Литература

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M. J. Assael, J. H. Dymond, M. Papadaki, and P. M. Patterson. Correlation and prediction of dense fluid transport coefficients. I. n-alkanes. International Journal of Thermophysics, 13(2):269–281, 1992. doi:10.1007/bf00504436.

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L. E. Baker, A. C. Pierce, and K. D. Luks. Gibbs energy analysis of phase equilibria. Society of Petroleum Engineers Journal, 22(5):731–742, 1982. doi:10.2118/9806-PA.

[BPRS18]

Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Andreyevich Radul, and Jeffrey Mark Siskind. Automatic differentiation in machine learning: a survey. Journal of Machine Learning Research, 18:1–43, 2018. URL: https://jmlr.org/papers/v18/17-468.html.

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J. Bezanson, A. Edelman, S. Karpinski, and V. B. Shah. Julia: a fresh approach to numerical computing. SIAM Review, 59(1):65–98, 2017. doi:10.1137/141000671.

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T. A. Driscoll and R. J. Braun. Fundamentals of numerical computation. Society for Industrial and Applied Mathematics, 2017. ISBN 978-1-61197-507-9. URL: https://tobydriscoll.net/project/fnc/.

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Andreas Griewank. Who invented the reverse mode of differentiation? In Martin Grötschel, editor, Optimization Stories, pages 389–400. EMS Press, 2012. doi:10.4171/dms/6/38.

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J. Gross and G. Sadowski. Perturbed-chain SAFT: an equation of state based on a perturbation theory for chain molecules. Industrial & Engineering Chemistry Research, 40(4):1244–1260, 2001. doi:10.1021/ie0003887.

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Michael Innes. Don't unroll adjoint: differentiating ssa-form programs. CoRR, 2018. doi:https://doi.org/10.48550/arXiv.1810.07951.

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David Johnston. Advances in thermodynamics of the van der waals fluid. arXiv, 2014. doi:10.1088/978-1-627-05532-1.

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Mykel J. Kochenderfer and Tim A. Wheeler. Algorithms for optimization. The MIT Press, 2019. ISBN 978-0-262-03942-0.

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T.Y. Kwak and G.A. Mansoori. Van der waals mixing rules for cubic equations of state. applications for supercritical fluid extraction modelling. Chemical Engineering Science, 41(5):1303–1309, 1986. doi:10.1016/0009-2509(86)87103-2.

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T. Kwong. Hands-on design patterns and best practices with Julia: proven solutions to common problems in software design for Julia 1.x. Packt Publishing, 2020. ISBN 9781838648817.

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J. Lekner. Parametric solution of the van der waals liquid–vapor coexistence curve. American Journal of Physics, 50(2):161–163, 1982. doi:10.1119/1.12877.

[LeV02]

R. J. LeVeque. Finite Volume Methods for Hyperbolic Problems. Cambridge University Press, 2002. doi:10.1017/cbo9780511791253.

[MSA03]

Joaquim R. R. A. Martins, Peter Sturdza, and Juan J. Alonso. The complex-step derivative approximation. ACM Transactions on Mathematical Software, 29(3):245–262, 2003. doi:10.1145/838250.838251.

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M. L. Michelsen. The isothermal flash problem. part I. stability. Fluid Phase Equilibria, 9(1):1–19, 1982. doi:10.1016/0378-3812(82)85001-2.

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I. F. D. Oliveira and R. H. C. Takahashi. An enhancement of the bisection method average performance preserving minmax optimality. ACM Transactions on Mathematical Software, 2020. doi:10.1145/3423597.

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[RLP16]

J. Revels, M. Lubin, and T. Papamarkou. Forward-mode automatic differentiation in Julia. arXiv:1607.07892 [cs.MS], 2016. URL: https://arxiv.org/abs/1607.07892.

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C. Ridders. A new algorithm for computing a single root of a real continuous function. IEEE Transactions on Circuits and Systems, 26(11):979–980, 1979. doi:10.1109/TCS.1979.1084580.

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R. E. Wengert. A simple automatic derivative evaluation program. Communications of the ACM, 7(8):463–464, 1964. doi:10.1145/355586.364791.

[02]

А.И. Брусиловский. Фазовые превращения при разработке месторождений нефти и газа. ВИНИТИ РАН, Грааль, 2002. ISBN 5-94688-031-4.

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