Author

G. M. Noetscher

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioengineering· 1 Biophysics· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Approach:deep learning· 1 Assay:electrophysiology· 1 Approach:simulation· 1 Approach:statistical model· 1

Frequent Co-Authors

A. Nazarian· 1 shared A. Nummenmaa· 1 shared A. Prokop· 1 shared A. T. Htet· 1 shared E. H. Burnham· 1 shared G. B. Saturnino· 1 shared J. Ackerman· 1 shared J. E. Brown· 1 shared

Activity by Year

2018-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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1 shared paperBiophysics

A. Nazarian

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2 papers

Recent·Most cited

BiophysicsbioRxivPreprintJul 16, 2023

An In-Silico Testbed for Fast and Accurate MR Labeling of Orthopaedic Implants

G. M. Noetscher, P. J. Serano, M. Horner…·S. N. Makaroff

The paper presents an in-silico testbed for MR labeling of orthopaedic implants, approved by FDA as a medical device development tool for 1.5 T examinations.

DOI 10.1101/2023.07.16.549234

simulation

𝕏 PostBluesky

BioengineeringbioRxivDec 21, 2018

Comparative Performance of the Finite Element Method and the Boundary Element Fast Multipole Method for Problems Mimicking Transcranial Magnetic Stimulation (TMS)

A. T. Htet, G. B. Saturnino, E. H. Burnham…·S. N. Makarov

This paper compares the finite element method and boundary element fast multipole method for transcranial magnetic stimulation simulations, finding BEM-FMM offers lower error and faster performance for high-resolution models.

DOI 10.1088/1741-2552/aafbb9

electrophysiologydeep learning

𝕏 PostBluesky