Identifying Lightning Structures Via Machine Learning
暂无分享,去创建一个
[1] E. K. Lenzi,et al. Machine learning partners in criminal networks , 2022, Scientific Reports.
[2] E. Schömer,et al. End-to-End Prediction of Lightning Events from Geostationary Satellite Images , 2022, Remote Sensing.
[3] Arthur A. B. Pessa,et al. Determining liquid crystal properties with ordinal networks and machine learning , 2022, Chaos, Solitons & Fractals.
[4] W. Nazarewicz,et al. Machine Learning in Nuclear Physics , 2021, 2112.02309.
[5] T. Marshall,et al. Luminosity with large amplitude pulses after the initial breakdown stage in intracloud lightning flashes , 2021, Atmospheric Research.
[6] P. Garcia. A machine learning based control of chaotic systems , 2021, Chaos, Solitons & Fractals.
[7] W. Lyu,et al. A deep learning framework for lightning forecasting with multi‐source spatiotemporal data , 2021, Quarterly Journal of the Royal Meteorological Society.
[8] S. Buitink,et al. Interferometric imaging of intensely radiating negative leaders , 2021, Physical Review D.
[9] E. Fiori,et al. Cloud-to-Ground lightning nowcasting using Machine Learning , 2021, International Conference on Logic Programming.
[10] Manzhu Yu,et al. Lightning Strike Location Identification Based on 3D Weather Radar Data , 2021, Frontiers in Environmental Science.
[11] Lingxiao Wang,et al. Detecting the chiral magnetic effect via deep learning , 2021, Physical Review C.
[12] S. Buitink,et al. Time resolved 3D interferometric imaging of a section of a negative leader with LOFAR , 2021, Physical Review D.
[13] Lijia Jiang,et al. Deep learning stochastic processes with QCD phase transition , 2021, Physical Review D.
[14] S. Buitink,et al. The Initial Stage of Cloud Lightning Imaged in High‐Resolution , 2021, Journal of Geophysical Research: Atmospheres.
[15] H. Edens,et al. Dart‐Leader and K‐Leader Velocity From Initiation Site to Termination Time‐Resolved With 3D Interferometry , 2020, Journal of Geophysical Research: Atmospheres.
[16] Kai Zhou,et al. Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk , 2020, Mach. Learn. Sci. Technol..
[17] Samuel Lalmuanawma,et al. Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review , 2020, Chaos, Solitons & Fractals.
[18] K. Zhou,et al. Continuous-mixture Autoregressive Networks for efficient variational calculation of many-body systems , 2020, 2005.04857.
[19] J. Dwyer,et al. Radio Emission Reveals Inner Meter-Scale Structure of Negative Lightning Leader Steps. , 2020, Physical review letters.
[20] J. Carrasquilla. Machine learning for quantum matter , 2020, 2003.11040.
[21] D. Bourilkov. Machine and deep learning applications in particle physics , 2019, International Journal of Modern Physics A.
[22] S. Cummer,et al. Needles and Lightning Leader Dynamics Imaged with 100–200 MHz Broadband VHF Interferometry , 2019, Geophysical Research Letters.
[23] F. Rachidi,et al. Nowcasting lightning occurrence from commonly available meteorological parameters using machine learning techniques , 2019, npj Climate and Atmospheric Science.
[24] J. Dwyer,et al. Needle-like structures discovered on positively charged lightning branches , 2019, Nature.
[25] Naftali Tishby,et al. Machine learning and the physical sciences , 2019, Reviews of Modern Physics.
[26] L. Pang,et al. Regressive and generative neural networks for scalar field theory , 2018, Physical Review D.
[27] M. Caffrey,et al. Broadband RF Interferometric Mapping and Polarization (BIMAP) Observations of Lightning Discharges: Revealing New Physics Insights Into Breakdown Processes , 2018, Journal of Geophysical Research: Atmospheres.
[28] Eli Upfal,et al. Machine Learning in High Energy Physics Community White Paper , 2018, Journal of Physics: Conference Series.
[29] Joaquin F. Rodriguez-Nieva,et al. Identifying topological order through unsupervised machine learning , 2018, Nature Physics.
[30] David J. Schwab,et al. A high-bias, low-variance introduction to Machine Learning for physicists , 2018, Physics reports.
[31] H. Stöcker,et al. An equation-of-state-meter of quantum chromodynamics transition from deep learning , 2018, Nature Communications.
[32] Hans-Peter Kriegel,et al. DBSCAN Revisited, Revisited , 2017, ACM Trans. Database Syst..
[33] X. Qie,et al. Upward negative leaders in positive triggered lightning: Stepping and branching in the initial stage , 2017 .
[34] Xiushu Qie,et al. High‐speed video observation of stepwise propagation of a natural upward positive leader , 2016 .
[35] M. D. Tran,et al. Initiation and propagation of cloud-to-ground lightning observed with a high-speed video camera , 2016, Scientific Reports.
[36] Martin Wattenberg,et al. How to Use t-SNE Effectively , 2016 .
[37] Matthias Troyer,et al. Solving the quantum many-body problem with artificial neural networks , 2016, Science.
[38] Yang Zhang,et al. Observations of narrow bipolar events reveal how lightning is initiated in thunderstorms , 2016, Nature Communications.
[39] Jingzhou Liu,et al. Visualizing Large-scale and High-dimensional Data , 2016, WWW.
[40] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[41] M. Stock,et al. Continuous broadband digital interferometry of lightning using a generalized cross‐correlation algorithm , 2014 .
[42] Harald E. Edens,et al. Photographic observations of streamers and steps in a cloud‐to‐air negative leader , 2014 .
[43] J. Dwyer,et al. The physics of lightning , 2014 .
[44] G. Parisi,et al. Scale-free correlations in starling flocks , 2009, Proceedings of the National Academy of Sciences.
[45] Nobuyuki Takagi,et al. Spatial and temporal properties of optical radiation produced by stepped leaders , 1999 .
[46] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .