Literatura académica sobre el tema "Computational methods"
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Artículos de revistas sobre el tema "Computational methods"
ILIE, Marcel, Augustin Semenescu, Gabriela Liliana STROE y 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, n.º 2 (2021): 78–86. http://dx.doi.org/10.56082/annalsarscieng.2021.2.78.
Texto completoMaeda, H. "Computational Methods". Journal of Offshore Mechanics and Arctic Engineering 115, n.º 1 (1 de febrero de 1993): 7–8. http://dx.doi.org/10.1115/1.2920095.
Texto completoSprevak, Mark. "Not All Computational Methods Are Effective Methods". Philosophies 7, n.º 5 (10 de octubre de 2022): 113. http://dx.doi.org/10.3390/philosophies7050113.
Texto completoDutra, Herica Silva, Edinêis de Brito Guirardello, Yin Li y Jeannie P. Cimiotti. "Nurse Burnout Revisited: A Comparison of Computational Methods". Journal of Nursing Measurement 27, n.º 1 (1 de abril de 2019): E17—E33. http://dx.doi.org/10.1891/1061-3749.27.1.e17.
Texto completoCarstensen, Carsten, Björn Engquist y Daniel Peterseim. "Computational Multiscale Methods". Oberwolfach Reports 11, n.º 2 (2014): 1625–81. http://dx.doi.org/10.4171/owr/2014/30.
Texto completoEngquist, Björn y Daniel Peterseim. "Computational Multiscale Methods". Oberwolfach Reports 16, n.º 3 (9 de septiembre de 2020): 2099–181. http://dx.doi.org/10.4171/owr/2019/35.
Texto completo&NA;. "ENGINEERING/COMPUTATIONAL METHODS". ASAIO Journal 42, n.º 2 (abril de 1996): 56–57. http://dx.doi.org/10.1097/00002480-199642020-00011.
Texto completoCross, M. "Computational Galerkin methods". Applied Mathematical Modelling 9, n.º 3 (junio de 1985): 226. http://dx.doi.org/10.1016/0307-904x(85)90012-5.
Texto completoNakazawa, Shohei. "Computational Galerkin methods". Computer Methods in Applied Mechanics and Engineering 50, n.º 2 (agosto de 1985): 199–200. http://dx.doi.org/10.1016/0045-7825(85)90091-x.
Texto completoKourou, K. y DI Fotiadis. "Computational Modelling in Cancer: Methods and Applications". Biomedical Data Journal 01, n.º 1 (enero de 2015): 15–25. http://dx.doi.org/10.11610/bmdj.01103.
Texto completoTesis sobre el tema "Computational methods"
Vakili, Mohammadjavad. "Methods in Computational Cosmology". Thesis, New York University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10260795.
Texto completoState 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.)
Steklova, Klara. "Computational methods in hydrogeophysics". Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/60815.
Texto completoScience, Faculty of
Earth, Ocean and Atmospheric Sciences, Department of
Graduate
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.
Texto completoQC 20130124
Chernyshenko, Dmitri. "Computational methods in micromagnetics". Thesis, University of Southampton, 2016. https://eprints.soton.ac.uk/398126/.
Texto completoArgamon, Shlomo. "Computational methods for counterterrorism". Berlin Heidelberg Springer, 2009. http://d-nb.info/993136176/04.
Texto completoZhu, Tulong. "Meshless methods in computational mechanics". Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/11795.
Texto completoHugtenburg, Richard P. "Computational methods in radiation oncology". Thesis, University of Canterbury. Physics and Astronomy, 1998. http://hdl.handle.net/10092/6796.
Texto completoBertolani, Steve James. "Computational Methods for Modeling Enzymes". Thesis, University of California, Davis, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10928544.
Texto completoEnzymes 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.
Syed, Zeeshan Hassan 1980. "Computational methods for physiological data". Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/54671.
Texto completoAuthor 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.
Fei, Bingxin. "Computational Methods for Option Pricing". Digital WPI, 2011. https://digitalcommons.wpi.edu/etd-theses/381.
Texto completoLibros sobre el tema "Computational methods"
LIU, G. R., V. B. C. TAN y X. HAN, eds. Computational Methods. Dordrecht: Springer Netherlands, 2006. http://dx.doi.org/10.1007/978-1-4020-3953-9.
Texto completoR, Liu G., Tan V. B. C y Han X, eds. Computational methods. Dordrecht: Springer, 2006.
Buscar texto completoModern computational methods. Philadelphia: Taylor & Francis, 1998.
Buscar texto completoJoint, 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.
Buscar texto completoPhillips, C. Computational numerical methods. Hemel Hempstead: Ellis Horwood, 1986.
Buscar texto completoChris, Phillips. Computational numerical methods. Chichester [West Sussex]: Ellis Horwood, 1986.
Buscar texto completoHoffman, Johan. Methods in Computational Science. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2021. http://dx.doi.org/10.1137/1.9781611976724.
Texto completoSirca, Simon y Martin Horvat. Computational Methods for Physicists. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32478-9.
Texto completoGraziani, Frank, ed. Computational Methods in Transport. Berlin/Heidelberg: Springer-Verlag, 2006. http://dx.doi.org/10.1007/3-540-28125-8.
Texto completoWilson, Stephen, ed. Methods in Computational Chemistry. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4615-7416-3.
Texto completoCapítulos de libros sobre el tema "Computational methods"
Wen, Chih-Yung, Yazhong Jiang y Lisong Shi. "Introduction". En Engineering Applications of Computational Methods, 1–5. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0876-9_1.
Texto completoLi, Tjonnie G. F. "Computational Methods". En Extracting Physics from Gravitational Waves, 73–91. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19273-4_5.
Texto completoIto, Tomonori y Toru Akiyama. "Computational Methods". En 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.
Texto completoDemaison, Jean y Natalja Vogt. "Computational Methods". En Lecture Notes in Chemistry, 7–52. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60492-9_2.
Texto completoAndersen, Nils y Klaus Bartschat. "Computational Methods". En 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.
Texto completoThomson, William T. "Computational Methods". En Theory of Vibration with Applications, 234–67. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-6872-2_9.
Texto completoBartók-Pártay, Albert. "Computational Methods". En The Gaussian Approximation Potential, 51–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14067-9_5.
Texto completoPopp, Karl y Werner Schiehlen. "Computational Methods". En Ground Vehicle Dynamics, 239–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68553-1_7.
Texto completoAndersen, Nils y Klaus Bartschat. "Computational Methods". En 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.
Texto completoAndersen, Timothy D. y Chjan C. Lim. "Computational Methods". En 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.
Texto completoActas de conferencias sobre el tema "Computational methods"
Zhang, Xiaofeng y Hailin Zou. "On Computational Tools, Computational Thinking and Computational Methods". En 2009 First International Workshop on Education Technology and Computer Science. ETCS 2009. IEEE, 2009. http://dx.doi.org/10.1109/etcs.2009.120.
Texto completoNayar, Shree. "Advances in Computational Imaging". En Adaptive Optics: Analysis, Methods & Systems. Washington, D.C.: OSA, 2015. http://dx.doi.org/10.1364/aoms.2015.jt1a.2.
Texto completoTay, A. A. O. y K. Y. Lam. "Computational Methods in Engineering". En International Conference on Computational Methods in Engineering. WORLD SCIENTIFIC, 1992. http://dx.doi.org/10.1142/9789814536417.
Texto completoGoncharsky, A. V., A. N. Matvienko, D. O. Savin y A. V. Yasko. "Computational diagnostics of electronic components". En Analytical Methods for Optical Tomography, editado por Gennady G. Levin. SPIE, 1992. http://dx.doi.org/10.1117/12.131901.
Texto completoKaufman, Jonathan J., Gangming Luo, Bruno Bianco, Alessandro Chiabrera y Robert S. Siffert. "Computational methods for NDT". En Nondestructive Evaluation Techniques for Aging Infrastructures & Manufacturing, editado por George Y. Baaklini, Carol A. Nove y Eric S. Boltz. SPIE, 1999. http://dx.doi.org/10.1117/12.339846.
Texto completoCarlos, José, Díaz Ramos, Oscar J. Garay, Eduardo García-Río y Ramón Vázquez-Lorenzo. "Computational methods in Mathematics". En CURVATURE AND VARIATIONAL MODELING IN PHYSICS AND BIOPHYSICS. AIP, 2008. http://dx.doi.org/10.1063/1.2918093.
Texto completo"Computational techniques". En 2008 12th International Conference on Mathematical Methods in Electromagnetic Theory. IEEE, 2008. http://dx.doi.org/10.1109/mmet.2008.4581000.
Texto completoKaijima, Sawako y Panagiotis Michalatos. "Computational Design Consultancy". En eCAADe 2008: Architecture "in computro" - Integrating methods and techniques. eCAADe, 2008. http://dx.doi.org/10.52842/conf.ecaade.2008.311.
Texto completoBieniasz, L. K., George Maroulis y Theodore E. Simos. "A Unifying View of Computational Electrochemistry". En Computational Methods in Science and Engineering. AIP, 2007. http://dx.doi.org/10.1063/1.2827031.
Texto completoVenkatesh, Suresh, Naren Viswanathan y David Schurig. "W-Band Sparse Synthetic Aperture for Computational Imaging". En Adaptive Optics: Analysis, Methods & Systems. Washington, D.C.: OSA, 2015. http://dx.doi.org/10.1364/aoms.2015.jt5a.17.
Texto completoInformes sobre el tema "Computational methods"
Bower, James M. y Christof Koch. Methods in Computational Neuroscience. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 1990. http://dx.doi.org/10.21236/ada231397.
Texto completoBetancourt, O. Computational methods for stellerator configurations. Office of Scientific and Technical Information (OSTI), enero de 1992. http://dx.doi.org/10.2172/5546010.
Texto completoVogel, Curtis R. Computational Methods for Atmospheric Optics. Fort Belvoir, VA: Defense Technical Information Center, agosto de 2002. http://dx.doi.org/10.21236/ada409646.
Texto completoBorah, Bolindra N., Robert E. White, A. Kyrillidis, S. Shankarlingham y Y. Ji. Computational Methods in Continuum Mechanics. Fort Belvoir, VA: Defense Technical Information Center, noviembre de 1993. http://dx.doi.org/10.21236/ada278144.
Texto completoBetancourt, O. Computational methods for stellerator configurations. Office of Scientific and Technical Information (OSTI), enero de 1989. http://dx.doi.org/10.2172/5746101.
Texto completoDupuis, Paul y Harold Kushner. Computational Methods for Stochastic Networks. Fort Belvoir, VA: Defense Technical Information Center, marzo de 2012. http://dx.doi.org/10.21236/ada567599.
Texto completoBorah, Bolindra N., Robert E. White, A. Kyrillidis, S. Shankarlingham y Y. Ji. Computational Methods in Continuum Mechanics. Fort Belvoir, VA: Defense Technical Information Center, noviembre de 1993. http://dx.doi.org/10.21236/ada275560.
Texto completoLasinski, B., D. Larson, D. Hewett, A. Langdon y C. Still. Computational Methods for Collisional Plasma Physics. Office of Scientific and Technical Information (OSTI), febrero de 2004. http://dx.doi.org/10.2172/15009790.
Texto completoBrandt, Achi. Multiscale Computational Methods in Molecular Simulations. Fort Belvoir, VA: Defense Technical Information Center, noviembre de 2001. http://dx.doi.org/10.21236/ada407040.
Texto completoVogel, Curtis R. Computational Methods in Advanced Imaging Sciences. Fort Belvoir, VA: Defense Technical Information Center, mayo de 2006. http://dx.doi.org/10.21236/ada451632.
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