University of Texas at Austin-ko ikertzaileekin lankidetzan egindako argitalpenak (65)

2024

  1. Robust Variational Physics-Informed Neural Networks

    Computer Methods in Applied Mechanics and Engineering, Vol. 425

2023

  1. A Deep Double Ritz Method (D2RM) for solving Partial Differential Equations using Neural Networks

    Computer Methods in Applied Mechanics and Engineering, Vol. 405

  2. An exponential integration generalized multiscale finite element method for parabolic problems

    Journal of Computational Physics, Vol. 479

  3. Fast parallel IGA-ADS solver for time-dependent Maxwell's equations

    Computers and Mathematics with Applications, Vol. 151, pp. 36-49

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

    Geophysical Journal International, Vol. 235, Núm. 1, pp. 150-165

2022

  1. 2.5-D Deep Learning Inversion of LWD and Deep-Sensing em Measurements Across Formations with Dipping Faults

    IEEE Geoscience and Remote Sensing Letters, Vol. 19

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

    International Journal for Numerical Methods in Engineering, Vol. 123, Núm. 9, pp. 2142-2161

  3. Real-Time 2.5D Inversion of LWD Resistivity Measurements Using Deep Learning for Geosteering Applications Across Faulted Formations

    Petrophysics, Vol. 63, Núm. 4, pp. 506-518

2021

  1. A DPG-based time-marching scheme for linear hyperbolic problems

    Computer Methods in Applied Mechanics and Engineering, Vol. 373

  2. Deep Learning Driven Self-adaptive Hp Finite Element Method

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  3. Equivalence between the DPG method and the exponential integrators for linear parabolic problems

    Journal of Computational Physics, Vol. 429

  4. Error control and loss functions for the deep learning inversion of borehole resistivity measurements

    International Journal for Numerical Methods in Engineering, Vol. 122, Núm. 6, pp. 1629-1657

  5. Uncertainty Quantification on the Inversion of Geosteering Measurements using Deep Learning

    3rd EAGE/SPE Geosteering Workshop

2020

  1. A deep learning approach to the inversion of borehole resistivity measurements

    Computational Geosciences, Vol. 24, Núm. 3, pp. 971-994

2019

  1. Recent advances on the inversion of deep directional borehole resistivity measurements

    Exploration Geophysics, Vol. 2019, Núm. 1

2018

  1. Fast 2.5D finite element simulations of borehole resistivity measurements

    Computational Geosciences, Vol. 22, Núm. 5, pp. 1271-1281

2017

  1. 1.5D based inversion of logging-while-drilling resistivity measurements in 3D formations

    79th EAGE Conference and Exhibition 2017

  2. Fast inversion of logging-while-drilling resistivity measurements acquired in multiple wells

    Geophysics, Vol. 82, Núm. 3, pp. E111-E120

  3. Fast simulation of 2.5D LWD resistivity tools

    79th EAGE Conference and Exhibition 2017

2015

  1. Fast 1D inversion of logging-while-drilling resistivity measurements for improved estimation of formation resistivity in high-angle and horizontal wells

    Geophysics, Vol. 80, Núm. 2, pp. E111-E124