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Thermal tomography is a method for recovering heterogeneous thermal properties employing only boundary measurements. This paper focuses on the development of efficient inverse solvers for scenarios where the evolution of boundary conditions can vary in time. A transient heat model with two material parameters – volumetric capacity and a coefficient of thermal conductivity – is introduced for the description of the underlying physical phenomena. All proposed identification algorithms are deterministic methods based on a regularised Gauss-Newton method. A basic framework, implementation details, and the modification of general constraints initially derived for a standard setup of the Calderón problem are discussed here. Moreover, the algorithms are numerically verified for numerous examples, and results obtained show that the inverse problem exhibits a certain degree of ambiguity for a particular measurement-loading scenario. In other words, the important material property minimising the magnitude of error of the objective function seems to be the effusivity field rather than accurate identification of the individual thermal fields.
Jan Havelka; Anna Kučerová; Jan Sýkora. Efficient inverse solvers for thermal tomography. Computers & Mathematics with Applications 2021, 97, 314 -328.
AMA StyleJan Havelka, Anna Kučerová, Jan Sýkora. Efficient inverse solvers for thermal tomography. Computers & Mathematics with Applications. 2021; 97 ():314-328.
Chicago/Turabian StyleJan Havelka; Anna Kučerová; Jan Sýkora. 2021. "Efficient inverse solvers for thermal tomography." Computers & Mathematics with Applications 97, no. : 314-328.
This paper presents a combined experimental and numerical investigation of the behavior of glued laminated timber beams when exposed to fire. The influence on the time variation of charring rates based on the evolution of temperature profiles is examined for different fire scenarios and durations as well as different beam’s cross-section sizes. Predictions of charring depths provided by numerical simulations of heat transfer and simplified charring rate models are compared. In the absence of a mass transport representation, a Bayesian inference is introduced to identify the temperature-dependent material parameters for the conventional heat transfer model. A similar approach is adopted when adjusting the selected parameters of the charring rate models to account for variations in actual fire scenarios, which potentially depart from standard fire exposure. When compared to experimental results, both approaches confirmed their predictive capabilities, particularly in the stage of initial design. Since presented in the framework of Bayesian statistics, they open the door to fully stochastic analysis with an emphasis on the load bearing capacity of the studied beams.
Lucie Kucíková; Tomáš Janda; Jan Sýkora; Michal Šejnoha; Guido Marseglia. Experimental and numerical investigation of the response of GLT beams exposed to fire. Construction and Building Materials 2021, 299, 123846 .
AMA StyleLucie Kucíková, Tomáš Janda, Jan Sýkora, Michal Šejnoha, Guido Marseglia. Experimental and numerical investigation of the response of GLT beams exposed to fire. Construction and Building Materials. 2021; 299 ():123846.
Chicago/Turabian StyleLucie Kucíková; Tomáš Janda; Jan Sýkora; Michal Šejnoha; Guido Marseglia. 2021. "Experimental and numerical investigation of the response of GLT beams exposed to fire." Construction and Building Materials 299, no. : 123846.
Heating wood to high temperature changes either temporarily or permanently its physical properties. This issue is addressed in the present contribution by examining the effect of high temperature on residual mechanical properties of spruce wood, grounding on the results of full-scale fire tests performed on GLT beams. Given these tests, a computational model was developed to provide through-thickness temperature profiles allowing for the estimation of a charring depth on the one hand and on the other hand assigning a particular temperature to each specimen used subsequently in small-scale tensile tests. The measured Young’s moduli and tensile strengths were accompanied by the results from three-point bending test carried out on two groups of beams exposed to fire of a variable duration and differing in the width of the cross-section,
Lucie Kucíková; Michal Šejnoha; Tomáš Janda; Jan Sýkora; Pavel Padevět; Guido Marseglia. Mechanical Properties of Spruce Wood Extracted from GLT Beams Loaded by Fire. Sustainability 2021, 13, 5494 .
AMA StyleLucie Kucíková, Michal Šejnoha, Tomáš Janda, Jan Sýkora, Pavel Padevět, Guido Marseglia. Mechanical Properties of Spruce Wood Extracted from GLT Beams Loaded by Fire. Sustainability. 2021; 13 (10):5494.
Chicago/Turabian StyleLucie Kucíková; Michal Šejnoha; Tomáš Janda; Jan Sýkora; Pavel Padevět; Guido Marseglia. 2021. "Mechanical Properties of Spruce Wood Extracted from GLT Beams Loaded by Fire." Sustainability 13, no. 10: 5494.
A micromechanics based approach is outlined in this paper to predict evolution of moisture induced strains in spruce wood. Both analytical and numerical homogenization techniques are adopted first to provide estimates of effective coefficients of hygroexpansion to be multiplied by the current change in moisture content. This latter quantity is addressed next within the framework of non-Fickian constitutive model. Experimental measurements of coefficients of hygroexpansion exploiting the digital image correlation as well as determination of moisture transport using the cup model are carried out to support both applicability and numerical implementation of the presented approach.
Michal Šejnoha; Jan Sýkora; Jan Vorel; Lucie Kucíková; Jakub Antoš; Jaroslav Pokorný; Zbyšek Pavlík. Moisture induced strains in spruce from homogenization and transient moisture transport analysis. Computers & Structures 2019, 220, 114 -130.
AMA StyleMichal Šejnoha, Jan Sýkora, Jan Vorel, Lucie Kucíková, Jakub Antoš, Jaroslav Pokorný, Zbyšek Pavlík. Moisture induced strains in spruce from homogenization and transient moisture transport analysis. Computers & Structures. 2019; 220 ():114-130.
Chicago/Turabian StyleMichal Šejnoha; Jan Sýkora; Jan Vorel; Lucie Kucíková; Jakub Antoš; Jaroslav Pokorný; Zbyšek Pavlík. 2019. "Moisture induced strains in spruce from homogenization and transient moisture transport analysis." Computers & Structures 220, no. : 114-130.
Estimation of material parameters plays an important role in many scientific fields ranging from geophysics, medical imaging, archaeology, material science to the preservation of historical structures. This paper focuses on the civil engineering problem of heat transfer in cases where an intervention into a structure might not be allowed and where estimation of the material parameter can be conducted using only boundary measurements. For two decades, thermal tomography has addressed such scenarios. This study introduces a novel approach for recovering spatially distributed thermal properties based on the random field theory, which efficiently parametrizes the unknown parameter fields. The proposed approach is verified computationally and the results achieved correspond well to those provided by standard thermal tomography procedures.
Jan Havelka; Anna Kučerová; Jan Sýkora. Dimensionality reduction in thermal tomography. Computers & Mathematics with Applications 2019, 78, 3077 -3089.
AMA StyleJan Havelka, Anna Kučerová, Jan Sýkora. Dimensionality reduction in thermal tomography. Computers & Mathematics with Applications. 2019; 78 (9):3077-3089.
Chicago/Turabian StyleJan Havelka; Anna Kučerová; Jan Sýkora. 2019. "Dimensionality reduction in thermal tomography." Computers & Mathematics with Applications 78, no. 9: 3077-3089.
Jan Havelka; Jan Sýkora. Application of Calderón's inverse problem in civil engineering. Applications of Mathematics 2018, 63, 687 -712.
AMA StyleJan Havelka, Jan Sýkora. Application of Calderón's inverse problem in civil engineering. Applications of Mathematics. 2018; 63 (6):687-712.
Chicago/Turabian StyleJan Havelka; Jan Sýkora. 2018. "Application of Calderón's inverse problem in civil engineering." Applications of Mathematics 63, no. 6: 687-712.
Eliška Janouchová; Jan Sýkora; Anna Kučerová. Polynomial chaos in evaluating failure probability: A comparative study. Applications of Mathematics 2018, 63, 713 -737.
AMA StyleEliška Janouchová, Jan Sýkora, Anna Kučerová. Polynomial chaos in evaluating failure probability: A comparative study. Applications of Mathematics. 2018; 63 (6):713-737.
Chicago/Turabian StyleEliška Janouchová; Jan Sýkora; Anna Kučerová. 2018. "Polynomial chaos in evaluating failure probability: A comparative study." Applications of Mathematics 63, no. 6: 713-737.
Jan Sýkora; Daniela Jarušková; Michal Šejnoha; Jiří Šejnoha. Fire risk analysis focused on damage of the tunnel lining. Fire Safety Journal 2018, 95, 51 -65.
AMA StyleJan Sýkora, Daniela Jarušková, Michal Šejnoha, Jiří Šejnoha. Fire risk analysis focused on damage of the tunnel lining. Fire Safety Journal. 2018; 95 ():51-65.
Chicago/Turabian StyleJan Sýkora; Daniela Jarušková; Michal Šejnoha; Jiří Šejnoha. 2018. "Fire risk analysis focused on damage of the tunnel lining." Fire Safety Journal 95, no. : 51-65.
Advances in meta-modelling and increasing computational capacity of modern computerspermitted many researches to focus on parameter identification in probabilistic setting. Increasinglypopular Bayesian inference allows to estimate model parameters together with corresponding epistemicuncertainties from indirect experimental measurements. However in case of a heterogeneousmaterial model, the identification procedure has to be able to quantify the aleatory uncertainties capturingthe variability of the material properties. Parameter identification of a heterogeneous materialmodel can be formulated as a search for probabilistic description of its parameters providing the distributionof the model response corresponding to the distribution of the observed data, i.e. a stochasticinversion problem. By prescribing a specific type of probability distribution to the model parameterswith corresponding uncertain moments, the task changes to the identification of these so-calledhyperparameters of the distribution which can be inferred in the Bayesian way.
Eliška Janouchová; Anna Kučerová; Jan Sýkora. Bayesian Updating of Aleatory Uncertainties in Heterogeneous Materials. Advanced Materials Research 2017, 1144, 136 -141.
AMA StyleEliška Janouchová, Anna Kučerová, Jan Sýkora. Bayesian Updating of Aleatory Uncertainties in Heterogeneous Materials. Advanced Materials Research. 2017; 1144 ():136-141.
Chicago/Turabian StyleEliška Janouchová; Anna Kučerová; Jan Sýkora. 2017. "Bayesian Updating of Aleatory Uncertainties in Heterogeneous Materials." Advanced Materials Research 1144, no. : 136-141.
In order to obtain material properties of a given system it is convenient in many casesto perform only noninvasive, i.e. boundary measurements. Our interest is then focused on buildingmaterials and their functionality when exposed to heat and moisture. To describe the underlying phenomena of heat and moisture transfer we use Künzel model with stochastic description of materialproperties for its relative simplicity and sufficient accuracy. For the inverse procedure weintend to utilize the Calderón’s problem framework which is regularly used in medical imaging asElectrical Impedance Tomography and is based on knowing Dirichlet-to-Neumann or Neumannto-Dirichlet map.Overall this work serves as a preliminary study of both aforementioned computational models andits goal is therefore to build up a solid foundations for further redefinition of both models in order tofit the realistic loading conditions for building structures.
Jan Havelka; Jan Sýkora. Künzel Model and Boundary Inverse Problem. Advanced Materials Research 2017, 1144, 115 -120.
AMA StyleJan Havelka, Jan Sýkora. Künzel Model and Boundary Inverse Problem. Advanced Materials Research. 2017; 1144 ():115-120.
Chicago/Turabian StyleJan Havelka; Jan Sýkora. 2017. "Künzel Model and Boundary Inverse Problem." Advanced Materials Research 1144, no. : 115-120.
Microstructure reconstruction and compression techniques are designed to identify microstructures with desired properties. While a microstructure reconstruction involves searching for a microstructure with prescribed statistical properties, a microstructure compression focuses on efficient representation of material morphology for the purpose of multiscale modelling. Successful application of these techniques, nevertheless, requires proper understanding of the underlying statistical descriptors quantifying morphology of a material. In this paper, we focus on a lineal path function designed to capture short-range effects and phase connectedness, which can hardly be handled by the commonly used two-point probability function. Usage of the lineal path function is, however, significantly limited because of huge computational requirements. So as to examine the properties of the lineal path function during computationally exhaustive compression and reconstruction processes, we start with an acceleration of the lineal path evaluation, namely by porting part of its code to a graphics processing unit using the CUDA (Compute Unified Device Architecture) programming environment. This allows us to present a unique comparison of the entire lineal path function with the commonly used rough approximation based on the Monte Carlo and/or sampling template. Moreover, this accelerated version of the lineal path function is then compared to the two-point probability function during the compression and reconstruction of two-phase morphologies. Their significant features are discussed and illustrated using a set of artificial periodic as well as real-world random microstructures. Graphical
Jan Havelka; Anna Kučerová; Jan Sýkora. Compression and reconstruction of random microstructures using accelerated lineal path function. Computational Materials Science 2016, 122, 102 -117.
AMA StyleJan Havelka, Anna Kučerová, Jan Sýkora. Compression and reconstruction of random microstructures using accelerated lineal path function. Computational Materials Science. 2016; 122 ():102-117.
Chicago/Turabian StyleJan Havelka; Anna Kučerová; Jan Sýkora. 2016. "Compression and reconstruction of random microstructures using accelerated lineal path function." Computational Materials Science 122, no. : 102-117.
The calibration of a heterogeneous material model can be formulated as a search for probabilistic description of its parameters providing the distribution of the model response corresponding to the distribution of the observed data. This contribution is focused on developing a method for identification of parameters along with their variations based on combining measurements from different types of destructive experiments.
Eliška Janouchová; Anna Kučerová; Jan Sýkora. Stochastic Model Calibration Based on Measurements from Different Experiments. Applied Mechanics and Materials 2016, 825, 135 -140.
AMA StyleEliška Janouchová, Anna Kučerová, Jan Sýkora. Stochastic Model Calibration Based on Measurements from Different Experiments. Applied Mechanics and Materials. 2016; 825 ():135-140.
Chicago/Turabian StyleEliška Janouchová; Anna Kučerová; Jan Sýkora. 2016. "Stochastic Model Calibration Based on Measurements from Different Experiments." Applied Mechanics and Materials 825, no. : 135-140.
In this contribution we focus on the computational aspects for practical use of the uncertainty propagation in groundwater flow environment using stochastic finite element method based on generalized polynomial chaos (gPC), where the uncertain part is taking place only in the spatial distribution of the transport properties. In recent years, there has been a growing trend towards real world applications in computational mechanics, thus the reduction techniques have become very desirable. Our focus is on efficient Matlab implementation in terms of computational time and memory consumption without modifying the mathematical background.
Jan Havelka; Jan Sýkora; Anna Kučerová. Algorithmic Framework for Stochastic Galerkin Method. Applied Mechanics and Materials 2016, 825, 123 -128.
AMA StyleJan Havelka, Jan Sýkora, Anna Kučerová. Algorithmic Framework for Stochastic Galerkin Method. Applied Mechanics and Materials. 2016; 825 ():123-128.
Chicago/Turabian StyleJan Havelka; Jan Sýkora; Anna Kučerová. 2016. "Algorithmic Framework for Stochastic Galerkin Method." Applied Mechanics and Materials 825, no. : 123-128.
Microstructure reconstruction and compression techniques are designed to find a microstructure with desired properties. While the microstructure reconstruction searches for a microstructure with prescribed statistical properties, the microstructure compression focuses on efficient representation of material morphology for a purpose of multiscale modelling. Successful application of those techniques, nevertheless, requires proper understanding of underlying statistical descriptors quantifying material morphology. In this paper we focus on the lineal path function designed to capture namely short-range effects and phase connectedness, which can be hardly handled by the commonly used two-point probability function. The usage of the lineal path function is, however, significantly limited by huge computational requirements. So as to examine the properties of the lineal path function within the computationally exhaustive compression and reconstruction processes, we start with the acceleration of the lineal path evaluation, namely by porting part of its code to the graphics processing unit using the CUDA (Compute Unified Device Architecture) programming environment. This allows us to present a unique comparison of the entire lineal path function with the commonly used rough approximation based on the Monte Carlo and/or sampling template. Moreover, the accelerated version of the lineal path function is then compared with the two-point probability function within the compression and reconstruction of two-phase morphologies. Their significant features are thoroughly discussed and illustrated on a set of artificial periodic as well as real-world random microstructures.
Jan Havelka; Anna Kučerová; Jan Sýkora. Compression and Reconstruction of Random Microstructures using Accelerated Lineal Path Function. 2016, 1 .
AMA StyleJan Havelka, Anna Kučerová, Jan Sýkora. Compression and Reconstruction of Random Microstructures using Accelerated Lineal Path Function. . 2016; ():1.
Chicago/Turabian StyleJan Havelka; Anna Kučerová; Jan Sýkora. 2016. "Compression and Reconstruction of Random Microstructures using Accelerated Lineal Path Function." , no. : 1.
Homogenization of a simultaneous heat and moisture flow in a masonry wall is presented in this paper. The principle objective is to examine an impact of the assumed imperfect hydraulic contact on the resulting homogenized properties. Such a contact is characterized by a certain mismatching resistance allowing us to represent a discontinuous evolution of temperature and moisture fields across the interface, which is in general attributed to discontinuous capillary pressures caused by different pore size distributions of the adjacent porous materials. In achieving this, two particular laboratory experiments were performed to provide distributions of temperature and relative humidity in a sample of the masonry wall, which in turn served to extract the corresponding jumps and subsequently to obtain the required interface transition parameters by matching numerical predictions and experimental results. The results suggest a low importance of accounting for imperfect hydraulic contact for the derivation of macroscopic homogenized properties. On the other hand, they strongly support the need for a fully coupled multi-scale analysis due to significant dependence of the homogenized properties on actual moisture gradients and corresponding values of both macroscopic temperature and relative humidity.
Jan Sýkora; Michal Šejnoha; Jiří Šejnoha. Homogenization of coupled heat and moisture transport in masonry structures including interfaces. Applied Mathematics and Computation 2013, 219, 7275 -7285.
AMA StyleJan Sýkora, Michal Šejnoha, Jiří Šejnoha. Homogenization of coupled heat and moisture transport in masonry structures including interfaces. Applied Mathematics and Computation. 2013; 219 (13):7275-7285.
Chicago/Turabian StyleJan Sýkora; Michal Šejnoha; Jiří Šejnoha. 2013. "Homogenization of coupled heat and moisture transport in masonry structures including interfaces." Applied Mathematics and Computation 219, no. 13: 7275-7285.
To assess the durability of structures, heat and moisture transport need to be analyzed. To provide a reliable estimation of heat and moisture distribution in a certain structure, one needs to include all available information about the loading conditions and material parameters. Moreover, the information should be accompanied by a corresponding evaluation of its credibility. Here, the Bayesian inference is applied to combine different sources of information, so as to provide a more accurate estimation of heat and moisture fields [1]. The procedure is demonstrated on the probabilistic description of heterogeneous material where the uncertainties consist of a particular value of individual material characteristic and spatial fluctuations. As for the heat and moisture transfer, it is modelled in coupled setting [2].
Anna Kučerová; Jan Sýkora. Uncertainty updating in the description of coupled heat and moisture transport in heterogeneous materials. Applied Mathematics and Computation 2013, 219, 7252 -7261.
AMA StyleAnna Kučerová, Jan Sýkora. Uncertainty updating in the description of coupled heat and moisture transport in heterogeneous materials. Applied Mathematics and Computation. 2013; 219 (13):7252-7261.
Chicago/Turabian StyleAnna Kučerová; Jan Sýkora. 2013. "Uncertainty updating in the description of coupled heat and moisture transport in heterogeneous materials." Applied Mathematics and Computation 219, no. 13: 7252-7261.
A fully coupled transient heat and moisture transport in a masonry structure is examined in this paper. Supported by several successful applications in civil engineering the nonlinear diffusion model proposed by Künzel (1997) [16] is adopted in the present study. A strong material heterogeneity together with a significant dependence of the model parameters on initial conditions as well as the gradients of heat and moisture fields vindicates the use of a hierarchical modeling strategy to solve the problem of this kind. Attention is limited to the classical first order homogenization in a spatial domain developed here in the framework of a two step (meso–macro) multi-scale computational scheme (FE2 problem). Several illustrative examples are presented to investigate the influence of transient flow at the level of constituents (meso-scale) on the macroscopic response including the effect of macro-scale boundary conditions. A two-dimensional section of Charles Bridge subjected to actual climatic conditions is analyzed next to confirm the suitability of algorithmic format of FE2 scheme for the parallel computing.
Jan Sýkora; Tomáš Krejčí; Jaroslav Kruis; Michal Šejnoha. Computational homogenization of non-stationary transport processes in masonry structures. Journal of Computational and Applied Mathematics 2012, 236, 4745 -4755.
AMA StyleJan Sýkora, Tomáš Krejčí, Jaroslav Kruis, Michal Šejnoha. Computational homogenization of non-stationary transport processes in masonry structures. Journal of Computational and Applied Mathematics. 2012; 236 (18):4745-4755.
Chicago/Turabian StyleJan Sýkora; Tomáš Krejčí; Jaroslav Kruis; Michal Šejnoha. 2012. "Computational homogenization of non-stationary transport processes in masonry structures." Journal of Computational and Applied Mathematics 236, no. 18: 4745-4755.
The prediction of thermo-mechanical behaviour of heterogeneous materials such as heat and moisture transport is strongly influenced by the uncertainty in parameters. Such materials occur e.g., in historic buildings, and the durability assessment of these therefore needs a reliable and probabilistic simulation of transport processes, which is related to the suitable identification of material parameters. In order to include expert knowledge as well as experimental results, one can employ an updating procedure such as Bayesian inference. The classical probabilistic setting of the identification process in Bayes’ form requires the solution of a stochastic forward problem via computationally expensive sampling techniques, which makes the method almost impractical. In this paper novel stochastic computational techniques such as the stochastic Galerkin method are applied in order to accelerate the updating procedure. The idea is to replace the computationally expensive forward simulation via the conventional finite element (FE) method by the evaluation of a polynomial chaos expansion (PCE). Such an approximation of the FE model for the forward simulation perfectly suits the Bayesian updating. The presented uncertainty updating techniques are applied to the numerical model of coupled heat and moisture transport in heterogeneous materials with spatially varying coefficients defined by random fields.
Anna Kučerová; Jan Sýkora; Bojana Rosić; Hermann G. Matthies. Acceleration of uncertainty updating in the description of transport processes in heterogeneous materials. Journal of Computational and Applied Mathematics 2012, 236, 4862 -4872.
AMA StyleAnna Kučerová, Jan Sýkora, Bojana Rosić, Hermann G. Matthies. Acceleration of uncertainty updating in the description of transport processes in heterogeneous materials. Journal of Computational and Applied Mathematics. 2012; 236 (18):4862-4872.
Chicago/Turabian StyleAnna Kučerová; Jan Sýkora; Bojana Rosić; Hermann G. Matthies. 2012. "Acceleration of uncertainty updating in the description of transport processes in heterogeneous materials." Journal of Computational and Applied Mathematics 236, no. 18: 4862-4872.
The paper reviews several topics associated with the homogenization of transport processed in historical masonry structures. Since these often experience an irregular or random pattern, we open the subject by summarizing essential steps in the formulation of a suitable computational model in the form of Statistically Equivalent Periodic Unit Cell (SEPUC). Accepting SEPUC as a reliable representative volume element is supported by application of the Fast Fourier Transform to both the SEPUC and large binary sample of real masonry in search for effective thermal conductivities limited here to a steady state heat conduction problem. Fully coupled non-stationary heat and moisture transport is addressed next in the framework of two-scale first-order homogenization approach with emphases on the application of boundary and initial conditions on the meso-scale.
Jan Sýkora; Jan Zeman; Michal Sejnoha. Selected topics in homogenization of transport processes in historical masonry structures. 2012, 1 .
AMA StyleJan Sýkora, Jan Zeman, Michal Sejnoha. Selected topics in homogenization of transport processes in historical masonry structures. . 2012; ():1.
Chicago/Turabian StyleJan Sýkora; Jan Zeman; Michal Sejnoha. 2012. "Selected topics in homogenization of transport processes in historical masonry structures." , no. : 1.
Parameter identification problems are formulated in a probabilistic language, where the randomness reflects the uncertainty about the knowledge of the true values. This setting allows conceptually easily to incorporate new information, e.g. through a measurement, by connecting it to Bayes's theorem. The unknown quantity is modelled as a (may be high-dimensional) random variable. Such a description has two constituents, the measurable function and the measure. One group of methods is identified as updating the measure, the other group changes the measurable function. We connect both groups with the relatively recent methods of functional approximation of stochastic problems, and introduce especially in combination with the second group of methods a new procedure which does not need any sampling, hence works completely deterministically. It also seems to be the fastest and more reliable when compared with other methods. We show by example that it also works for highly nonlinear non-smooth problems with non-Gaussian measures.
Bojana V. Rosić; Anna Kučerová; Jan Sýkora; Oliver Pajonk; Alexander Litvinenko; Hermann G. Matthies. Parameter Identification in a Probabilistic Setting. 2012, 1 .
AMA StyleBojana V. Rosić, Anna Kučerová, Jan Sýkora, Oliver Pajonk, Alexander Litvinenko, Hermann G. Matthies. Parameter Identification in a Probabilistic Setting. . 2012; ():1.
Chicago/Turabian StyleBojana V. Rosić; Anna Kučerová; Jan Sýkora; Oliver Pajonk; Alexander Litvinenko; Hermann G. Matthies. 2012. "Parameter Identification in a Probabilistic Setting." , no. : 1.