Two- and three-dimensional Direct Numerical Simulation of particle-laden gravity currents. - L. F. R. Espath; L. C. Pinto; Sylvain Laizet; Jorge Hugo Silvestrini (Comput. Geosci., 2014)

Reynolds number and settling velocity influence for finite-release particle-laden gravity currents in a basin. - E. P. Francisco; L. F. R. Espath; Sylvain Laizet; Jorge Hugo Silvestrini (Comput. Geosci., 2018)

An energy-stable generalized-α method for the Swift-Hohenberg equation. - A. F. Sarmiento; L. F. R. Espath; Philippe A. Vignal; Lisandro Dalcín; Matteo Parsani; Victor M. Calo (J. Comput. Appl. Math., 2018)

Reactive n-species Cahn-Hilliard system: A thermodynamically-consistent model for reversible chemical reactions. - S. P. Clavijo; A. F. Sarmiento; L. F. R. Espath; Lisandro Dalcín; Adriano M. A. Côrtes; Victor M. Calo (J. Comput. Appl. Math., 2019)

On the equivalence of different adaptive batch size selection strategies for stochastic gradient descent methods. - Luis Espath; Sebastian Krumscheid; Raúl Tempone; Pedro Vilanova (CoRR, 2021)

A spatio-temporal adaptive phase-field fracture method. - Nicolas A. Labanda; Luis Espath; Victor Manuel Calo (CoRR, 2021)

Small-noise approximation for Bayesian optimal experimental design with nuisance uncertainty. - Arved Bartuska; Luis Espath; Raúl Tempone (CoRR, 2021)

Deep nurbs - admissible neural networks. - Hamed Saidaoui; Luis Espath; Raúl Tempone (CoRR, 2022)

Physics-informed Spectral Learning: the Discrete Helmholtz-Hodge Decomposition. - Luis Espath; Pouria Behnoudfar; Raúl Tempone (CoRR, 2023)

Double-loop quasi-Monte Carlo estimator for nested integration. - Arved Bartuska; André Gustavo Carlon; Luis Espath; Sebastian Krumscheid; Raúl Tempone (CoRR, 2023)

Multilevel randomized quasi-Monte Carlo estimator for nested integration. - Arved Bartuska; André Gustavo Carlon; Luis Espath; Sebastian Krumscheid; Raúl Tempone (CoRR, 2024)

Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in stochastic optimization. - André Gustavo Carlon; Luis Espath; Raúl Tempone (Optim. Methods Softw., 2024)

Deep NURBS - admissible physics-informed neural networks. - Hamed Saidaoui; Luis Espath; Raúl Tempone (Eng. Comput., 2024)

Scalable Method for Bayesian Experimental Design without Integrating over Posterior Distribution. - Vinh Hoang; Luis Espath; Sebastian Krumscheid; Raúl Tempone (SIAM/ASA J. Uncertain. Quantification, 2025)

Laplace-based strategies for Bayesian optimal experimental design with nuisance uncertainty. - Arved Bartuska; Luis Espath; Raúl Tempone (Stat. Comput., 2025)