exponential integrators 

  Muñoz-Matute, J.; Pardo, D.; Calo, Victor M. (2022-05-15)

  Exponential time integrators are well-established discretization methods for time semilinear systems of ordinary differential equations. These methods use (Formula presented.) functions, which are matrix functions related ...

• Supervised Deep Learning with Finite Element simulations for damage identification in bridges 

  Fernandez-Navamuel, A.; Zamora-Sánchez, Diego; Omella, Ángel J.; Pardo, D.; Garcia-Sanchez, David; Magalhães, Filipe (2022-04-15)

  This work proposes a supervised Deep Learning approach for damage identification in bridge structures. We employ a hybrid methodology that incorporates Finite Element simulations to enrich the training phase of a Deep ...

• Algebraic equations for constant width curves and Zindler curves 

  Rochera, D. (2022-03)

  An explicit method to compute algebraic equations of curves of constant width and Zindler curves generated by a family of middle hedgehogs is given thanks to a property of Chebyshev polynomials. This extends the methodology ...

• Curve-guided 5-axis CNC flank milling of free-form surfaces using custom-shaped tools 

  Rajain, K.; Sliusarenko, O.; Bizzarri, M.; Barton, M. (2022-03)

  A new method for 5-axis flank milling of free-form surfaces is proposed. Existing flank milling path-planning methods typically use on-market milling tools whose shape is cylindrical or conical, and is therefore not ...

• 2.5-D Deep Learning Inversion of LWD and Deep-Sensing em Measurements Across Formations with Dipping Faults 

  Noh, Kyubo; Pardo, D.; Torres-Verdin, Carlos (2022-01-01)

  Deep learning (DL) inversion of induction logging measurements is used in well geosteering for real-time imaging of the distribution of subsurface electrical conductivity. We develop a DL inversion workflow to solve 2.5-D ...

• On quadrature rules for solving Partial Differential Equations using Neural Networks 

  Rivera, J.A; Taylor, J.M.; Omella, Ángel J.; Pardo, D. (2022-04-01)

  Neural Networks have been widely used to solve Partial Differential Equations. These methods require to approximate definite integrals using quadrature rules. Here, we illustrate via 1D numerical examples the quadrature ...

• Uncertainty Quantification on the Inversion of Geosteering Measurements using Deep Learning 

  Rivera, J.A; Shahriari, M; Pardo, D.; Omella, J.; Torres-Verdín, C. (2021-11-01)

  We propose the use of a Deep Learning (DL) algorithm for the real-time inversion of electromagnetic measurements acquired during geosteering operations. Moreover, we show that when the DL algorithm is equipped with a ...

• On numerical solution of Fredholm and Hammerstein integral equations via Nystr\"{o}m method and Gaussian quadrature rules for splines 

  Barrera, D.; Barton, M.; Chiarella, A.; Remogna, S. (2022-01)

  Nystr\"{o}m method is a standard numerical technique to solve Fredholm integral equations of the second kind where the integration of the kernel is approximated using a quadrature formula. Traditionally, the quadrature ...

• Deep learning enhanced principal component analysis for structural health monitoring 

  Fernandez-Navamuel, A.; Magalhães, Filipe; Zamora-Sánchez, Diego; Omella, Ángel J.; Garcia-Sanchez, David; Pardo, D. (2022-01-01)

  This paper proposes a Deep Learning Enhanced Principal Component Analysis (PCA) approach for outlier detection to assess the structural condition of bridges. We employ partially explainable autoencoder architecture to ...

• Error representation of the time-marching DPG scheme 

  Muñoz-Matute, J.; Demkowicz, Leszek; Pardo, D. (2022-03-01)

  In this article, we introduce an error representation function to perform adaptivity in time of the recently developed time-marching Discontinuous Petrov–Galerkin (DPG) scheme. We first provide an analytical expression for ...

• A Finite Element based Deep Learning solver for parametric PDEs 

  Uriarte, C.; Pardo, D.; Omella, A. J. (2021)

  We introduce a dynamic Deep Learning (DL) architecture based on the Finite Element Method (FEM) to solve linear parametric Partial Differential Equations(PDEs). The connections between neurons in the architecture mimic the ...

• Numerical Approaches for Loads and Motions Assessment of Floating WECs Moored by Means of Catenary Mooring Systems 

  Touzon, I.; Petuya, V.; Nava, V.; Alonso-Reig, M.; Mendikoa, I. (2022)

  Technologies for harvesting offshore renewable energy based on float- ing platforms, such as offshore wind, wave and tidal energies, are currently being developed with the purpose of achieving a competitive cost of energy. ...

• Modeling extra-deep electromagnetic logs using a deep neural network 

  Alyaev, S.; Shahriari, M.; Pardo, D.; Omella, A. J.; Larsen, D.; Jahani, N.; Suter, E. (2021-05)

  Modern geosteering is heavily dependent on real-time interpretation of deep electromagnetic (EM) measurements. We have developed a methodology to construct a deep neural network (DNN) model trained to reproduce a full set ...

• Motile dislocations knead odd crystals into whorls 

  Bililign, E. S.; Balboa, F.; Ganan, Y. A.; Poncet, A.; Soni, V.; Magkiriadou, S.; Shelley, M.J.; Bartolo, D.; Irvine, W.T.M. (2021-01-01)

  The competition between thermal fluctuations and potential forces governs the stability of matter in equilibrium, in particular the proliferation and annihilation of topological defects. However, driving matter out of ...

• Majorant series for the N-body problem 

  Antoñana, M.; Chartier, P.; Murua, A. (2021-01-01)

  As a follow-up of a previous work of the authors, this work considers uniform global time-renormalization functions for the gravitationalN-body problem. It improves on the estimates of the radii of convergence obtained ...

• Global Time-Renormalization of the Gravitational N-body Problem 

  Antoñana, M.; Chartier, P.; Makazaga, J.; Murua, A. (2021-01-01)

  This work considers the gravitational N-body problem and introduces global time-renormalization functions that allow the efficient numerical integration with fixed time-steps. First, a lower bound of the radius of convergence ...

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