Luca Laurenti

31 papers receiving 342 citations

Hit Papers

A defect-based physics-informed machine learning framewor...202220262023202420224080120

Peers

Luca Laurenti
Comparison fields: 5 of 59
  • Mechanical Engineering 139
  • Artificial Intelligence 65
  • Computational Theory and Mathematics 57
  • Mechanics of Materials 54
  • Molecular Biology 53
Replace Xiangwei Li with:
Xiangwei Li China
Peter C. Breedveld Netherlands
Sumit Ray United States
Matthew Tesch United States
Jang-Ho Seo South Korea
Daehwan Lho South Korea
Brian Chen United States
C.D. Schaper United States
Luca Laurenti relative to Xiangwei Li China Xiangwei Li's profile →
Citations per field
00.5×9.5×
Xiangwei Li · 1×
Citations per year

Countries citing papers authored by Luca Laurenti

Since Specialization
Citations

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

Fields of papers citing papers by Luca Laurenti

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Luca Laurenti

This figure shows the co-authorship network connecting the top 25 collaborators of Luca Laurenti. A scholar is included among the top collaborators of Luca Laurenti 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 Luca Laurenti. Luca Laurenti 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
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A defect-based physics-informed machine learning framework for fatigue finite life prediction in additive manufacturingbreakdown →
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Robustness Quantification for Classification with Gaussian Processes.
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Global Adversarial Robustness Guarantees for Neural Networks
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About Luca Laurenti

Luca Laurenti is a scholar working on Statistics, Probability and Uncertainty, Computational Theory and Mathematics and Control and Systems Engineering, having authored 35 papers that have together received 350 indexed citations. Recurring topics across this work include Fault Detection and Control Systems (9 papers), Adversarial Robustness in Machine Learning (9 papers) and Formal Methods in Verification (9 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (29 citations), Mechanical Engineering (139 citations) and Computational Theory and Mathematics (57 citations). Luca Laurenti has collaborated with scholars based in Netherlands, United Kingdom and United States. Frequent co-authors include Enrico Salvati, Marta Kwiatkowska, Luca Cardelli, Francesco De Bona, Andrea Patanè, Morteza Lahijanian, Alessandro Abate, Simeon C. Calvert, Attila Csikász‐Nagy and Max Tschaikowski. Their work appears in journals such as IEEE Transactions on Automatic Control, Biophysical Journal and Computer Methods in Applied Mechanics and Engineering.

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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