Julia Novo

1.5k total citations
65 papers, 1.0k citations indexed

About

Julia Novo is a scholar working on Computational Mechanics, Computational Theory and Mathematics and Numerical Analysis. According to data from OpenAlex, Julia Novo has authored 65 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 56 papers in Computational Mechanics, 23 papers in Computational Theory and Mathematics and 22 papers in Numerical Analysis. Recurrent topics in Julia Novo's work include Advanced Numerical Methods in Computational Mathematics (55 papers), Computational Fluid Dynamics and Aerodynamics (26 papers) and Advanced Mathematical Modeling in Engineering (22 papers). Julia Novo is often cited by papers focused on Advanced Numerical Methods in Computational Mathematics (55 papers), Computational Fluid Dynamics and Aerodynamics (26 papers) and Advanced Mathematical Modeling in Engineering (22 papers). Julia Novo collaborates with scholars based in Spain, Germany and United States. Julia Novo's co-authors include Javier de Frutos, Bosco Garcı́a-Archilla, Volker John, Edriss S. Titi, M. P. Calvo, Petr Knobloch, Blanca Ayuso de Dios, Samuele Rubino, Shannon Wynne and Gabriel R. Barrenechea and has published in prestigious journals such as Journal of Computational Physics, Computer Methods in Applied Mechanics and Engineering and Mathematics of Computation.

In The Last Decade

Julia Novo

59 papers receiving 986 citations

Peers

Julia Novo
Charalambos Makridakis Greece
Walter Zulehner Austria
Nianyu Yi China
Qingsong Zou China
Slimane Adjerid United States
Gerhard Starke Germany
Petr Knobloch Czechia
Etienne Emmrich Germany
Danping Yang China
Charalambos Makridakis Greece
Julia Novo
Citations per year, relative to Julia Novo Julia Novo (= 1×) peers Charalambos Makridakis

Countries citing papers authored by Julia Novo

Since Specialization
Citations

This map shows the geographic impact of Julia Novo's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Julia Novo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Julia Novo more than expected).

Fields of papers citing papers by Julia Novo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Julia Novo. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Julia Novo. The network helps show where Julia Novo may publish in the future.

Co-authorship network of co-authors of Julia Novo

This figure shows the co-authorship network connecting the top 25 collaborators of Julia Novo. A scholar is included among the top collaborators of Julia Novo based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Julia Novo. Julia Novo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Frutos, Javier de, Bosco Garcı́a-Archilla, & Julia Novo. (2025). Optimal Bounds for POD Approximations of Infinite Horizon Control Problems Based on Time Derivatives. Journal of Scientific Computing. 103(1).
2.
Novo, Julia, et al.. (2024). Can neural networks learn finite elements?. Journal of Computational and Applied Mathematics. 453. 116168–116168.
3.
Frutos, Javier de & Julia Novo. (2023). Optimal Bounds for Numerical Approximations of Infinite Horizon Problems Based on Dynamic Programming Approach. SIAM Journal on Control and Optimization. 61(2). 415–433. 2 indexed citations
4.
Garcı́a-Archilla, Bosco, Volker John, & Julia Novo. (2023). POD-ROMs for Incompressible Flows Including Snapshots of the Temporal Derivative of the Full Order Solution. SIAM Journal on Numerical Analysis. 61(3). 1340–1368. 10 indexed citations
5.
Novo, Julia & Samuele Rubino. (2021). Error analysis of proper orthogonal decomposition stabilized methods for incompressible flows. Biblos-e Archivo (Universidad Autónoma de Madrid). 19 indexed citations
6.
Frutos, Javier de, Bosco Garcı́a-Archilla, Volker John, & Julia Novo. (2019). Error Analysis of Non Inf-sup Stable Discretizations of the time-dependent Navier--Stokes Equations with Local Projection Stabilization. LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas). 19 indexed citations
7.
Novo, Julia, et al.. (2019). A posteriori error estimations for mixed finite element approximations to the Navier–Stokes equations based on Newton-type linearization. Journal of Computational and Applied Mathematics. 367. 112429–112429. 7 indexed citations
8.
Novo, Julia, et al.. (2019). Generalized postprocessed approximations to the Navier–Stokes equations based on two grids. Journal of Computational and Applied Mathematics. 368. 112516–112516. 1 indexed citations
9.
Frutos, Javier de, Bosco Garcı́a-Archilla, & Julia Novo. (2019). Grad-div stabilization for the time-dependent Boussinesq equations with inf-sup stable finite elements. Applied Mathematics and Computation. 349. 281–291. 5 indexed citations
10.
John, Volker, Songül Kaya, & Julia Novo. (2017). Finite element error analysis of a mantle convection model. Open MIND.
11.
Frutos, Javier de, Bosco Garcı́a-Archilla, Volker John, & Julia Novo. (2017). Semi-robust Local Projection Stabilization for Non Inf-sup Stable Discretizations of the Evolutionary Navier-Stokes Equations. arXiv (Cornell University). 1 indexed citations
12.
John, Volker, et al.. (2015). Finite element methods for the incompressible Stokes equations with variable viscosity. ZAMM ‐ Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik. 96(2). 205–216. 9 indexed citations
13.
John, Volker & Julia Novo. (2015). Analysis of the Pressure Stabilized Petrov--Galerkin Method for the Evolutionary Stokes Equations Avoiding Time Step Restrictions. SIAM Journal on Numerical Analysis. 53(2). 1005–1031. 12 indexed citations
14.
Frutos, Javier de, Bosco Garcı́a-Archilla, Volker John, & Julia Novo. (2015). Grad-div Stabilization for the Evolutionary Oseen Problem with Inf-sup Stable Finite Elements. Journal of Scientific Computing. 66(3). 991–1024. 45 indexed citations
15.
Frutos, Javier de, Bosco Garcı́a-Archilla, & Julia Novo. (2011). A posteriori error estimations for mixed finite-element approximations to the Navier–Stokes equations. Journal of Computational and Applied Mathematics. 236(6). 1103–1122. 10 indexed citations
16.
Frutos, Javier de, Bosco Garcı́a-Archilla, & Julia Novo. (2011). An adaptive finite element method for evolutionary convection dominated problems. Computer Methods in Applied Mechanics and Engineering. 200(49-52). 3601–3612. 9 indexed citations
17.
John, Volker & Julia Novo. (2011). Error Analysis of the SUPG Finite Element Discretization of Evolutionary Convection-Diffusion-Reaction Equations. SIAM Journal on Numerical Analysis. 49(3). 1149–1176. 71 indexed citations
18.
Frutos, Javier de & Julia Novo. (2000). An enhanced pseudospectral Chebyshev method for dissipative partial differential equations. Journal of Computational and Applied Mathematics. 115(1-2). 137–150. 2 indexed citations
19.
Garcı́a-Archilla, Bosco, Julia Novo, & Edriss S. Titi. (1999). An approximate inertial manifolds approach to postprocessing the Galerkin method for the Navier-Stokes equations. Mathematics of Computation. 68(227). 893–911. 51 indexed citations
20.
Garcı́a-Archilla, Bosco, Julia Novo, & Edriss S. Titi. (1998). Postprocessing the Galerkin Method: a Novel Approach to Approximate Inertial Manifolds. SIAM Journal on Numerical Analysis. 35(3). 941–972. 79 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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