Peter L. Green

696 total citations
26 papers, 520 citations indexed

About

Peter L. Green is a scholar working on Mechanical Engineering, Civil and Structural Engineering and Automotive Engineering. According to data from OpenAlex, Peter L. Green has authored 26 papers receiving a total of 520 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Mechanical Engineering, 5 papers in Civil and Structural Engineering and 4 papers in Automotive Engineering. Recurrent topics in Peter L. Green's work include Gaussian Processes and Bayesian Inference (4 papers), Additive Manufacturing and 3D Printing Technologies (4 papers) and Additive Manufacturing Materials and Processes (3 papers). Peter L. Green is often cited by papers focused on Gaussian Processes and Bayesian Inference (4 papers), Additive Manufacturing and 3D Printing Technologies (4 papers) and Additive Manufacturing Materials and Processes (3 papers). Peter L. Green collaborates with scholars based in United Kingdom, United States and Australia. Peter L. Green's co-authors include Neil D. Sims, Paolo Paoletti, Chris Sutcliffe, Evangelos Papatheou, Kate Black, David A. Raftos, Sham V. Nair, H A Khalid, J. Dardis and Rebecca Newton and has published in prestigious journals such as Nature Communications, European Journal of Operational Research and IEEE Access.

In The Last Decade

Peter L. Green

24 papers receiving 499 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Peter L. Green United Kingdom	10	305	128	99	82	81	26	520
Xianbo Liu China	12	246 0.8×	28 0.2×	32 0.3×	59 0.7×	116 1.4×	22	432
Cong Ding China	16	323 1.1×	58 0.5×	8 0.1×	20 0.2×	85 1.0×	40	540
Ehsan Mohseni United Kingdom	15	420 1.4×	42 0.3×	70 0.7×	49 0.6×	68 0.8×	48	636
Vincent Y. Blouin United States	11	129 0.4×	69 0.5×	67 0.7×	83 1.0×	72 0.9×	47	405
Kamal Pal India	16	660 2.2×	57 0.4×	66 0.7×	22 0.3×	110 1.4×	45	790
Jae-Woong Kim South Korea	14	374 1.2×	42 0.3×	43 0.4×	28 0.3×	58 0.7×	72	534
Viktor Berbyuk Sweden	13	347 1.1×	76 0.6×	36 0.4×	182 2.2×	72 0.9×	79	569
Sa’id Golabi Iran	12	487 1.6×	254 2.0×	74 0.7×	88 1.1×	32 0.4×	32	664
Lorenzo Capponi Italy	10	87 0.3×	68 0.5×	33 0.3×	96 1.2×	30 0.4×	32	279

Countries citing papers authored by Peter L. Green

Since Specialization

Citations

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

Fields of papers citing papers by Peter L. Green

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Peter L. Green. 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 Peter L. Green. The network helps show where Peter L. Green may publish in the future.

Co-authorship network of co-authors of Peter L. Green

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Howard, Alex, Peter L. Green, Yin Mo, et al.. (2025). Artificial intelligence and infectious diseases: tackling antimicrobial resistance, from personalised care to antibiotic discovery. The Lancet Infectious Diseases. 26(3). e181–e192. 4 indexed citations

Green, Peter L., et al.. (2025). Fatigue Analysis and Predicting of Hot Mix Asphalt Based on a Fracture Mechanics and Damage Density. International Journal of Pavement Research and Technology.

Roberts, Gareth, et al.. (2024). Simulation to optimize the laboratory diagnosis of bacteremia. Microbiology Spectrum. 12(11). e0144924–e0144924.

Green, Peter L., et al.. (2024). Bayesian Calibration to Address the Challenge of Antimicrobial Resistance: A Review. IEEE Access. 12. 100772–100791. 1 indexed citations

Howard, Alex, David M. Hughes, Peter L. Green, et al.. (2024). Personalised antimicrobial susceptibility testing with clinical prediction modelling informs appropriate antibiotic use. Nature Communications. 15(1). 9924–9924. 4 indexed citations

Howard, Alex, et al.. (2024). Bayesian estimation of the prevalence of antimicrobial resistance: a mathematical modelling study. Journal of Antimicrobial Chemotherapy. 79(9). 2317–2326. 1 indexed citations

Green, Peter L.. (2024). Distributed Gaussian Processes With Uncertain Inputs. IEEE Access. 12. 176087–176093. 1 indexed citations

Paoletti, Paolo, et al.. (2023). Predicting gas pores from photodiode measurements in laser powder bed fusion builds. Progress in Additive Manufacturing. 9(4). 885–888. 4 indexed citations

Green, Peter L., et al.. (2023). Predicting product quality in continuous manufacturing processes using a scalable robust Gaussian Process approach. Engineering Applications of Artificial Intelligence. 127. 107233–107233. 5 indexed citations

10.

Green, Peter L., et al.. (2022). Ensemble Kalman filter based sequential Monte Carlo sampler for sequential Bayesian inference. Statistics and Computing. 32(1). 4 indexed citations

11.

Paoletti, Paolo, et al.. (2021). Automatic quality assessments of laser powder bed fusion builds from photodiode sensor measurements. Progress in Additive Manufacturing. 7(2). 143–160. 44 indexed citations

12.

Jump, Michael, et al.. (2020). Predicting On-axis Rotorcraft Dynamic Responses Using Machine Learning Techniques. Journal of the American Helicopter Society. 65(3). 1–12. 4 indexed citations

13.

Green, Peter L., et al.. (2020). A possibilistic interpretation of ensemble forecasts: experiments on the imperfect Lorenz 96 system. Advances in science and research. 17. 39–45. 3 indexed citations

14.

Sutcliffe, Chris, et al.. (2019). Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning. Additive manufacturing. 27. 42–53. 161 indexed citations

15.

Green, Peter L., et al.. (2017). Predicting fatigue performance of hot mix asphalt using artificial neural networks. Road Materials and Pavement Design. 18(sup2). 141–154. 34 indexed citations

16.

Jesus, Tiago S., Gerald Choon‐Huat Koh, Michel D. Landry, et al.. (2016). Finding the “Right-Size” Physical Therapy Workforce: International Perspective Across 4 Countries. Physical Therapy. 96(10). 1597–1609. 31 indexed citations

17.

Green, Peter L.. (2014). Bayesian System Identification of Nonlinear Dynamical Systems using a Fast MCMC Algorithm. White Rose Research Online (University of Leeds, The University of Sheffield, University of York). 3 indexed citations

18.

Green, Peter L., Keith Worden, Kais Atallah, & Neil D. Sims. (2012). The effect of Duffing-type non-linearities and Coulomb damping on the response of an energy harvester to random excitations. Journal of Intelligent Material Systems and Structures. 23(18). 2039–2054. 21 indexed citations

19.

Green, Peter L., Sham V. Nair, & David A. Raftos. (2002). Secretion of a collectin-like protein in tunicates is enhanced during inflammatory responses. Developmental & Comparative Immunology. 27(1). 3–9. 18 indexed citations

20.

Nair, Sham V., Sarina Pearce, Peter L. Green, et al.. (2000). A collectin-like protein from tunicates. Comparative Biochemistry and Physiology Part B Biochemistry and Molecular Biology. 125(2). 279–289. 41 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.

Explore authors with similar magnitude of impact