Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations
2023152 citationsBen Moseley, Andrew Markham et al.Advances in Computational Mathematicsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Tarje Nissen‐Meyer
Since
Specialization
Citations
This map shows the geographic impact of Tarje Nissen‐Meyer'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 Tarje Nissen‐Meyer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tarje Nissen‐Meyer more than expected).
Fields of papers citing papers by Tarje Nissen‐Meyer
This network shows the impact of papers produced by Tarje Nissen‐Meyer. 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 Tarje Nissen‐Meyer. The network helps show where Tarje Nissen‐Meyer may publish in the future.
Co-authorship network of co-authors of Tarje Nissen‐Meyer
This figure shows the co-authorship network connecting the top 25 collaborators of Tarje Nissen‐Meyer.
A scholar is included among the top collaborators of Tarje Nissen‐Meyer 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 Tarje Nissen‐Meyer. Tarje Nissen‐Meyer is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Nissen‐Meyer, Tarje, Benjamin Fernando, Kuangdai Leng, et al.. (2019). Modelling the effects of 3D shallow scatterers and atmospheric sources on Martian seismic signals at high frequencies. AGU Fall Meeting Abstracts. 2019.1 indexed citations
11.
Schwarz, Benjamin, Karin Sigloch, & Tarje Nissen‐Meyer. (2017). Adapting Controlled-source Coherence Analysis to Dense Array Data in Earthquake Seismology. AGU Fall Meeting Abstracts. 2017. 10637.1 indexed citations
12.
Driel, Martin van, et al.. (2016). Syngine: On-Demand Synthetic Seismograms from the IRIS DMC based on AxiSEM & Instaseis. EGUGA.3 indexed citations
13.
Stähler, Simon C., Martin van Driel, L. M. Auer, et al.. (2016). MC Kernel: Broadband Waveform Sensitivity Kernels for Seismic Tomography. EGU General Assembly Conference Abstracts.5 indexed citations
Casarotti, Emanuele, Federica Magnoni, Nicolas Le Goff, et al.. (2008). Mesh Generation for Short-Period Seismic Wave Propagation Based Upon the Spectral- Element Method: Southern California.. AGUFM. 2008.2 indexed citations
20.
Mercerat, Diego, Jean‐Paul Ampuero, & Tarje Nissen‐Meyer. (2006). Dispersion Analysis and High-Order Symplectic Time Schemes in Spectral-Element Based Seismic Wave Propagation. AGUFM. 2006.2 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.