Academic literature on the topic 'Conditional Hesse matrix'

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Journal articles on the topic "Conditional Hesse matrix"

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Барановский, Алексей Сергеевич, Дмитрий Викторович Ушаков, Валерий Геннадьевич Шамонин, Станислав Анатольевич Зуев, and Светлана Юрьевна Хатунцева. "Assessment of fire protection distances between the objects of different purpose taking into account the cooling of external surfaces." Pozharnaia bezopasnost`, no. 1(102) (March 24, 2021): 60–66. http://dx.doi.org/10.37657/vniipo.pb.2021.91.10.007.

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Проведена серия расчетов по оценке температуры при пожаре в режиме, регулируемом вентиляцией, поскольку при этом достигается максимальное значение температуры. Исследуется радиационный и конвективный теплообмен с наружными поверхностями стен помещения. Рассмотрен случай пожара в помещениях складского типа. The application of local variation method for solving fire safety problems with variational formulation (continuous or finite-dimensional) makes it possible to significantly simplify the procedure of mathematical calculations for fire modeling and calculation tasks in the field of fire safety. The choice of the local variation method as a method of direct search for the conditional optimization problem (if there are restrictions) is associated with the absence of the need to calculate the derivatives of the target function (gradient and Hesse matrix). This saves the user from the tedious procedure of getting analytical or finite-difference expressions for the specified derivatives. This choice seems also to be due to the task of non-differentiable optimization. The article provides a justification for choosing the local variation method as one of the alternating-variable descent methods, developed for problems of thermal conductivity, control, etc. Specific features in the implementation of this algorithm are noted, as well as test examples of its application on several well-known test examples representing poorly conditioned “functionals” of small dimension. There are considered the methods of numerical implementation either by Hooke-Jeeves, which is one of the methods of coordinate descent developed for unconditional optimization problems, or by Nelder - Mead, one of the most effective methods if the number of variables does not exceed 6. Example researches showed that regularization contributes to the convergence of the method of local variations. The most effective one is a stabilizer that uses an exact solution. Some predominance of negative test results on the use of regularization is explained by insufficient conditionality of the examples used. This circumstance indicates the need for further search for optimal possibilities of using the method of local variations for applied problems. If it is necessary other stabilizers, not considered in the article, can be used. For the purpose of optimization of the formulated problem, it seems optimal to design a new type of stabilizers.
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Medina, H., N. Romano, and G. B. Chirico. "Kalman filters for assimilating near-surface observations in the Richards equation – Part 2: A dual filter approach for simultaneous retrieval of states and parameters." Hydrology and Earth System Sciences Discussions 9, no. 12 (2012): 13329–72. http://dx.doi.org/10.5194/hessd-9-13329-2012.

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Abstract. We present a dual Kalman Filter (KF) approach for retrieving states and parameters controlling soil water dynamics in a homogenous soil column by using near-surface state observations. The dual Kalman filter couples a standard KF algorithm for retrieving the states and an unscented KF algorithm for retrieving the parameters. We examine the performance of the dual Kalman Filter applied to two alternative state-space formulations of the Richards equation, respectively differentiated by the type of variable employed for representing the states: either the soil water content (θ) or the soil matric pressure head (h). We use a synthetic time-series series of true states and noise corrupted observations and a synthetic time-series of meteorological forcing. The performance analyses account for the effect of the input parameters, the observation depth and the assimilation frequency as well as the relationship between the retrieved states and the assimilated variables. We show that the identifiability of the parameters is strongly conditioned by several factors, such as the initial guess of the unknown parameters, the wet or dry range of the retrieved states, the boundary conditions, as well as the form (h-based or θ-based) of the state-space formulation. State identifiability is instead efficient even with a relatively coarse time-resolution of the assimilated observation. The accuracy of the retrieved states exhibits limited sensitivity to the observation depth and the assimilation frequency.
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Srikanthan, R., and T. A. McMahon. "Stochastic generation of annual, monthly and daily climate data: A review." Hydrology and Earth System Sciences 5, no. 4 (2001): 653–70. http://dx.doi.org/10.5194/hess-5-653-2001.

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Abstract. The generation of rainfall and other climate data needs a range of models depending on the time and spatial scales involved. Most of the models used previously do not take into account year to year variations in the model parameters. Long periods of wet and dry years were observed in the past but were not taken into account. Recently, Thyer and Kuczera (1999) developed a hidden state Markov model to account for the wet and dry spells explicitly in annual rainfall. This review looks firstly at traditional time series models and then at the more complex models which take account of the pseudo-cycles in the data. Monthly rainfall data have been generated successfully by using the method of fragments. The main criticism of this approach is the repetitions of the same yearly pattern when only a limited number of years of historical data are available. This deficiency has been overcome by using synthetic fragments but this brings an additional problem of generating the right number of months with zero rainfall. Disaggregation schemes are effective in obtaining monthly data but the main problem is the large number of parameters to be estimated when dealing with many sites. Several simplifications have been proposed to overcome this problem. Models for generating daily rainfall are well developed. The transition probability matrix method preserves most of the characteristics of daily, monthly and annual characteristics and is shown to be the best performing model. The two-part model has been shown by many researchers to perform well across a range of climates at the daily level but has not been tested adequately at monthly or annual levels. A shortcoming of the existing models is the consistent underestimation of the variances of the simulated monthly and annual totals. As an alternative, conditioning model parameters on monthly amounts or perturbing the model parameters with the Southern Oscillation Index (SOI) result in better agreement between the variance of the simulated and observed annual rainfall and these approaches should be investigated further. As climate data are less variable than rainfall, but are correlated among themselves and with rainfall, multisite-type models have been used successfully to generate annual data. The monthly climate data can be obtained by disaggregating these annual data. On a daily time step at a site, climate data have been generated using a multisite type model conditional on the state of the present and previous days. The generation of daily climate data at a number of sites remains a challenging problem. If daily rainfall can be modelled successfully by a censored power of normal distribution then the model can be extended easily to generate daily climate data at several sites simultaneously. Most of the early work on the impacts of climate change used historical data adjusted for the climate change. In recent studies, stochastic daily weather generation models are used to compute climate data by adjusting the parameters appropriately for the future climates assumed.
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Books on the topic "Conditional Hesse matrix"

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Optimization of Objective Functions: Analytics. Numerical Methods. Design of Experiments. Fizmatlit Publisher, 2009.

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Conference papers on the topic "Conditional Hesse matrix"

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Abdul Aziz, Khairil Faiz, Azreen Mustafa, Paul Wong, et al. "World First Commercial Application of Permanent Interventionless Monitoring Using Intelligent Inflow Gas Tracer Technology." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/206326-ms.

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Abstract Over the past decade, commercially available inflow tracers have been increasingly used to permanently monitor lower completions without the need for intervention. They have been designed to release selectively to oil or water, typically for clean-up verification, inflow quantification and identifying the location of water breakthrough in oil reservoirs. Naturally, there has been an industry demand and requirement to develop inflow gas tracers to monitor gas reservoirs and identifying the location of gas breakthrough in oil reservoirs. In a green field development, it is important to obtain as much measurements as possible to understand completion efficiency and guide reservoir management decisions. This paper presents the first commercial installation of inflow gas tracer technology that has been deployed in a dry gas field by HESS Malaysia in open hole stand-alone screen completions. It discusses the original monitoring objectives of this application in a full field development and how they evolved due to the gas tracer capabilities and the need for early well and field information. This paper will also discuss the retrofit screen design that allowed the gas tracers embedded in a polymer matrix called gas systems (GS) to be installed inside premium mesh screens. At the wellsite, sampling campaign adjustments were executed depending on the flowing conditions during the clean-up, restarts to obtain relative flow contribution and inflow performance under multi-rate testing conditions. Using a structured approach, the inflow gas monitoring project included feasibility studies, well candidate selection, lessons learnt and developed best practices based on installations in six producing wells in the North Malay Basin (NMB).
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