分类： 物理学 >> 凝聚态:电子结构、电、磁和光学性质 提交时间： 2024-01-20
摘要： Constructing models to discover physics underlying magnanimous data is a traditional strategy in data mining which has been proved to be powerful and successful. In this work, a multi-optimized recurrent neural network (MRNN) is utilized to predict the dynamics of photosynthetic excitation energy transfer (EET) in a light-harvesting complex. The original data set produced by the master equation were trained to forecast the EET evolution. An agreement between our prediction and the theoretical deduction with an accuracy of over 99.26 % is found, showing the validity of the proposed MRNN. A time-segment polynomial fitting multiplied by a unit step function results in a striking consistence with analytical formulations for the photosynthetic EET. The work sets up a precedent for accurate EET prediction from large data set by establishing analytical descriptions for physics hidden behind, through minimizing the processing cost during the evolution of week-coupling EET.
分类： 物理学 >> 电磁学、光学、声学、传热、经典力学和流体动力学 提交时间： 2016-10-17
摘要： The fluctuations of the current and voltage of the quantized unit cell equivalent circuit in loss-less mesoscopic left-handed transmission lines (LH TL)are deduced by thermal field dynamics (TFD) theory. And the fluctuations dependent of negative refractive index (NRI) of mesoscopic LH TL is discussed further in thermal Fock state. The results indicate that the quantum fluctuations show linear increasing dependent of NRI, while the frequency within the microwave frequency band and thermal photons show destructive dependent of NRI at some temperature. When the unit cell equivalent circuit operates at the rising temperature, the NRI is decreasing. The results demonstrates that the lower frequency and temperature, little thermal photons are more conducive to NRI of the mesoscopic LH TL, which is significant for the miniaturizing applications of LH TL.
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