David E. Bernal

1.1k total citations
40 papers, 672 citations indexed

About

David E. Bernal is a scholar working on Control and Systems Engineering, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, David E. Bernal has authored 40 papers receiving a total of 672 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Control and Systems Engineering, 14 papers in Artificial Intelligence and 11 papers in Computational Theory and Mathematics. Recurrent topics in David E. Bernal's work include Advanced Control Systems Optimization (14 papers), Quantum Computing Algorithms and Architecture (12 papers) and Process Optimization and Integration (11 papers). David E. Bernal is often cited by papers focused on Advanced Control Systems Optimization (14 papers), Quantum Computing Algorithms and Architecture (12 papers) and Process Optimization and Integration (11 papers). David E. Bernal collaborates with scholars based in United States, Finland and Canada. David E. Bernal's co-authors include Ignacio E. Grossmann, Jan Kronqvist, Andreas Lundell, Dimitar Trenev, Stuart M. Harwood, Luis Ricardez‐Sandoval, Jorge M. Gómez, Claudio Gambella, Donny Greenberg and Andrea Simonetto and has published in prestigious journals such as Proceedings of the National Academy of Sciences, European Journal of Operational Research and Industrial & Engineering Chemistry Research.

In The Last Decade

David E. Bernal

33 papers receiving 634 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
David E. Bernal United States 15 288 158 112 101 89 40 672
Ashutosh Mahajan India 7 156 0.5× 44 0.3× 153 1.4× 116 1.1× 107 1.2× 16 563
Stefan Vigerske Germany 14 226 0.8× 53 0.3× 148 1.3× 148 1.5× 150 1.7× 31 582
Yuji Shinano Germany 12 91 0.3× 84 0.5× 168 1.5× 122 1.2× 53 0.6× 45 496
Nicolas W. Sawaya United States 7 380 1.3× 40 0.3× 152 1.4× 160 1.6× 221 2.5× 7 778
Peng Guo China 19 93 0.3× 85 0.5× 49 0.4× 159 1.6× 128 1.4× 62 879
Shurong Li China 16 403 1.4× 73 0.5× 148 1.3× 52 0.5× 25 0.3× 132 910
Jan Kronqvist Finland 10 268 0.9× 84 0.5× 83 0.7× 109 1.1× 128 1.4× 25 524
Jixin Qian China 16 655 2.3× 322 2.0× 130 1.2× 169 1.7× 42 0.5× 121 1.1k
Dingwei Wang China 13 142 0.5× 158 1.0× 78 0.7× 111 1.1× 11 0.1× 48 629
Vladimir Mahalec Canada 19 593 2.1× 89 0.6× 107 1.0× 78 0.8× 23 0.3× 66 1.1k

Countries citing papers authored by David E. Bernal

Since Specialization
Citations

This map shows the geographic impact of David E. Bernal'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 David E. Bernal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David E. Bernal more than expected).

Fields of papers citing papers by David E. Bernal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David E. Bernal. 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 David E. Bernal. The network helps show where David E. Bernal may publish in the future.

Co-authorship network of co-authors of David E. Bernal

This figure shows the co-authorship network connecting the top 25 collaborators of David E. Bernal. A scholar is included among the top collaborators of David E. Bernal 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 David E. Bernal. David E. Bernal 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.
Lee, Albert, et al.. (2025). Logic-Based Discrete-Steepest Descent: A solution method for process synthesis Generalized Disjunctive Programs. Computers & Chemical Engineering. 195. 108993–108993. 2 indexed citations
2.
Kronqvist, Jan, David E. Bernal, & Ignacio E. Grossmann. (2025). 50 years of mixed-integer nonlinear and disjunctive programming. European Journal of Operational Research. 331(3). 687–705.
3.
Dupont, Maxime, et al.. (2025). Benchmarking quantum optimization for the maximum-cut problem on a superconducting quantum computer. Physical Review Applied. 23(1). 4 indexed citations
4.
Gustafson, Erik, et al.. (2025). Toward efficient quantum computation of molecular ground‐state energies. AIChE Journal. 71(12).
5.
Bernal, David E. & Ignacio E. Grossmann. (2024). Convex mixed-integer nonlinear programs derived from generalized disjunctive programming using cones. Computational Optimization and Applications. 88(1). 251–312. 4 indexed citations
6.
Sawaya, Nicolas P. D., Daniel P. Tabor, David E. Bernal, et al.. (2024). HamLib: A library of Hamiltonians for benchmarking quantum algorithms and hardware. Quantum. 8. 1559–1559. 3 indexed citations
7.
Bhatia, Amandeep Singh & David E. Bernal. (2024). Federated learning with tensor networks: a quantum AI framework for healthcare. Machine Learning Science and Technology. 5(4). 45035–45035. 5 indexed citations
8.
Bernal, David E., et al.. (2023). Modeling for integrated refinery planning with crude-oil scheduling. Process Safety and Environmental Protection. 192. 141–157. 7 indexed citations
9.
Bernal, David E., et al.. (2023). Mind the O˜: Asymptotically Better, but Still Impractical, Quantum Distributed Algorithms. Algorithms. 16(7). 332–332. 2 indexed citations
10.
Sawaya, Nicolas P. D., Daniel P. Tabor, David E. Bernal, et al.. (2023). HamLib: A Library of Hamiltonians for Benchmarking Quantum Algorithms and Hardware. eScholarship (California Digital Library). 389–390. 7 indexed citations
11.
Bernal, David E., et al.. (2022). Alternative regularizations for Outer-Approximation algorithms for convex MINLP. Journal of Global Optimization. 84(4). 807–842. 6 indexed citations
12.
Bernal, David E., et al.. (2022). Perspectives of quantum computing for chemical engineering. AIChE Journal. 68(6). 33 indexed citations
13.
Mostafaei, Hossein, et al.. (2020). Large-scale selective maintenance optimization using bathtub-shaped failure rates. Computers & Chemical Engineering. 139. 106876–106876. 20 indexed citations
14.
Bernal, David E., Stefan Vigerske, Francisco Trespalacios, & Ignacio E. Grossmann. (2019). Improving the performance of DICOPT in convex MINLP problems using a feasibility pump. Optimization methods & software. 35(1). 171–190. 25 indexed citations
15.
Kronqvist, Jan, David E. Bernal, Andreas Lundell, & Ignacio E. Grossmann. (2018). A review and comparison of solvers for convex MINLP. Optimization and Engineering. 20(2). 397–455. 198 indexed citations
16.
Tang, Lixin, et al.. (2017). Improved quadratic cuts for convex mixed-integer nonlinear programs. Computers & Chemical Engineering. 109. 77–95. 16 indexed citations
17.
Arribas, Javier, David E. Bernal, Carles Fernández–Prades, Pau Closas, & Juan A. Fernandez–Rubio. (2009). A Novel Real-time Platform for Digital Beamforming with GNSS Software Defined Receivers. 2329–2343. 3 indexed citations
18.
Bernal, David E., Pau Closas, & Juan A. Fernandez–Rubio. (2008). Particle Filtering Algorithm for Ultra-tight GNSS/INS Integration. 2137–2144. 9 indexed citations
19.
Closas, Pau, Carles Fernández–Prades, David E. Bernal, & Juan A. Fernandez–Rubio. (2008). Bayesian Direct Position Estimation. 183–190. 11 indexed citations
20.
Closas, Pau, Carles Fernández–Prades, Juan A. Fernandez–Rubio, & David E. Bernal. (2008). Particle Filtering Strategies for Efficient Multipath Mitigation. 644–651. 1 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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