Roberto Molinaro

970 citations
11 papers · 599 · 1 hit paper · h-index 9

Impact in

Papers in

Roberto Molinaro

10 papers receiving 576 citations

Roberto Molinaro's Hit Papers

Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for PDEs 2021 · 222 citations
2220+1+3Years since publication50100150200

Peers

Roberto Molinaro
Comparison fields: 5 of 63
  • Statistical and Nonlinear Physics 373
  • Computational Mechanics 184
  • Statistics, Probability and Uncertainty 49
  • Artificial Intelligence 110
  • Modeling and Simulation 15
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Citations per field
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Citations per year

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-authors

The 7 scholars most cited alongside Roberto Molinaro, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Roberto Molinaro Line = papers co-authored together Roberto Molinaro links everyone, so they are left out of the graph.

All Works

11 of 11 papers shown
#Work
1
Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for PDEs
Hit paper breakdown →
2021222
2 2021108
3 202193
4 202048
5 202439
6 202333
7 202423
8 202116
9
Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs II: A class of inverse problems.
202014
10 20203
11 20240

About Roberto Molinaro

Roberto Molinaro is a scholar working on Statistical and Nonlinear Physics, Computational Mechanics, Artificial Intelligence, Mechanical Engineering and Electronic, Optical and Magnetic Materials, having authored 11 papers that have together received 599 indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (8 papers), Fluid Dynamics and Turbulent Flows (4 papers), Neural Networks and Applications (2 papers), Magnetic Properties and Applications (2 papers), Numerical methods in engineering (1 paper), Real-time simulation and control systems (1 paper), Heat Transfer Mechanisms (1 paper) and Probabilistic and Robust Engineering Design (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (373 citations), Computational Mechanics (184 citations), Statistics, Probability and Uncertainty (49 citations), Artificial Intelligence (110 citations) and Modeling and Simulation (15 citations). Roberto Molinaro has collaborated with scholars based in Switzerland, Norway and United States. Frequent co-authors include Siddhartha Mishra, D. Lakehal, Chidambaram Narayanan, Laura De Lorenzis, E. Hosseini, Roger Käppeli and Emmanuel de Bézenac. Their work appears in journals such as Computer Methods in Applied Mechanics and Engineering, IMA Journal of Numerical Analysis, Journal of Computational Mathematics, SIAM Journal on Numerical Analysis and Computers & Fluids.

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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