Relevant bibliographies by topics / Computational analysis

Academic literature on the topic 'Computational analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 September 2023

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Journal articles on the topic "Computational analysis"

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Liu, G. R. "Computational methods for certified solutions, adaptive analysis, real-time computation, and inverse analysis of mechanics problem." Proceedings of The Computational Mechanics Conference 2011.24 (2011): _—1_—_—5_. http://dx.doi.org/10.1299/jsmecmd.2011.24._-1_.

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ILIE, Marcel, Augustin Semenescu, Gabriela Liliana STROE, and 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, no. 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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Juneja, Shall, Deepayan Mukherjee, and Sachi Garg. "Computational Analysis of RNA Nucleotide Sequences." International Journal of Trend in Scientific Research and Development Volume-3, Issue-2 (February 28, 2019): 369–72. http://dx.doi.org/10.31142/ijtsrd21342.

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Fajdiga, Gorazd. "Computational fatigue analysis of contacting mechanical elements." Tehnicki vjesnik - Technical Gazette 22, no. 1 (2015): 169–75. http://dx.doi.org/10.17559/tv-20140429122305.

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Bhardwaj, Shalini, and Yashwant Buke. "Computational Fluid Dynamics Analysis of A Turbocharger System." International Journal of Scientific Research 3, no. 5 (June 1, 2012): 161–64. http://dx.doi.org/10.15373/22778179/may2014/49.

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Gao, Feng, Gang Li, Rui Hu, and Hiroshi Okada. "Computational Fluid Dynamic Analysis of Coronary Artery Stenting." International Journal of Bioscience, Biochemistry and Bioinformatics 4, no. 3 (2014): 155–59. http://dx.doi.org/10.7763/ijbbb.2014.v4.330.

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LESSNER, Daniel. "ANALYSIS OF TERM MEANING "COMPUTATIONAL THINKING"." Journal of Technology and Information 6, no. 1 (April 1, 2014): 71–88. http://dx.doi.org/10.5507/jtie.2014.006.

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Takizawa, Kenji, Yuri Bazilevs, Tayfun E. Tezduyar, Ming-Chen Hsu, and Takuya Terahara. "Computational Cardiovascular Medicine With Isogeometric Analysis." Journal of Advanced Engineering and Computation 6, no. 3 (September 30, 2022): 167. http://dx.doi.org/10.55579/jaec.202263.381.

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Isogeometric analysis (IGA) brought superior accuracy to computations in both fluid and solid mechanics. The increased accuracy has been in representing both the problem geometry and the variables computed. Beyond using IGA basis functions in space, with IGA basis functions in time in a space–time (ST) context, we can have increased accuracy also in representing the motion of solid surfaces. Around the core methods such as the residual-based variational multiscale (VMS), ST-VMS and arbitrary Lagrangian–Eulerian VMS methods, with complex-geometry IGA mesh generation methods and immersogeometric analysis, and with special methods targeting specific classes of computations, the IGA has been very effective in computational cardiovascular medicine. We provide an overview of these IGA-based computational cardiovascular-medicine methods and present examples of the computations performed.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.
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Planitz, Max, and R. E. Moore. "Computational Functional Analysis." Mathematical Gazette 70, no. 451 (March 1986): 69. http://dx.doi.org/10.2307/3615858.

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Fink, James P., and R. E. Moore. "Computational Functional Analysis." Mathematics of Computation 47, no. 175 (July 1986): 372. http://dx.doi.org/10.2307/2008105.

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Dissertations / Theses on the topic "Computational analysis"

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Pocock, Matthew Richard. "Computational analysis of genomes." Thesis, University of Cambridge, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.615724.

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Cattinelli, I. "INVESTIGATIONS ON COGNITIVE COMPUTATION AND COMPUTATIONAL COGNITION." Doctoral thesis, Università degli Studi di Milano, 2011. http://hdl.handle.net/2434/155482.

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This Thesis describes our work at the boundary between Computer Science and Cognitive (Neuro)Science. In particular, (1) we have worked on methodological improvements to clustering-based meta-analysis of neuroimaging data, which is a technique that allows to collectively assess, in a quantitative way, activation peaks from several functional imaging studies, in order to extract the most robust results in the cognitive domain of interest. Hierarchical clustering is often used in this context, yet it is prone to the problem of non-uniqueness of the solution: a different permutation of the same input data might result in a different clustering result. In this Thesis, we propose a new version of hierarchical clustering that solves this problem. We also show the results of a meta-analysis, carried out using this algorithm, aimed at identifying specific cerebral circuits involved in single word reading. Moreover, (2) we describe preliminary work on a new connectionist model of single word reading, named the two-component model because it postulates a cascaded information flow from a more cognitive component that computes a distributed internal representation for the input word, to an articulatory component that translates this code into the corresponding sequence of phonemes. Output production is started when the internal code, which evolves in time, reaches a sufficient degree of clarity; this mechanism has been advanced as a possible explanation for behavioral effects consistently reported in the literature on reading, with a specific focus on the so called serial effects. This model is here discussed in its strength and weaknesses. Finally, (3) we have turned to consider how features that are typical of human cognition can inform the design of improved artificial agents; here, we have focused on modelling concepts inspired by emotion theory. A model of emotional interaction between artificial agents, based on probabilistic finite state automata, is presented: in this model, agents have personalities and attitudes that can change through the course of interaction (e.g. by reinforcement learning) to achieve autonomous adaptation to the interaction partner. Markov chain properties are then applied to derive reliable predictions of the outcome of an interaction. Taken together, these works show how the interplay between Cognitive Science and Computer Science can be fruitful, both for advancing our knowledge of the human brain and for designing more and more intelligent artificial systems.
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Shenoy, A. "Computational analysis of facial expressions." Thesis, University of Hertfordshire, 2010. http://hdl.handle.net/2299/4359.

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This PhD work constitutes a series of inter-disciplinary studies that use biologically plausible computational techniques and experiments with human subjects in analyzing facial expressions. The performance of the computational models and human subjects in terms of accuracy and response time are analyzed. The computational models process images in three stages. This includes: Preprocessing, dimensionality reduction and Classification. The pre-processing of face expression images includes feature extraction and dimensionality reduction. Gabor filters are used for feature extraction as they are closest biologically plausible computational method. Various dimensionality reduction methods: Principal Component Analysis (PCA), Curvilinear Component Analysis (CCA) and Fisher Linear Discriminant (FLD) are used followed by the classification by Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA). Six basic prototypical facial expressions that are universally accepted are used for the analysis. They are: angry, happy, fear, sad, surprise and disgust. The performance of the computational models in classifying each expression category is compared with that of the human subjects. The Effect size and Encoding face enable the discrimination of the areas of the face specific for a particular expression. The Effect size in particular emphasizes the areas of the face that are involved during the production of an expression. This concept of using Effect size on faces has not been reported previously in the literature and has shown very interesting results. The detailed PCA analysis showed the significant PCA components specific for each of the six basic prototypical expressions. An important observation from this analysis was that with Gabor filtering followed by non linear CCA for dimensionality reduction, the dataset vector size may be reduced to a very small number, in most cases it was just 5 components. The hypothesis that the average response time (RT) for the human subjects in classifying the different expressions is analogous to the distance measure of the data points from the classification hyper-plane was verified. This means the harder a facial expression is to classify by human subjects, the closer to the classifying hyper-plane of the classifier it is. A bi-variate correlation analysis of the distance measure and the average RT suggested a significant anti-correlation. The signal detection theory (SDT) or the d-prime determined how well the model or the human subjects were in making the classification of an expressive face from a neutral one. On comparison, human subjects are better in classifying surprise, disgust, fear, and sad expressions. The RAW computational model is better able to distinguish angry and happy expressions. To summarize, there seems to some similarities between the computational models and human subjects in the classification process.
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Etherington, Graham John. "Computational analysis of foodborne viruses." Thesis, University of East Anglia, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.423473.

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Wen, Wen. "Computational texture analysis and segmentation." Thesis, University of Strathclyde, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358812.

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Buchala, Samarasena. "Computational analysis of face images." Thesis, University of Hertfordshire, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.431938.

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Hussain, R. "Computational geometry using fourier analysis." Thesis, De Montfort University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391483.

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Ikoma, Hayato. "Computational microscopy for sample analysis." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/91427.

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Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2014.
46
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 41-44).
Computational microscopy is an emerging technology which extends the capabilities of optical microscopy with the help of computation. One of the notable example is super resolution fluorescence microscopy which achieves sub-wavelength resolution. This thesis explores the novel application of computational imaging methods to fluorescence microscopy and oblique illumination microscopy. In fluorescence spectroscopy, we have developed a novel nonlinear matrix unmixing algorithm to separate fluorescence spectra distorted by absorption effect. By extending the method to tensor form, we have also demonstrated the performance of a nonlinear fluorescence tensor unmixing algorithm on spectral fluorescence imaging. In the future, this algorithm may be applied to fluorescence unmixing in deep tissue imaging. The performance of the two algorithms were examined on simulation and experiments. In another project, we applied switchable multiple oblique illuminations to reflected-light microscopy. While the proposed system is easily implemented compared to existing methods, we demonstrate that the microscope detects the direction of surface roughness whose height is as small as illumination wavelength.
by Hayato Ikoma.
S.M.
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Xu, Yangjian. "Computational analysis of fretting fatigue." Düsseldorf VDI-Verl, 2009. http://d-nb.info/996624554/04.

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Li, Xiang. "Computational analysis of ultraviolet reactors /." Online version of thesis, 2009. http://hdl.handle.net/1850/11175.

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Books on the topic "Computational analysis"

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Anastassiou, George A., and Oktay Duman, eds. Computational Analysis. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28443-9.

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Laub, Alan J. Computational matrix analysis. Philadelphia: Society for Industrial and Applied Mathematics, 2012.

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Neuman, Yair. Computational Personality Analysis. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42460-6.

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Im, Chang-Hwan, ed. Computational EEG Analysis. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0908-3.

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Meredith, David, ed. Computational Music Analysis. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25931-4.

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Laube, Patrick. Computational Movement Analysis. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10268-9.

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Computational functional analysis. Chichester [Eng.]: E. Horwood, 1985.

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Abraham, Ajith, Aboul-Ella Hassanien, and Vaclav Sná¿el, eds. Computational Social Network Analysis. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84882-229-0.

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Anastassiou, George A. Intelligent Mathematics: Computational Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17098-0.

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Zhang, David, Wangmeng Zuo, and Peng Wang. Computational Pulse Signal Analysis. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-4044-3.

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Book chapters on the topic "Computational analysis"

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Betounes, David. "Computational Analysis." In Partial Differential Equations for Computational Science, 87–108. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-2198-2_5.

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Weihrauch, Klaus. "7. Computational Complexity." In Computable Analysis, 195–235. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-56999-9_7.

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Hart, George W. "Multidimensional Computational Methods." In Multidimensional Analysis, 171–208. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-4208-6_7.

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Dascalu, Mihai. "Computational Discourse Analysis." In Analyzing Discourse and Text Complexity for Learning and Collaborating, 53–77. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03419-5_4.

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Wang, DeLiang. "Computational Scene Analysis." In Challenges for Computational Intelligence, 163–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71984-7_8.

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Hausser, Roland. "Computational language analysis." In Foundations of Computational Linguistics, 13–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04337-0_2.

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Gudmundsson, Joachim, Patrick Laube, and Thomas Wolle. "Computational Movement Analysis." In Springer Handbook of Geographic Information, 423–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-540-72680-7_22.

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Hausser, Roland. "Computational language analysis." In Foundations of Computational Linguistics, 13–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03920-5_2.

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Elwert, Frederik. "Computational Text Analysis." In The Routledge Handbook of Research Methods in the Study of Religion, 164–79. 2nd ed. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003222491-12.

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Erciyes, K. "Genome Analysis." In Computational Biology, 183–210. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24966-7_9.

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Conference papers on the topic "Computational analysis"

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

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Majak, Jüri, and M. Di Sciuva. "Preface: Computational Mechanics." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2019. AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0026521.

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Bechhoefer, Eric. "Low Computational, Nonlinear Component Trend Analysis." In Vertical Flight Society 75th Annual Forum & Technology Display. The Vertical Flight Society, 2019. http://dx.doi.org/10.4050/f-0075-2019-14606.

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This paper is concerned with a nonlinear, computationally efficient method for trending component health. While conceptually simple, the goal of component trending is to reduce spurious noise in the measured component health and to estimate the remaining useful life (RUL). The need for lower computational effort allows this to be done on an embedded system. This would be important for display on a cockpit multi-function display. Additionally, we describe a new method for state smoothing. This is a forward-backward technique with no computational overhead associated with updating the plant noise (associated with a Kalman Smoother), significantly reducing the number of operations needed. Finally, a comparison is made between the precision of the nonlinear state estimation vs. a linear state estimation in the computation of the RUL. The precision was quantified using mean relative of the prognostic.
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Chamis, C. C., and R. H. Johns. "Computational Engine Structural Analysis." In ASME 1986 International Gas Turbine Conference and Exhibit. American Society of Mechanical Engineers, 1986. http://dx.doi.org/10.1115/86-gt-70.

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A significant research activity at the NASA Lewis Research Center is the computational simulation of complex multidisciplinary engine structural problems. This simulation is performed using computational engine structural analysis (CESA) which consists of integrated multidisciplinary computer codes in conjunction with computer post-processing for “problem-specific” application. A variety of the computational simulations of specific cases are described in some detail in this paper. These case studies include (1) aeroelastic behavior of bladed rotors, (2) high velocity impact of fan blades, (3) blade-loss transient response, (4) rotor/stator/squeeze-film/bearing interaction, (5) blade-fragment/rotor-burst containment, and (6) structural behavior of advanced swept turboprops. These representative case studies were selected to demonstrate the breadth of the problems analyzed and the role of the computer including post-processing and graphical display of voluminous output data.
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Gupta, M. Satyanarayana, Nirmith Kumar Mishra, Mosin, Aishwarya Jaiswal, and Ankadala Jyoshnavi. "Computational analysis over wings." In PROCEEDINGS OF THE 1ST INTERNATIONAL CONFERENCE ON FRONTIER OF DIGITAL TECHNOLOGY TOWARDS A SUSTAINABLE SOCIETY. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0113273.

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Degtyarev, Alexander, Vasily Khramushin, and Julia Shichkina. "Tensor methodology and computational geometry in direct computational experiments in fluid mechanics." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2016). Author(s), 2017. http://dx.doi.org/10.1063/1.4992291.

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

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Hundur, Yakup. "Preface of the "Symposium on computational nanomaterials"." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756530.

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Anandan, Princia, Florinda Schembri, and Maide Bucolo. "Computational modeling of droplet based logic circuits." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756102.

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Eoyang, Glenda H. "Human systems dynamics: Toward a computational model." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756214.

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Reports on the topic "Computational analysis"

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George, D. L. Computational techniques in gamma-ray skyshine analysis. Office of Scientific and Technical Information (OSTI), December 1988. http://dx.doi.org/10.2172/6077591.

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Gentile, Ann C., Youssef M. Marzouk, James M. Brandt, and Philippe Pierre Pebay. Meaningful statistical analysis of large computational clusters. Office of Scientific and Technical Information (OSTI), July 2005. http://dx.doi.org/10.2172/958384.

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Lax, P., and M. Berger. Applied analysis/computational mathematics. Final report 1993. Office of Scientific and Technical Information (OSTI), December 1993. http://dx.doi.org/10.2172/10113926.

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Babuska, Ivo, Y. Li, and K. L. Jerina. Reliability of Computational Analysis of Plasticity Problems. Fort Belvoir, VA: Defense Technical Information Center, March 1991. http://dx.doi.org/10.21236/ada239646.

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Davis, George B., and Kathleen M. Carley. Computational Analysis of Merchant Marine GPS Data. Fort Belvoir, VA: Defense Technical Information Center, November 2006. http://dx.doi.org/10.21236/ada471469.

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Barhen, J., C. W. Glover, and V. A. Protopopescu. Advanced computational tools for 3-D seismic analysis. Office of Scientific and Technical Information (OSTI), June 1996. http://dx.doi.org/10.2172/450786.

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Grandhi, Ramana V. Computational Mechanics Approach for Multidisciplinary Nonlinear Sensitivity Analysis. Fort Belvoir, VA: Defense Technical Information Center, June 2003. http://dx.doi.org/10.21236/ada416568.

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Rzhetsky, Andrey, and Dimitris Anastassiou. COMPUTATIONAL ANALYSIS AND SIMULATION OF BACTERIAL MOLECULAR NETWORKS. Office of Scientific and Technical Information (OSTI), December 2009. http://dx.doi.org/10.2172/968434.

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Konstantin Mischaikow, Michael Schatz, William Kalies, and Thomas Wanner. Multiscale analysis of nonlinear systems using computational homology. Office of Scientific and Technical Information (OSTI), May 2010. http://dx.doi.org/10.2172/979569.

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Grandhi, Ramana V. Computational Mechanics Approach for Multidisciplinary Nonlinear Sensitivity Analysis. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada388853.

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