Daniele E. Schiavazzi

986 citations
42 papers · 731 indexed · h-index 15

Daniele E. Schiavazzi

40 papers receiving 717 citations

Peers

Daniele E. Schiavazzi
Comparison fields: 5 of 78
  • Statistics, Probability and Uncertainty 117
  • Cardiology and Cardiovascular Medicine 269
  • Statistical and Nonlinear Physics 95
  • Computational Mechanics 109
  • Radiology, Nuclear Medicine and Imaging 108
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Toni Lassila United Kingdom
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Citations per year

Countries citing papers authored by Daniele E. Schiavazzi

Since Specialization
Citations

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

Fields of papers citing papers by Daniele E. Schiavazzi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Daniele E. Schiavazzi, 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 Daniele E. Schiavazzi Line = papers co-authored together Daniele E. Schiavazzi links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20254
3 20251
4 20253
5 20248
6 20233
7 20235
8 202122
9 201915
10 201910
11 201837
12 201831
13 201681
14 201623
15 201573
16
Data Assimilation and Propagation of Uncertainty in Multiscale Cardiovascular Simulation
20151
17
SimVascular 2.0: an Integrated Open Source Pipeline for Image-Based Cardiovascular Modeling and Simulation
20152
18 20151
19 201557
20 201432

About Daniele E. Schiavazzi

Daniele E. Schiavazzi is a scholar working on Statistics, Probability and Uncertainty, Statistical and Nonlinear Physics and Cardiology and Cardiovascular Medicine, having authored 42 papers that have together received 731 indexed citations. Recurring topics across this work include Probabilistic and Robust Engineering Design (14 papers), Cardiovascular Function and Risk Factors (11 papers), Model Reduction and Neural Networks (9 papers), Elasticity and Material Modeling (7 papers), Gaussian Processes and Bayesian Inference (5 papers), Cardiac Imaging and Diagnostics (4 papers), Fluid Dynamics and Turbulent Flows (4 papers) and Coronary Interventions and Diagnostics (4 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (117 citations), Cardiology and Cardiovascular Medicine (269 citations) and Statistical and Nonlinear Physics (95 citations). Daniele E. Schiavazzi has collaborated with scholars based in United States, United Kingdom and Chile. Frequent co-authors include Alison L. Marsden, Andrew M. Kahn, Filippo Coletti, John K. Eaton, Justin Tran, Tain‐Yen Hsia, Abhay B. Ramachandra, Christopher J. Elkins, Giancarlo Pennati and Gianluca Geraci. Their work appears in journals such as PLoS ONE, Journal of Computational Physics 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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