James A. Sethian
Affiliations: | Mathematics | University of California, Berkeley, Berkeley, CA, United States |
Area:
Applied mathematics, Computational physics, Partial differential equationsGoogle:
"James Sethian"Children
Sign in to add traineeAlexander B. Vladimirsky | grad student | 2001 | UC Berkeley |
Maria K. Cameron | grad student | 2007 | UC Berkeley |
Elizabeth J. Chester | grad student | 2007 | UC Berkeley |
Jae-Seok Huh | grad student | 2007 | UC Berkeley |
Ying Shan | grad student | 2007 | UC Berkeley |
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Publications
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Casas PS, Garzon M, Gray LJ, et al. (2019) Numerical study on electrohydrodynamic multiple droplet interactions. Physical Review. E. 100: 063111 |
Noack MM, Yager KG, Fukuto M, et al. (2019) A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering. Scientific Reports. 9: 11809 |
Karslıoğlu O, Gehlmann M, Müller J, et al. (2019) An Efficient Algorithm for Automatic Structure Optimization in X-ray Standing-Wave Experiments Journal of Electron Spectroscopy and Related Phenomena. 230: 10-20 |
Pandolfi RJ, Allan DB, Arenholz E, et al. (2018) Xi-cam: a versatile interface for data visualization and analysis. Journal of Synchrotron Radiation. 25: 1261-1270 |
Garzon M, Gray LJ, Sethian JA. (2018) Electrohydrodynamic coalescence of droplets using an embedded potential flow model. Physical Review. E. 97: 033112 |
Pelt DM, Sethian JA. (2018) A mixed-scale dense convolutional neural network for image analysis. Proceedings of the National Academy of Sciences of the United States of America. 115: 254-259 |
Pelt D, Batenburg K, Sethian J. (2018) Improving Tomographic Reconstruction from Limited Data Using Mixed-Scale Dense Convolutional Neural Networks Journal of Imaging. 4: 128 |
Perciano T, Ushizima D, Krishnan H, et al. (2017) Insight into 3D micro-CT data: exploring segmentation algorithms through performance metrics. Journal of Synchrotron Radiation. 24: 1065-1077 |
Donatelli JJ, Sethian JA, Zwart PH. (2017) Reconstruction from limited single-particle diffraction data via simultaneous determination of state, orientation, intensity, and phase. Proceedings of the National Academy of Sciences of the United States of America |
Daurer BJ, Krishnan H, Perciano T, et al. (2017) Nanosurveyor: a framework for real-time data processing. Advanced Structural and Chemical Imaging. 3: 7 |