This map shows the geographic impact of J. E. Field'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 J. E. Field with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites J. E. Field more than expected).
This network shows the impact of papers produced by J. E. Field. 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 J. E. Field. The network helps show where J. E. Field may publish in the future.
Co-authorship network of co-authors of J. E. Field
This figure shows the co-authorship network connecting the top 25 collaborators of J. E. Field.
A scholar is included among the top collaborators of J. E. Field 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 J. E. Field. J. E. Field is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Nagel, S. R., S. W. Haan, J. R. Rygg, et al.. (2021). Effect of the mounting membrane on shape in ICF implosions. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information).
Kruse, Michael, J. E. Field, James A. Gaffney, et al.. (2019). Area-Based Image Metrics Elucidate Differences Between Radiation-Hydrodynamics Simulations and NIF Experimental X-ray Images. APS Division of Plasma Physics Meeting Abstracts. 2019.1 indexed citations
5.
Nora, R., J. E. Field, C. V. Young, et al.. (2018). 3D HYDRA Capsule Studies on the Effect of Hohlraum Windows. Bulletin of the American Physical Society. 2018.
Humbird, Kelli, Ryan G. McClarren, J. E. Field, et al.. (2017). Using deep neural networks to augment NIF post-shot analysis. Bulletin of the American Physical Society. 2017.1 indexed citations
9.
Peterson, J. L., L. Berzak Hopkins, Kelli Humbird, et al.. (2017). Enhancing Hohlraum Design with Artificial Neural Networks. Bulletin of the American Physical Society. 2017.
Humbird, Kelli, J. L. Peterson, S. Brandon, et al.. (2016). Surrogate models for identifying robust, high yield regions of parameter space for ICF implosion simulations. Bulletin of the American Physical Society. 2016.1 indexed citations
Nora, R., B. K. Spears, Riccardo Tommasini, et al.. (2015). Quantifying low-mode shell asymmetry as a means to predict ICF implosion performance on the NIF. Bulletin of the American Physical Society. 2015.1 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.