Gotowe bibliografie tematyczne / Computational methods

Gotowa bibliografia na temat „Computational methods”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 25 maja 2024

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Artykuły w czasopismach na temat "Computational methods"

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ILIE, Marcel, Augustin Semenescu, Gabriela Liliana STROE i Sorin BERBENTE. "NUMERICAL COMPUTATIONS OF THE CAVITY FLOWS USING THE POTENTIAL FLOW THEORY". ANNALS OF THE ACADEMY OF ROMANIAN SCIENTISTS Series on ENGINEERING SCIENCES 13, nr 2 (2021): 78–86. http://dx.doi.org/10.56082/annalsarscieng.2021.2.78.

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Computational fluid dynamics of turbulent flows requires large computational resources or are not suitable for the computations of transient flows. Therefore methods such as Reynolds-averaged Navier-Stokes equations are not suitable for the computation of transient flows. The direct numerical simulation provides the most accurate solution, but it is not suitable for high-Reynolds number flows. Large-eddy simulation (LES) approach is computationally less demanding than the DNS but still computationally expensive. Therefore, alternative computational methods must be sought. This research concerns the modelling of inviscid incompressible cavity flow using the potential flow. The numerical methods employed the finite differences approach. The time and space discretization is achieved using second-order schemes. The studies reveal that the finite differences approach is a computationally efficient approach and large computations can be performed on a single computer. The analysis of the flow physics reveals the presence of the recirculation region inside the cavity as well at the corners of the cavity
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Maeda, H. "Computational Methods". Journal of Offshore Mechanics and Arctic Engineering 115, nr 1 (1.02.1993): 7–8. http://dx.doi.org/10.1115/1.2920095.

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Sprevak, Mark. "Not All Computational Methods Are Effective Methods". Philosophies 7, nr 5 (10.10.2022): 113. http://dx.doi.org/10.3390/philosophies7050113.

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An effective method is a computational method that might, in principle, be executed by a human. In this paper, I argue that there are methods for computing that are not effective methods. The examples I consider are taken primarily from quantum computing, but these are only meant to be illustrative of a much wider class. Quantum inference and quantum parallelism involve steps that might be implemented in multiple physical systems, but cannot be implemented, or at least not at will, by an idealised human. Recognising that not all computational methods are effective methods is important for at least two reasons. First, it is needed to correctly state the results of Turing and other founders of computation theory. Turing is sometimes said to have offered a replacement for the informal notion of an effective method with the formal notion of a Turing machine. I argue that such a view only holds under limited circumstances. Second, not distinguishing between computational methods and effective methods can lead to mistakes when quantifying over the class of all possible computational methods. Such quantification is common in philosophy of mind in the context of thought experiments that explore the limits of computational functionalism. I argue that these ‘homuncular’ thought experiments should not be treated as valid.
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Dutra, Herica Silva, Edinêis de Brito Guirardello, Yin Li i Jeannie P. Cimiotti. "Nurse Burnout Revisited: A Comparison of Computational Methods". Journal of Nursing Measurement 27, nr 1 (1.04.2019): E17—E33. http://dx.doi.org/10.1891/1061-3749.27.1.e17.

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Background and PurposeTo examine computational measures of job-related burnout to determine the best computation to estimate job satisfaction and intent to leave in Brazilian nursing professionals.MethodsMaslach Burnout Inventory-Human Services Survey (MBI-HSS) was used assess burnout in 452 hospital-based nursing professionals. Adjusted logistic regression models were fit using different computations of burnout to estimate outcomes of interest.ResultsTotal mean score of burnout subscales was the best estimate of job satisfaction (Cox-Snell R2 = 0.312; Nagelkerke R2 = 0.450) and intent to leave (Cox-Snell R2 = 0.156; Nagelkerke R2 = 0.300), as was high emotional exhaustion (Cox-Snell R2 = 0.219; Nagelkerke R2 = 0.316).ConclusionWe have provided evidence that different computations of data from the Portuguese (Brazil) MBI-HSS can be used in to estimate the effect of job-related burnout on nurse outcomes.
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Carstensen, Carsten, Björn Engquist i Daniel Peterseim. "Computational Multiscale Methods". Oberwolfach Reports 11, nr 2 (2014): 1625–81. http://dx.doi.org/10.4171/owr/2014/30.

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Engquist, Björn, i Daniel Peterseim. "Computational Multiscale Methods". Oberwolfach Reports 16, nr 3 (9.09.2020): 2099–181. http://dx.doi.org/10.4171/owr/2019/35.

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&NA;. "ENGINEERING/COMPUTATIONAL METHODS". ASAIO Journal 42, nr 2 (kwiecień 1996): 56–57. http://dx.doi.org/10.1097/00002480-199642020-00011.

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Cross, M. "Computational Galerkin methods". Applied Mathematical Modelling 9, nr 3 (czerwiec 1985): 226. http://dx.doi.org/10.1016/0307-904x(85)90012-5.

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Nakazawa, Shohei. "Computational Galerkin methods". Computer Methods in Applied Mechanics and Engineering 50, nr 2 (sierpień 1985): 199–200. http://dx.doi.org/10.1016/0045-7825(85)90091-x.

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Kourou, K., i DI Fotiadis. "Computational Modelling in Cancer: Methods and Applications". Biomedical Data Journal 01, nr 1 (styczeń 2015): 15–25. http://dx.doi.org/10.11610/bmdj.01103.

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Rozprawy doktorskie na temat "Computational methods"

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Vakili, Mohammadjavad. "Methods in Computational Cosmology". Thesis, New York University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10260795.

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State of the inhomogeneous universe and its geometry throughout cosmic history can be studied by measuring the clustering of galaxies and the gravitational lensing of distant faint galaxies. Lensing and clustering measurements from large datasets provided by modern galaxy surveys will forever shape our understanding of the how the universe expands and how the structures grow. Interpretation of these rich datasets requires careful characterization of uncertainties at different stages of data analysis: estimation of the signal, estimation of the signal uncertainties, model predictions, and connecting the model to the signal through probabilistic means. In this thesis, we attempt to address some aspects of these challenges.

The first step in cosmological weak lensing analyses is accurate estimation of the distortion of the light profiles of galaxies by large scale structure. These small distortions, known as the cosmic shear signal, are dominated by extra distortions due to telescope optics and atmosphere (in the case of ground-based imaging). This effect is captured by a kernel known as the Point Spread Function (PSF) that needs to be fully estimated and corrected for. We address two challenges a head of accurate PSF modeling for weak lensing studies. The first challenge is finding the centers of point sources that are used for empirical estimation of the PSF. We show that the approximate methods for centroiding stars in wide surveys are able to optimally saturate the information content that is retrievable from astronomical images in the presence of noise.

The fist step in weak lensing studies is estimating the shear signal by accurately measuring the shapes of galaxies. Galaxy shape measurement involves modeling the light profile of galaxies convolved with the light profile of the PSF. Detectors of many space-based telescopes such as the Hubble Space Telescope (HST) sample the PSF with low resolution. Reliable weak lensing analysis of galaxies observed by the HST camera requires knowledge of the PSF at a resolution higher than the pixel resolution of HST. This PSF is called the super-resolution PSF. In particular, we present a forward model of the point sources imaged through filters of the HST WFC3 IR channel. We show that this forward model can accurately estimate the super-resolution PSF. We also introduce a noise model that permits us to robustly analyze the HST WFC3 IR observations of the crowded fields.

Then we try to address one of the theoretical uncertainties in modeling of galaxy clustering on small scales. Study of small scale clustering requires assuming a halo model. Clustering of halos has been shown to depend on halo properties beyond mass such as halo concentration, a phenomenon referred to as assembly bias. Standard large-scale structure studies with halo occupation distribution (HOD) assume that halo mass alone is sufficient to characterize the connection between galaxies and halos. However, assembly bias could cause the modeling of galaxy clustering to face systematic effects if the expected number of galaxies in halos is correlated with other halo properties. Using high resolution N-body simulations and the clustering measurements of Sloan Digital Sky Survey (SDSS) DR7 main galaxy sample, we show that modeling of galaxy clustering can slightly improve if we allow the HOD model to depend on halo properties beyond mass.

One of the key ingredients in precise parameter inference using galaxy clustering is accurate estimation of the error covariance matrix of clustering measurements. This requires generation of many independent galaxy mock catalogs that accurately describe the statistical distribution of galaxies in a wide range of physical scales. We present a fast and accurate method based on low-resolution N-body simulations and an empirical bias model for generating mock catalogs. We use fast particle mesh gravity solvers for generation of dark matter density field and we use Markov Chain Monti Carlo (MCMC) to estimate the bias model that connects dark matter to galaxies. We show that this approach enables the fast generation of mock catalogs that recover clustering at a percent-level accuracy down to quasi-nonlinear scales.

Cosmological datasets are interpreted by specifying likelihood functions that are often assumed to be multivariate Gaussian. Likelihood free approaches such as Approximate Bayesian Computation (ABC) can bypass this assumption by introducing a generative forward model of the data and a distance metric for quantifying the closeness of the data and the model. We present the first application of ABC in large scale structure for constraining the connections between galaxies and dark matter halos. We present an implementation of ABC equipped with Population Monte Carlo and a generative forward model of the data that incorporates sample variance and systematic uncertainties. (Abstract shortened by ProQuest.)

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Steklova, Klara. "Computational methods in hydrogeophysics". Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/60815.

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Parameter and state estimation for groundwater models within a coupled hydrogeophysical framework has become common in the last few years as it has been shown that such estimates are usually better than those from a single data inversion. Different approaches have been suggested in literature to combine the essentially two different modalities in order to obtain better estimates for groundwater models, and improve monitoring of processes such as solute transport. However, the coupled approaches usually come at a price of higher computational cost and difficulties in coupling the geophysical and groundwater inverse problems. Unlike in other studies, we developed both the groundwater and geophysical models in the same computational environment in order to test different minimization strategies. When solving the coupled inverse problem, the objective function consists of data misfit and regularization terms as well as a coupling term that relates groundwater and geophysical states. We present a novel approach to solve the inverse problem using an Alternating Direction Method of Multipliers (ADMM) to minimize the coupled objective function. ADMM enables us to treat the groundwater and geophysical part separately and thus use existing software with minor changes. However, ADMM as well as many other coupled approaches relies on implementing some petrophysical relationship to couple the groundwater and geophysical variable. Such relationships are usually uncertain and hard to parametrize for a large region and can potentially produce solute mass errors in the final model estimates. Therefore, in this thesis we examine coupled approaches that replace the fixed petrophysical relationship by a more loose structure similarity constraint. Besides, we propose efficient computational methods to minimize the objective function when there is no explicit petrophysical constraint. All approaches were tested on 3D synthetic examples. In the solute tracer test we estimated hydraulic conductivity or solute distribution using a structure coupled inversion, and were able to reduce the errors compared to a single data inversion alone. For a more complex example of seawater intrusion we implemented the ADMM method, and obtained better estimates for the solute distribution compared to just considering each data separately, or solving the problem with a simple coupled approach.
Science, Faculty of
Earth, Ocean and Atmospheric Sciences, Department of
Graduate
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af, Klinteberg Ludvig. "Computational methods for microfluidics". Licentiate thesis, KTH, Numerisk analys, NA, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-116384.

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This thesis is concerned with computational methods for fluid flows on the microscale, also known as microfluidics. This is motivated by current research in biological physics and miniaturization technology, where there is a need to understand complex flows involving microscale structures. Numerical simulations are an important tool for doing this. The first paper of the thesis presents a numerical method for simulating multiphase flows involving insoluble surfactants and moving contact lines. The method is based on an explicit interface tracking method, wherein the interface between two fluids is decomposed into segments, which are represented locally on an Eulerian grid. The framework of this method provides a natural setting for solving the advection-diffusion equation governing the surfactant concentration on the interface. Open interfaces and moving contact lines are also incorporated into the method in a natural way, though we show that care must be taken when regularizing interface forces to the grid near the boundary of the computational domain. In the second paper we present a boundary integral formulation for sedimenting particles in periodic Stokes flow, using the completed double layer boundary integral formulation. The long-range nature of the particle-particle interactions lead to the formulation containing sums which are not absolutely convergent if computed directly. This is solved by applying the method of Ewald summation, which in turn is computed in a fast manner by using the FFT-based spectral Ewald method. The complexity of the resulting method is O(N log N), as the system size is scaled up with the number of discretization points N. We apply the method to systems of sedimenting spheroids, which are discretized using the Nyström method and a basic quadrature rule. The Ewald summation method used in the boundary integral method of the second paper requires a decomposition of the potential being summed. In the introductory chapters of the thesis we present an overview of the available methods for creating Ewald decompositions, and show how the methods and decompositions can be related to each other.

QC 20130124

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Chernyshenko, Dmitri. "Computational methods in micromagnetics". Thesis, University of Southampton, 2016. https://eprints.soton.ac.uk/398126/.

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With the continued growth of computational power, computational modelling has become an increasingly important part of modern science. The field of micromagnetism has benefited from the increase of computational power, leading in recent decades to the development of many important micromagnetic methods. This thesis aims to address some computational challenges relevant to the field of micromagnetism today. The computation of the demagnetising field is often the most time-consuming part of a micromagnetic simulation. In the finite difference method, this computation is usually done using the Fourier transform method, in which the demagnetising field is computed as the convolution of the magnetisation field with the demagnetising tensor. An analytical formula for the demagnetising tensor is available, however due to numerical cancellation errors it can only be applied for close distances between the interacting cells. For far distances between the interacting cells other approaches, such as asymptotic expansion, have to be used. In this thesis, we present a new method to compute the demagnetising tensor by means of numerical integration. The method offers improved accuracy over existing methods for the intermediate range of distances. In the finite element method, the computation of the demagnetising field is commonly done using the hybrid FEM/BEM method. The fast multipole method offers potential theoretical advantages over the hybrid FEM/BEM method particularly for the current and future generations of computing hardware. In micromagnetics, it has been applied to compute the demagnetising field in the finite difference setting and to compute the magnetostatic interaction between nanoparticles, however no implementation of the fast multipole method in finite elements is yet available. As one of the steps towards it, in this thesis we develop a new formula for the energy of the magnetostatic interaction between linearly magnetized polyhedrons. This formula can be used to compute the direct interaction between finite element cells in the fast multipole method. Ferromagnetic resonance is a popular experimental technique for probing the dynamical properties of magnetic systems. We extend the eigenvalue method for the computation of resonance modes to the computation of the FMR spectrum, and apply it to compute ferromagnetic resonance for a proposed FMR standard reference problem.
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Argamon, Shlomo. "Computational methods for counterterrorism". Berlin Heidelberg Springer, 2009. http://d-nb.info/993136176/04.

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Zhu, Tulong. "Meshless methods in computational mechanics". Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/11795.

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Hugtenburg, Richard P. "Computational methods in radiation oncology". Thesis, University of Canterbury. Physics and Astronomy, 1998. http://hdl.handle.net/10092/6796.

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This thesis examines computer technology in radiation oncology and the intimate role that it must now play in the dosimetry practices of this discipline. Aspects of the dosimetry and implementation of five radiation therapies are examined, namely total-body irradiation (TBI), total-skin electron therapy (TSET), electron therapy, superficial therapy and ophthalmic brachytherapy. Computational techniques, in particular, Monte Carlo and several other numerical methods are used. The Monte Carlo platform, EGS4, and the treatment planning system, GRATIS, have been implemented on SUN and Silicon Graphics workstations. Monte Carlo methods are used in the investigation of electron therapy planning and superficial and brachytherapy dosimetry practices. Monte Carlo techniques are used for radiation protection calculations of linear accelerator bunker design and for the optimisation of an in vivoX-ray fluorescence (XRF) technique used to measure platinum uptake associated with cisplatin chemotherapy. Inverse Monte Carlo methods have been examined and implemented. Inverse methods, applied to in-phantom dose measurements, are used to determine phase-space information. such as spectra, for an incident electron beam. Analogous methods are examined for megavoltage and superficial X-rays in particular, source parameterisation with attenuation and photoactivation techniques. Two linear accelerators, a Varian 2100C and a Varian 600C, provide Megavoltage X-rays or electrons. The TBI therapy uses a 6 MV X-ray beam. The TSET technique uses 6 MeV electrons which degraded to a lower energy by a screen placed in front of the patient at an extended source distance. The 9 Me V and the 20 Me V electron modalities are also closely examined. Two Philips superficial therapy units, RT100 and RT50 provide 10 through 100 kVp X-rays. 1251 seeds are used for the investigation of ophthalmic brachytherapy dosimetry. Methods of dosimetry incorporated in this work include in-phantom, ionisation chamber and diode measurements. Thermoluminescent dosimeters (TLDs), Silverhalide and radio chromic films are used. Measurements have been performed in water, solid water. polymethyl-methacrylate (PMMA), and polystyrene phantoms. Fricke, ferrous based gels are investigated as a method of dosimetry in a uniform medium. Three-dimensional dose distributions are examined for several radiation modalities. The concentration of radiation-induced ferric ions and hence dose is determined using magnetic resonance imaging (MRI). A high-purity germanium detector and a thalium doped sodium iodide detector are employed for the measurement of source spectra and for fluorescing and activated materials.
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Bertolani, Steve James. "Computational Methods for Modeling Enzymes". Thesis, University of California, Davis, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10928544.

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Enzymes play a crucial role in modern biotechnology, industry, food processing and medical applications. Since their first discovered industrial use, man has attempted to discover new enzymes from Nature to catalyze different chemical reactions. In modern times, with the advent of computational methods, protein structure solutions, protein sequencing and DNA synthesis methods, we now have the tools to enable new approaches to rational enzyme engineering. With an enzyme structure in hand, a researcher may run an in silico experiment to sample different amino acids in the active site in order to identify new combinations which likely stabilize a transition-state-enzyme model. A suggested mutation can then be encoded into the desired enzyme gene, ordered, synthesized and tested. Although this truly astonishing feat of engineering and modern biotechnology allows the redesign of existing enzymes to acquire a new substrate specificity, it still requires a large amount of time, capital and technical capabilities.

Concurrently, while making strides in computational protein design, the cost of sequencing DNA plummeted after the turn of the century. With the reduced cost of sequencing, the number of sequences in public databases of naturally occurring proteins has grown exponentially. This new, large source of information can be utilized to enable rational enzyme design, as long as it can be coupled with accurate modeling of the protein sequences.

This work first describes a novel approach to reengineering enzymes (Genome Enzyme Orthologue Mining; GEO) that utilizes the vast amount of protein sequences in modern databases along with extensive computation modeling and achieves comparable results to the state-of-the-art rational enzyme design methods. Then, inspired by the success of this new method and aware of it's reliance on the accuracy of the protein models, we created a computational benchmark to both measure the accuracy of our models as well as improve it by encoding additional information about the structure, derived from mechanistic studies (Catalytic Geometry constraints; CG). Lastly, we use the improved accuracy method to automatically model hundreds of putative enzymes sequences and dock substrates into them to extract important features that are then used to inform experiments and design. This is used to reengineer a ribonucleotide reductase to catalyze a aldehyde deformylating oxygenase reaction.

These chapters advance the field of rational enzyme engineering, by providing a novel technique that may enable efficient routes to rationally design enzymes for reactions of interest. These chapters also advance the field of homology modeling, in the specific domain in which the researcher is modeling an enzyme with a known chemical reaction. Lastly, these chapters and techniques lead to an example which utilizes highly accurate computational models to create features which can help guide the rational design of enzyme catalysts.

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Syed, Zeeshan Hassan 1980. "Computational methods for physiological data". Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/54671.

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Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2009.
Author is also affiliated with the MIT Dept. of Electrical Engineering and Computer Science. Cataloged from PDF version of thesis.
Includes bibliographical references (p. 177-188).
Large volumes of continuous waveform data are now collected in hospitals. These datasets provide an opportunity to advance medical care, by capturing rare or subtle phenomena associated with specific medical conditions, and by providing fresh insights into disease dynamics over long time scales. We describe how progress in medicine can be accelerated through the use of sophisticated computational methods for the structured analysis of large multi-patient, multi-signal datasets. We propose two new approaches, morphologic variability (MV) and physiological symbolic analysis, for the analysis of continuous long-term signals. MV studies subtle micro-level variations in the shape of physiological signals over long periods. These variations, which are often widely considered to be noise, can contain important information about the state of the underlying system. Symbolic analysis studies the macro-level information in signals by abstracting them into symbolic sequences. Converting continuous waveforms into symbolic sequences facilitates the development of efficient algorithms to discover high risk patterns and patients who are outliers in a population. We apply our methods to the clinical challenge of identifying patients at high risk of cardiovascular mortality (almost 30% of all deaths worldwide each year). When evaluated on ECG data from over 4,500 patients, high MV was strongly associated with both cardiovascular death and sudden cardiac death. MV was a better predictor of these events than other ECG-based metrics. Furthermore, these results were independent of information in echocardiography, clinical characteristics, and biomarkers.
(cont.) Our symbolic analysis techniques also identified groups of patients exhibiting a varying risk of adverse outcomes. One group, with a particular set of symbolic characteristics, showed a 23 fold increased risk of death in the months following a mild heart attack, while another exhibited a 5 fold increased risk of future heart attacks.
by Zeeshan Hassan Syed.
Ph.D.
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Fei, Bingxin. "Computational Methods for Option Pricing". Digital WPI, 2011. https://digitalcommons.wpi.edu/etd-theses/381.

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This paper aims to practice the use of Monte Carlo methods to simulate stock prices in order to price European call options using control variates. American put options are priced using the binomial model separately. Finally, we use the information to form a portfolio position using an Interactive Brokers paper trading account.
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Książki na temat "Computational methods"

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LIU, G. R., V. B. C. TAN i X. HAN, red. Computational Methods. Dordrecht: Springer Netherlands, 2006. http://dx.doi.org/10.1007/978-1-4020-3953-9.

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R, Liu G., Tan V. B. C i Han X, red. Computational methods. Dordrecht: Springer, 2006.

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Modern computational methods. Philadelphia: Taylor & Francis, 1998.

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Joint, ASME/SES Applied Mechanics and Engineering Sciences Conference (1988 Berkeley Calif ). Computational probabilistic methods. New York, N.Y. (345 E. 47th St., New York 10017): American Society of Mechanical Engineers, 1988.

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Phillips, C. Computational numerical methods. Hemel Hempstead: Ellis Horwood, 1986.

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Chris, Phillips. Computational numerical methods. Chichester [West Sussex]: Ellis Horwood, 1986.

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Hoffman, Johan. Methods in Computational Science. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2021. http://dx.doi.org/10.1137/1.9781611976724.

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Sirca, Simon, i Martin Horvat. Computational Methods for Physicists. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32478-9.

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Graziani, Frank, red. Computational Methods in Transport. Berlin/Heidelberg: Springer-Verlag, 2006. http://dx.doi.org/10.1007/3-540-28125-8.

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Wilson, Stephen, red. Methods in Computational Chemistry. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4615-7416-3.

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Części książek na temat "Computational methods"

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Wen, Chih-Yung, Yazhong Jiang i Lisong Shi. "Introduction". W Engineering Applications of Computational Methods, 1–5. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0876-9_1.

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AbstractWith the rapid development of electronic computers, numerical computation has become an important paradigm of scientific discovery as well as a powerful tool for engineering research. Solving complex problems in a computational fashion is more than applying theories, equations, and formulas. Computational methods, also called algorithms or schemes, have strong influences on the outcomes of computations.
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Li, Tjonnie G. F. "Computational Methods". W Extracting Physics from Gravitational Waves, 73–91. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19273-4_5.

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Ito, Tomonori, i Toru Akiyama. "Computational Methods". W Epitaxial Growth of III-Nitride Compounds, 9–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76641-6_2.

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Demaison, Jean, i Natalja Vogt. "Computational Methods". W Lecture Notes in Chemistry, 7–52. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60492-9_2.

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Andersen, Nils, i Klaus Bartschat. "Computational Methods". W Springer Series on Atomic, Optical, and Plasma Physics, 97–124. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55216-3_6.

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Thomson, William T. "Computational Methods". W Theory of Vibration with Applications, 234–67. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-6872-2_9.

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Bartók-Pártay, Albert. "Computational Methods". W The Gaussian Approximation Potential, 51–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14067-9_5.

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Popp, Karl, i Werner Schiehlen. "Computational Methods". W Ground Vehicle Dynamics, 239–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68553-1_7.

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Andersen, Nils, i Klaus Bartschat. "Computational Methods". W Springer Series on Atomic, Optical, and Plasma Physics, 87–109. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0187-5_6.

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Andersen, Timothy D., i Chjan C. Lim. "Computational Methods". W Springer Monographs in Mathematics, 99–109. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1938-3_8.

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Streszczenia konferencji na temat "Computational methods"

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Zhang, Xiaofeng, i Hailin Zou. "On Computational Tools, Computational Thinking and Computational Methods". W 2009 First International Workshop on Education Technology and Computer Science. ETCS 2009. IEEE, 2009. http://dx.doi.org/10.1109/etcs.2009.120.

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Nayar, Shree. "Advances in Computational Imaging". W Adaptive Optics: Analysis, Methods & Systems. Washington, D.C.: OSA, 2015. http://dx.doi.org/10.1364/aoms.2015.jt1a.2.

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Tay, A. A. O., i K. Y. Lam. "Computational Methods in Engineering". W International Conference on Computational Methods in Engineering. WORLD SCIENTIFIC, 1992. http://dx.doi.org/10.1142/9789814536417.

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Goncharsky, A. V., A. N. Matvienko, D. O. Savin i A. V. Yasko. "Computational diagnostics of electronic components". W Analytical Methods for Optical Tomography, redaktor Gennady G. Levin. SPIE, 1992. http://dx.doi.org/10.1117/12.131901.

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Kaufman, Jonathan J., Gangming Luo, Bruno Bianco, Alessandro Chiabrera i Robert S. Siffert. "Computational methods for NDT". W Nondestructive Evaluation Techniques for Aging Infrastructures & Manufacturing, redaktorzy George Y. Baaklini, Carol A. Nove i Eric S. Boltz. SPIE, 1999. http://dx.doi.org/10.1117/12.339846.

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Carlos, José, Díaz Ramos, Oscar J. Garay, Eduardo García-Río i Ramón Vázquez-Lorenzo. "Computational methods in Mathematics". W CURVATURE AND VARIATIONAL MODELING IN PHYSICS AND BIOPHYSICS. AIP, 2008. http://dx.doi.org/10.1063/1.2918093.

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"Computational techniques". W 2008 12th International Conference on Mathematical Methods in Electromagnetic Theory. IEEE, 2008. http://dx.doi.org/10.1109/mmet.2008.4581000.

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Kaijima, Sawako, i Panagiotis Michalatos. "Computational Design Consultancy". W eCAADe 2008: Architecture "in computro" - Integrating methods and techniques. eCAADe, 2008. http://dx.doi.org/10.52842/conf.ecaade.2008.311.

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Bieniasz, L. K., George Maroulis i Theodore E. Simos. "A Unifying View of Computational Electrochemistry". W Computational Methods in Science and Engineering. AIP, 2007. http://dx.doi.org/10.1063/1.2827031.

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Venkatesh, Suresh, Naren Viswanathan i David Schurig. "W-Band Sparse Synthetic Aperture for Computational Imaging". W Adaptive Optics: Analysis, Methods & Systems. Washington, D.C.: OSA, 2015. http://dx.doi.org/10.1364/aoms.2015.jt5a.17.

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Raporty organizacyjne na temat "Computational methods"

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Bower, James M., i Christof Koch. Methods in Computational Neuroscience. Fort Belvoir, VA: Defense Technical Information Center, wrzesień 1990. http://dx.doi.org/10.21236/ada231397.

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Betancourt, O. Computational methods for stellerator configurations. Office of Scientific and Technical Information (OSTI), styczeń 1992. http://dx.doi.org/10.2172/5546010.

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Vogel, Curtis R. Computational Methods for Atmospheric Optics. Fort Belvoir, VA: Defense Technical Information Center, sierpień 2002. http://dx.doi.org/10.21236/ada409646.

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Borah, Bolindra N., Robert E. White, A. Kyrillidis, S. Shankarlingham i Y. Ji. Computational Methods in Continuum Mechanics. Fort Belvoir, VA: Defense Technical Information Center, listopad 1993. http://dx.doi.org/10.21236/ada278144.

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Betancourt, O. Computational methods for stellerator configurations. Office of Scientific and Technical Information (OSTI), styczeń 1989. http://dx.doi.org/10.2172/5746101.

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Dupuis, Paul, i Harold Kushner. Computational Methods for Stochastic Networks. Fort Belvoir, VA: Defense Technical Information Center, marzec 2012. http://dx.doi.org/10.21236/ada567599.

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Borah, Bolindra N., Robert E. White, A. Kyrillidis, S. Shankarlingham i Y. Ji. Computational Methods in Continuum Mechanics. Fort Belvoir, VA: Defense Technical Information Center, listopad 1993. http://dx.doi.org/10.21236/ada275560.

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Lasinski, B., D. Larson, D. Hewett, A. Langdon i C. Still. Computational Methods for Collisional Plasma Physics. Office of Scientific and Technical Information (OSTI), luty 2004. http://dx.doi.org/10.2172/15009790.

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Brandt, Achi. Multiscale Computational Methods in Molecular Simulations. Fort Belvoir, VA: Defense Technical Information Center, listopad 2001. http://dx.doi.org/10.21236/ada407040.

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Vogel, Curtis R. Computational Methods in Advanced Imaging Sciences. Fort Belvoir, VA: Defense Technical Information Center, maj 2006. http://dx.doi.org/10.21236/ada451632.

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