Physics-informed Meta-instrument for eXperiments (PiMiX) with applications to fusion energy
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| author | Wang, Zhehui Lin, Shanny Teng-Levy, Miles Chu, Pinghan Wolfe, Bradley T. Wong, Chun-Shang Campbell, Christopher S. Yue, Xin Zhang, Liyuan Aberle, Derek Alvarez, Mariana Alvarado Broughton, David Chen, Ray T. Cheng, Baolian Chu, Feng Fossum, Eric R. Foster, Mark A. Huang, Chengkun Kilic, Velat Krushelnick, Karl Li, Wenting Loomis, Eric Schmidt Jr., Thomas Sjue, Sky K. Tomkins, Chris Yarotski, Dmitry A. Zhu, Renyuan |
| author_facet | Wang, Zhehui Lin, Shanny Teng-Levy, Miles Chu, Pinghan Wolfe, Bradley T. Wong, Chun-Shang Campbell, Christopher S. Yue, Xin Zhang, Liyuan Aberle, Derek Alvarez, Mariana Alvarado Broughton, David Chen, Ray T. Cheng, Baolian Chu, Feng Fossum, Eric R. Foster, Mark A. Huang, Chengkun Kilic, Velat Krushelnick, Karl Li, Wenting Loomis, Eric Schmidt Jr., Thomas Sjue, Sky K. Tomkins, Chris Yarotski, Dmitry A. Zhu, Renyuan |
| contents | Data-driven methods (DDMs), such as deep neural networks, offer a generic approach to integrated data analysis (IDA), integrated diagnostic-to-control (IDC) workflows through data fusion (DF), which includes multi-instrument data fusion (MIDF), multi-experiment data fusion (MXDF), and simulation-experiment data fusion (SXDF). These features make DDMs attractive to nuclear fusion energy and power plant applications, leveraging accelerated workflows through machine learning and artificial intelligence. Here we describe Physics-informed Meta-instrument for eXperiments (PiMiX) that integrates X-ray (including high-energy photons such as $γ$-rays from nuclear fusion), neutron and others (such as proton radiography) measurements for nuclear fusion. PiMiX solves multi-domain high-dimensional optimization problems and integrates multi-modal measurements with multiphysics modeling through neural networks. Super-resolution for neutron detection and energy resolved X-ray detection have been demonstrated. Multi-modal measurements through MIDF can extract more information than individual or uni-modal measurements alone. Further optimization schemes through DF are possible towards empirical fusion scaling laws discovery and new fusion reactor designs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_08390 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Physics-informed Meta-instrument for eXperiments (PiMiX) with applications to fusion energy Wang, Zhehui Lin, Shanny Teng-Levy, Miles Chu, Pinghan Wolfe, Bradley T. Wong, Chun-Shang Campbell, Christopher S. Yue, Xin Zhang, Liyuan Aberle, Derek Alvarez, Mariana Alvarado Broughton, David Chen, Ray T. Cheng, Baolian Chu, Feng Fossum, Eric R. Foster, Mark A. Huang, Chengkun Kilic, Velat Krushelnick, Karl Li, Wenting Loomis, Eric Schmidt Jr., Thomas Sjue, Sky K. Tomkins, Chris Yarotski, Dmitry A. Zhu, Renyuan Data Analysis, Statistics and Probability Instrumentation and Detectors Plasma Physics Data-driven methods (DDMs), such as deep neural networks, offer a generic approach to integrated data analysis (IDA), integrated diagnostic-to-control (IDC) workflows through data fusion (DF), which includes multi-instrument data fusion (MIDF), multi-experiment data fusion (MXDF), and simulation-experiment data fusion (SXDF). These features make DDMs attractive to nuclear fusion energy and power plant applications, leveraging accelerated workflows through machine learning and artificial intelligence. Here we describe Physics-informed Meta-instrument for eXperiments (PiMiX) that integrates X-ray (including high-energy photons such as $γ$-rays from nuclear fusion), neutron and others (such as proton radiography) measurements for nuclear fusion. PiMiX solves multi-domain high-dimensional optimization problems and integrates multi-modal measurements with multiphysics modeling through neural networks. Super-resolution for neutron detection and energy resolved X-ray detection have been demonstrated. Multi-modal measurements through MIDF can extract more information than individual or uni-modal measurements alone. Further optimization schemes through DF are possible towards empirical fusion scaling laws discovery and new fusion reactor designs. |
| title | Physics-informed Meta-instrument for eXperiments (PiMiX) with applications to fusion energy |
| topic | Data Analysis, Statistics and Probability Instrumentation and Detectors Plasma Physics |
| url | https://arxiv.org/abs/2401.08390 |