6 papers
PyCC.id: A package for hypothesis-driven equation discovery with structural identifiability
Federico J. Gonzalez
Data-driven equation discovery is fundamentally an inverse problem that seeks to infer the governing differential equations of a system directly from time-series measurements. A kn…
Integrating prior knowledge in equation discovery: Interpretable symmetry-informed neural networks and symbolic regression via characteristic curves
Federico J. Gonzalez
Data-driven equation discovery aims to reconstruct governing equations directly from empirical observations. A fundamental challenge in this domain is the ill-posed nature of the i…
Interplay between electronic and phononic energy dissipation channels in the adsorption of CO on Cu(110)
Carmen A. Tachino, Federio J. Gonzalez, Alberto S. Muzas +3
In this work, we investigate the relative importance of electronic and phononic energy dissipation during the molecular adsorption of CO on Cu(110). Initial sticking probabilities…
CO Dissociative Sticking on Cu(110)
Federico J. Gonzalez, Carmen A. Tachino, H. Fabio Busnengo
In this work we investigate the dissociation of CO on Cu(110) by performing density functional theory calculations using the vdW-DF2 exchange-correlation functional, with a pot…
Interpretable neural network system identification method for two families of second-order systems based on characteristic curves
Federico J. Gonzalez, Luis P. Lara
Nonlinear system identification often involves a fundamental trade-off between interpretability and flexibility, often requiring the incorporation of physical constraints. We propo…
System identification based on characteristic curves: a mathematical connection between power series and Fourier analysis for first-order nonlinear systems
Federico Javier Gonzalez
Recently, the sinosoidal output response in power series (SORPS) formalism was presented for system identification and simulation. Based on the concept of characteristic curves (CC…