All Outputs (4)

Controlling ZIF-67 film properties in water-based cathodic electrochemical deposition (2024)
Journal Article
Elsayed, E., Brevis, I., Pandiyan, S., Wildman, R., van der Zee, K. G., & Tokay, B. (2024). Controlling ZIF-67 film properties in water-based cathodic electrochemical deposition. Journal of Solid State Chemistry, 338, Article 124820. https://doi.org/10.1016/j.jssc.2024.124820

One of the main approaches to increase the surface area of a substrate is through depositing a film of a porous materials such as Zeolite imidazole framework (ZIF). ZIF films have shown surpassing capabilities because of their zeolite-like features,... Read More about Controlling ZIF-67 film properties in water-based cathodic electrochemical deposition.

Learning quantities of interest from parametric PDEs: An efficient neural-weighted Minimal Residual approach (2024)
Journal Article
Brevis, I., Muga, I., Pardo, D., Rodriguez, O., & van der Zee, K. G. (2024). Learning quantities of interest from parametric PDEs: An efficient neural-weighted Minimal Residual approach. Computers and Mathematics with Applications, 164, 139-149. https://doi.org/10.1016/j.camwa.2024.04.006

The efficient approximation of parametric PDEs is of tremendous importance in science and engineering. In this paper, we show how one can train Galerkin discretizations to efficiently learn quantities of interest of solutions to a parametric PDE. The... Read More about Learning quantities of interest from parametric PDEs: An efficient neural-weighted Minimal Residual approach.

Neural Control of Discrete Weak Formulations: Galerkin, Least Squares & Minimal-Residual Methods with Quasi-Optimal Weights (2022)
Journal Article
Brevis, I., Muga, I., & van der Zee, K. G. (2022). Neural Control of Discrete Weak Formulations: Galerkin, Least Squares & Minimal-Residual Methods with Quasi-Optimal Weights. Computer Methods in Applied Mechanics and Engineering, 402, Article 115716. https://doi.org/10.1016/j.cma.2022.115716

There is tremendous potential in using neural networks to optimize numerical methods. In this paper, we introduce and analyse a framework for the neural optimization of discrete weak formulations, suitable for finite element methods. The main idea of... Read More about Neural Control of Discrete Weak Formulations: Galerkin, Least Squares & Minimal-Residual Methods with Quasi-Optimal Weights.

A machine-learning minimal-residual (ML-MRes) framework for goal-oriented finite element discretizations (2020)
Journal Article
Brevis, I., Muga, I., & van der Zee, K. G. (2021). A machine-learning minimal-residual (ML-MRes) framework for goal-oriented finite element discretizations. Computers and Mathematics with Applications, 95, 186-199. https://doi.org/10.1016/j.camwa.2020.08.012

We introduce the concept of machine-learning minimal-residual (ML-MRes) finite element discretizations of partial differential equations (PDEs), which resolve quantities of interest with striking accuracy, regardless of the underlying mesh size. The... Read More about A machine-learning minimal-residual (ML-MRes) framework for goal-oriented finite element discretizations.