[1] 陈祥, 阚瑞峰, 杨晨光, 等. 基于频分复用波长调制光谱的NO2及NH3浓度测量[J]. 光学学报, 2018, 38(5): 0512004.

    Chen X, Kan R F, Yang C G, et al. Concentration measurements of NO2 and NH3 based on wavelength-modulation frequency-division-multiplexing spectroscopic technique[J]. Acta Optica Sinica, 2018, 38(5): 0512004.

[2] 屈东胜, 樊宏杰, 刘连伟, 等. 基于近红外光谱的超声速燃烧场气体参数测量研究[J]. 光学学报, 2020, 40(3): 0330001.

    Qu D S, Fan H J, Liu L W, et al. Measurement of gas parameters in supersonic combustion field based on near-infrared spectroscopy[J]. Acta Optica Sinica, 2020, 40(3): 0330001.

[3] 张福才, 孙博君, 孙晓刚. 基于多目标极值优化法的多光谱真温反演[J]. 光学学报, 2019, 39(2): 0212008.

    Zhang F C, Sun B J, Sun X G. Multispectral true temperature inversion based on multi-objective minimization optimization method[J]. Acta Optica Sinica, 2019, 39(2): 0212008.

[4] 黄燕, 张国勇, 刘训臣, 等. 中红外激光测量扩散火焰温度场[J]. 工程热物理学报, 2017, 38(7): 1447-1453.

    Huang Y, Zhang G Y, Liu X C, et al. Temperature distribution of axisymmetric diffusion flame measured by mid-infrared laser[J]. Journal of Engineering Thermophysics, 2017, 38(7): 1447-1453.

[5] Liu C, Xu L J, Cao Z. Measurement of nonuniform temperature and concentration distributions by combining line-of-sight tunable diode laser absorption spectroscopy with regularization methods[J]. Applied Optics, 2013, 52(20): 4827-4842.

[6] Wang F, Cen K, Li N, et al. Two-dimensional tomography for gas concentration and temperature distributions based on tunable diode laser absorption spectroscopy[J]. Measurement Science and Technology, 2010, 21(4): 045301.

[7] Zhang G Y, Wang G Q, Huang Y, et al. Reconstruction and simulation of temperature and CO2 concentration in an axisymmetric flame based on TDLAS[J]. Optik, 2018, 170: 166-177.

[8] Liu X C, Zhang G Y, Huang Y, et al. Two-dimensional temperature and carbon dioxide concentration profiles in atmospheric laminar diffusion flames measured by mid-infrared direct absorption spectroscopy at 4.2 μm[J]. Applied Physics B, 2018, 124(4): 1-10.

[9] 张海丹. 基于高光谱成像系统的火焰三维温度场和烟黑浓度场重建研究[D]. 杭州: 浙江大学, 2016.

    Zhang HD. Reconstruction of flame temperature field and soot concentration field based on hyperspectral imaging system[D]. Hangzhou: Zhejiang University, 2016.

[10] Michalski RS, Carbonell JG, Mitchell TM. Machine learning[M]. Berlin, Heidelberg: Springer Berlin Heidelberg, 1983.

[11] Chen N F Y, Kasim M F, Ceurvorst L, et al. Machine learning applied to proton radiography of high-energy-density plasmas[J]. Physical Review E, 2017, 95: 043305.

[12] Rodrigues É O. Pinheiro V H A, Liatsis P, et al. Machine learning in the prediction of cardiac epicardial and mediastinal fat volumes[J]. Computers in Biology and Medicine, 2017, 89: 520-529.

[13] Huang J Q, Liu H C, Cai W W. Online in situ prediction of 3-D flame evolution from its history 2-D projections via deep learning[J]. Journal of Fluid Mechanics, 2019, 875: R2.

[14] Jin Y, Zhang W Q, Song Y, et al. Three-dimensional rapid flame chemiluminescence tomography via deep learning[J]. Optics Express, 2019, 27(19): 27308-27334.

[15] Johns J M, Burkes D. Development of multilayer perceptron networks for isothermal time temperature transformation prediction of U-Mo-X alloys[J]. Journal of Nuclear Materials, 2017, 490: 155-166.

[16] Ren T, Modest M F, Fateev A, et al. Machine learning applied to retrieval of temperature and concentration distributions from infrared emission measurements[J]. Applied Energy, 2019, 252: 113448.

[17] Taglialatela ScafatiF, LavorgnaM, MancarusoE, et al.Artificial intelligence for modeling and control of nonlinear phenomena in internal combustion engines[M] ∥SpringerBriefs in Applied Sciences and Technology. Cham: Springer International Publishing, 2017: 1- 19.

[18] Nutkiewicz A, Yang Z, Jain R K. Data-driven Urban Energy Simulation (DUE-S): a framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow[J]. Applied Energy, 2018, 225: 1176-1189.

[19] Rothman L S, Gordon I E, Barber R J, et al. HITEMP, the high-temperature molecular spectroscopic database[J]. Journal of Quantitative Spectroscopy and Radiative Transfer, 2010, 111(15): 2139-2150.

[20] Smooke M D. McEnally C S, Pfefferle L D, et al. Computational and experimental study of soot formation in a coflow, laminar diffusion flame[J]. Combustion and Flame, 1999, 117(1/2): 117-139.

[21] Cuoci A, Frassoldati A, Faravelli T, et al. Numerical modeling of laminar flames with detailed kinetics based on the operator-splitting method[J]. Energy & Fuels, 2013, 27(12): 7730-7753.

[22] Cuoci A, Frassoldati A, Faravelli T, et al. A computational tool for the detailed kinetic modeling of laminar flames: application to C2H4/CH4 coflow flames[J]. Combustion and Flame, 2013, 160(5): 870-886.

[23] Cuoci A, Frassoldati A, Faravelli T, et al. OpenSMOKE++: an object-oriented framework for the numerical modeling of reactive systems with detailed kinetic mechanisms[J]. Computer Physics Communications, 2015, 192: 237-264.

[24] KramerO. Machine learning for evolution strategies[M]. Cham: Springer International Publishing, 2016.

[25] Kingma DP, Ba J. Adam: a method for stochastic optimization[EB/OL].2014: arXiv:1412. 6980[2020-07-16]. https:∥arxiv.org/abs/1412. 6980.