Outputs (26)

Generalized Swift–Hohenberg and phase-field-crystal equations based on a second-gradient phase-field theory (2020)
Journal Article
Espath, L., Calo, V. M., & Fried, E. (2020). Generalized Swift–Hohenberg and phase-field-crystal equations based on a second-gradient phase-field theory. Meccanica, 55(10), 1853-1868. https://doi.org/10.1007/s11012-020-01228-9

The principle of virtual power is used derive a microforce balance for a second-gradient phase-field theory. In conjunction with constitutive relations consistent with a free-energy imbalance, this balance yields a broad generalization of the Swift–H... Read More about Generalized Swift–Hohenberg and phase-field-crystal equations based on a second-gradient phase-field theory.

Multilevel double loop Monte Carlo and stochastic collocation methods with importance sampling for Bayesian optimal experimental design (2020)
Journal Article
Beck, J., Mansour Dia, B., Espath, L., & Tempone, R. (2020). Multilevel double loop Monte Carlo and stochastic collocation methods with importance sampling for Bayesian optimal experimental design. International Journal for Numerical Methods in Engineering, 121(15), 3482-3503. https://doi.org/10.1002/nme.6367

An optimal experimental set-up maximizes the value of data for statistical inferences. The efficiency of strategies for finding optimal experimental set-ups is particularly important for experiments that are time-consuming or expensive to perform. In... Read More about Multilevel double loop Monte Carlo and stochastic collocation methods with importance sampling for Bayesian optimal experimental design.

Nesterov-aided stochastic gradient methods using Laplace approximation for Bayesian design optimization (2020)
Journal Article
Gustavo Carlon, A., Mansour Dia, B., Espath, L., Holdorf Lopez, R., & Tempone, R. (2020). Nesterov-aided stochastic gradient methods using Laplace approximation for Bayesian design optimization. Computer Methods in Applied Mechanics and Engineering, 363, Article 112909. https://doi.org/10.1016/j.cma.2020.112909

Finding the best setup for experiments is the primary concern for Optimal Experimental Design (OED). Here, we focus on the Bayesian experimental design problem of finding the setup that maximizes the Shannon expected information gain. We use the stoc... Read More about Nesterov-aided stochastic gradient methods using Laplace approximation for Bayesian design optimization.

A NURBS-based finite element formulation for incompressible fluid dynamics and fluid-structure interaction with rigid bodies (2019)
Journal Article
Tonon, P., Tonin, M. G., Espath, L. F., & Braun, A. L. (2020). A NURBS-based finite element formulation for incompressible fluid dynamics and fluid-structure interaction with rigid bodies. Latin American Journal of Solids and Structures, 17(1), Article e242. https://doi.org/10.1590/1679-78255772

A numerical investigation is performed here using a NURBS-based finite element formulation applied to classical Computational Fluid Dynamics (CFD) and Fluid-Structure Interaction (FSI) problems. Model capabilities related to refinement techniques are... Read More about A NURBS-based finite element formulation for incompressible fluid dynamics and fluid-structure interaction with rigid bodies.

Reactive n-species Cahn–Hilliard system: A thermodynamically-consistent model for reversible chemical reactions (2018)
Journal Article
Clavijo, S., Sarmiento, A., Espath, L., Dalcin, L., Cortes, A., & Calo, V. (2019). Reactive n-species Cahn–Hilliard system: A thermodynamically-consistent model for reversible chemical reactions. Journal of Computational and Applied Mathematics, 350, 143-154. https://doi.org/10.1016/j.cam.2018.10.007

We introduce a multicomponent Cahn–Hilliard system with multiple reversible chemical reactions. We derive the conservation laws of the multicomponent system within the thermodynamical constraints. Furthermore, we consider multiple chemical reactions... Read More about Reactive n-species Cahn–Hilliard system: A thermodynamically-consistent model for reversible chemical reactions.

Fast Bayesian experimental design: Laplace-based importance sampling for the expected information gain (2018)
Journal Article
Beck, J., Dia, B. M., Espath, L. F., Long, Q., & Tempone, R. (2018). Fast Bayesian experimental design: Laplace-based importance sampling for the expected information gain. Computer Methods in Applied Mechanics and Engineering, 334, 523-553. https://doi.org/10.1016/j.cma.2018.01.053

In calculating expected information gain in optimal Bayesian experimental design, the computation of the inner loop in the classical double-loop Monte Carlo requires a large number of samples and suffers from underflow if the number of samples is sma... Read More about Fast Bayesian experimental design: Laplace-based importance sampling for the expected information gain.