Roberto Molinaro

895 total citations · 1 hit paper
10 papers, 539 citations indexed

About

Roberto Molinaro is a scholar working on Statistical and Nonlinear Physics, Computational Mechanics and Artificial Intelligence. According to data from OpenAlex, Roberto Molinaro has authored 10 papers receiving a total of 539 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Statistical and Nonlinear Physics, 4 papers in Computational Mechanics and 3 papers in Artificial Intelligence. Recurrent topics in Roberto Molinaro's work include Model Reduction and Neural Networks (8 papers), Fluid Dynamics and Turbulent Flows (4 papers) and Neural Networks and Applications (2 papers). Roberto Molinaro is often cited by papers focused on Model Reduction and Neural Networks (8 papers), Fluid Dynamics and Turbulent Flows (4 papers) and Neural Networks and Applications (2 papers). Roberto Molinaro collaborates with scholars based in Switzerland, India and Norway. Roberto Molinaro's co-authors include Siddhartha Mishra, D. Lakehal, Chidambaram Narayanan, E. Hosseini and Laura De Lorenzis and has published in prestigious journals such as Computer Methods in Applied Mechanics and Engineering, SIAM Journal on Numerical Analysis and Journal of Quantitative Spectroscopy and Radiative Transfer.

In The Last Decade

Roberto Molinaro

10 papers receiving 508 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for PDEs

2021 206 citations Siddhartha Mishra, Roberto Molinaro IMA Journal of Numerical Analysis profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Roberto Molinaro Switzerland	9	347	177	103	85	67	10	539
Andrew J. Meade United States	10	212 0.6×	108 0.6×	119 1.2×	79 0.9×	48 0.7×	31	497
Georgios Kissas United States	4	257 0.7×	120 0.7×	72 0.7×	40 0.5×	32 0.5×	6	432
Deep Ray United States	11	448 1.3×	370 2.1×	99 1.0×	43 0.5×	50 0.7×	24	727
Zongren Zou United States	10	297 0.9×	73 0.4×	160 1.6×	50 0.6×	34 0.5×	15	588
George Em Karniadakis United States	6	219 0.6×	126 0.7×	63 0.6×	44 0.5×	22 0.3×	14	369
Kookjin Lee United States	9	302 0.9×	161 0.9×	72 0.7×	38 0.4×	18 0.3×	26	462
Daniel Zhengyu Huang United States	11	171 0.5×	111 0.6×	88 0.9×	63 0.7×	129 1.9×	25	484
Nicholas Geneva United States	7	236 0.7×	233 1.3×	83 0.8×	45 0.5×	17 0.3×	7	469

Countries citing papers authored by Roberto Molinaro

Since Specialization

Citations

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

Fields of papers citing papers by Roberto Molinaro

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Roberto Molinaro. 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 Roberto Molinaro. The network helps show where Roberto Molinaro may publish in the future.

Co-authorship network of co-authors of Roberto Molinaro

This figure shows the co-authorship network connecting the top 25 collaborators of Roberto Molinaro. A scholar is included among the top collaborators of Roberto Molinaro 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 Roberto Molinaro. Roberto Molinaro is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

1.

Molinaro, Roberto, et al.. (2024). Phase-field modeling of fracture with physics-informed deep learning. Computer Methods in Applied Mechanics and Engineering. 429. 117104–117104. 27 indexed citations

2.

Mishra, Siddhartha, et al.. (2024). wPINNs: Weak Physics Informed Neural Networks for Approximating Entropy Solutions of Hyperbolic Conservation Laws. SIAM Journal on Numerical Analysis. 62(2). 811–841. 18 indexed citations

3.

Hosseini, E., et al.. (2023). Single-track thermal analysis of laser powder bed fusion process: Parametric solution through physics-informed neural networks. Computer Methods in Applied Mechanics and Engineering. 410. 116019–116019. 33 indexed citations

4.

Mishra, Siddhartha & Roberto Molinaro. (2021). Estimates on the generalization error of physics-informed neural networks for approximating PDEs. IMA Journal of Numerical Analysis. 43(1). 1–43. 98 indexed citations

5.

Mishra, Siddhartha, et al.. (2021). Physics Informed Neural Networks (PINNs) For Approximating Nonlinear Dispersive PDEs. Journal of Computational Mathematics. 39(6). 816–847. 15 indexed citations

6.

Mishra, Siddhartha & Roberto Molinaro. (2021). Physics informed neural networks for simulating radiative transfer. Journal of Quantitative Spectroscopy and Radiative Transfer. 270. 107705–107705. 83 indexed citations

7.

Mishra, Siddhartha & Roberto Molinaro. (2021). Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for PDEs. IMA Journal of Numerical Analysis. 42(2). 981–1022. 206 indexed citations breakdown →

8.

Mishra, Siddhartha & Roberto Molinaro. (2020). Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs II: A class of inverse problems.. arXiv (Cornell University). 14 indexed citations

9.

Molinaro, Roberto, et al.. (2020). Embedding data analytics and CFD into the digital twin concept. Computers & Fluids. 214. 104759–104759. 43 indexed citations

10.

Lakehal, D. & Roberto Molinaro. (2020). On the paradigm of combining data analytics and CFD. AIP conference proceedings. 2309. 30035–30035. 2 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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