[1] A. Chakraborty, M. Croci, J. Muñoz-Matute. Petrov-Galerkin Neural Networks.
[2] J. M. Taylor, D. Pardo, J. Muñoz-Matute. Regularity-conforming Neural Networks (ReCoNNS) for solving Partial Differential Equations.
[3] M. Pabisz, J. Muñoz-Matute, M. Paszynki. Augmenting MRI scan data of glioblastoma brain tumor with real-time predictions of tumor evolution using exponential integrators method.
[4] J. Muñoz-Matute, A. Chakraborty, L. Demkowicz. On adjoint problems for DPG time-marching schemes.
[5] J. Muñoz-Matute, L. Demkowicz. Multistage DPG time-marching scheme for nonlinear problems. Submitted to SIAM Journal for Numerical Analysis in August 2023. https://doi.org/10.48550/arXiv.2309.00069
[6] S. Rojas, P. Mazuga, J. Muñoz-Matute, D. Pardo, M. Paszynski. Robust Variational Physics-Informed Neural Networks. Submitted to Computer Methods in Applied Mechanics and Engineering in December 2023. https://doi.org/10.48550/arXiv.2308.16910
[7] L. F. Contreras, D. Pardo, E. Abreu, J. Muñoz-Matute, C. Diaz, J. Galvis. An exponential integration generalized multiscale finite element method for parabolic problems. Journal of Computational Physics, 2023, vol. 479, pp. 112014. https://doi.org/10.1016/j.jcp.2023.112014
[8] M. Croci, J. Muñoz-Matute. Exploiting Kronecker structure in exponential integrators: fast approximation of the action of φ-functions via quadrature. Journal of Computational Science, 2023, vol. 67, pp. 101966. https://doi.org/10.1016/j.jocs.2023.101966
[9] C. Uriarte, D. Pardo, I. Muga, J. Muñoz-Matute, A deep double Ritz method for solving partial differential equations. Computer Methods in Applied Mechanics and Engineering, 2023, vol. 405, pp. 115892. https://doi.org/10.1016/j.cma.2023.115892
[10] J. Muñoz-Matute, L. Demkowicz, N. V. Roberts. Combining DPG in space with DPG time-marching scheme for the transient advection-reaction equation. Computer Methods in Applied Mechanics and Engineering, 2022, vol. 402, pp. 115471. https://doi.org/10.1016/j.cma.2022.115471
[11] L. Demkowicz, N. V. Roberts, J. Muñoz-Matute. The DPG method for the convection-reaction problem, revisited. Computational Methods in Applied Mathematics, 2022, vol. 23, no. 1, pp. 93-125. https://doi.org/10.1515/cmam-2021-0149
[12] J. Muñoz-Matute, D. Pardo, V. M. Calo. Exploiting the Kronecker product structure of φ−functions with applications to exponential time integrators. International Journal for Numerical Methods in Engineering, 2022, vol. 123, no. 9, pp. 2142-2161. https://doi.org/10.1002/nme.6929
[13] J. Muñoz-Matute, L. Demkowicz, D. Pardo. Error representation of the time-marching DPG scheme. Computer Methods in Applied Mechanics and Engineering, 2022, vol. 391, pp. 114480. https://doi.org/10.1016/j.cma.2021.114480
[14] J. Muñoz-Matute, D. Pardo, L. Demkowicz. A DPG-based time-marching scheme for linear hyperbolic problems. Computer Methods in Applied Mechanics and Engineering, 2021, vol. 373, pp. 113539. https://doi.org/10.1016/j.cma.2020.113539
[15] J. Muñoz-Matute, D. Pardo, L. Demkowicz. Equivalence between the DPG method and the exponential integrators for linear parabolic problems. Journal of Computational Physics, 2021, vol. 429, pp. 110016. https://doi.org/10.1016/j.jcp.2020.110016
[16] M. Los, J. Muñoz-Matute, I. Muga, M. Paszynski. Isogeometric Residual Minimization Method (iGRM) for Stokes and Navier-Stokes problems. Computers & Mathematics with Applications, 2021, vol. 95, pp. 200-214. https://doi.org/10.1016/j.camwa.2020.11.013
[17] M. Los, J. Muñoz-Matute, K. Podsiadlo, M. Paszynski, K. Pingali. Parallel shared-memory Isogeometric Residual Minimization (iGRM) for three-dimensional advection-diffusion problems. Lecture Notes in Computer Science, 2020, vol. 12143, pp. 133-148. https://doi.org/10.1007/978-3-030-50436-6-10
[18] M. Los, J. Muñoz-Matute, I. Muga, M. Paszynski. Isogeometric Residual Minimization Method (iGRM) with direction splitting for non-stationary advection-diffusion problems. Computers & Mathematics with Applications, 2020, vol. 79, p. 213-229. https://doi.org/10.1016/j.camwa.2019.06.023
[19] J. Muñoz-Matute, D. Pardo, V. M. Calo, E. Alberdi. Variational formulations for explicit Runge-Kutta methods. Finite Elements in Analysis & Design, 2019, vol. 165, p. 77-93. https://doi.org/10.1016/j.finel.2019.06.007
[20] J. Muñoz-Matute, V. M. Calo, D. Pardo, E. Alberdi, K. G. van der Zee. Explicit-in-time goal-oriented adaptivity. Computer Methods in Applied Mechanics and Engineering, 2019, vol. 347, p. 176-200. https://doi.org/10.1016/j.cma.2018.12.028
[21] J. Muñoz-Matute, D. Pardo, V. M. Calo, E. Alberdi. Forward-in-time goal-oriented adaptivity. International Journal for Numerical Methods in Engineering, 2019, vol. 119, p. 490-505. https://doi.org/10.1002/nme.6059
[22] J. Muñoz-Matute, E. Alberdi, D. Pardo, V. M. Calo. Time-domain goal-oriented adaptivity using pseudo-dual error representations. Computer Methods in Applied Mechanics and Engineering, 2017, vol. 325, p. 395-415. https://doi.org/10.1016/j.cma.2017.06.037