Report
Deep neural network Grad-Shafranov solver constrained with measured magnetic signals
| العنوان: | Deep neural network Grad-Shafranov solver constrained with measured magnetic signals |
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| المؤلفون: | Joung, Semin, Kim, Jaewook, Kwak, Sehyun, Bak, J. G., Lee, S. G., Han, H. S., Kim, H. S., Lee, Geunho, Kwon, Daeho, Ghim, Y. -c. |
| سنة النشر: | 2019 |
| المجموعة: | Computer Science Physics (Other) |
| مصطلحات موضوعية: | Physics - Plasma Physics, Computer Science - Machine Learning |
| الوصف: | A neural network solving Grad-Shafranov equation constrained with measured magnetic signals to reconstruct magnetic equilibria in real time is developed. Database created to optimize the neural network's free parameters contain off-line EFIT results as the output of the network from $1,118$ KSTAR experimental discharges of two different campaigns. Input data to the network constitute magnetic signals measured by a Rogowski coil (plasma current), magnetic pick-up coils (normal and tangential components of magnetic fields) and flux loops (poloidal magnetic fluxes). The developed neural networks fully reconstruct not only the poloidal flux function $\psi\left( R, Z\right)$ but also the toroidal current density function $j_\phi\left( R, Z\right)$ with the off-line EFIT quality. To preserve robustness of the networks against a few missing input data, an imputation scheme is utilized to eliminate the required additional training sets with large number of possible combinations of the missing inputs. |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1088/1741-4326/ab555f |
| URL الوصول: | http://arxiv.org/abs/1911.02882 |
| رقم الانضمام: | edsarx.1911.02882 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1088/1741-4326/ab555f |
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