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Relevant bibliographies by topics / Computational modeling and simulation

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Academic literature on the topic 'Computational modeling and simulation'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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Journal articles on the topic "Computational modeling and simulation"

1

Greif, Hajo. "Exploring Minds: Modes of Modeling and Simulation in Artificial Intelligence." Perspectives on Science 29, no. 4 (July 2021): 409–35. http://dx.doi.org/10.1162/posc_a_00377.

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Abstract The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. The proposed taxonomy cuts across the traditional dichotomies between symbolic and embodied AI, general intelligence and symbol and intelligence and cognitive simulation and human/non-human-like AI. According to the taxonomy proposed here, one can distinguish between four distinct general approaches that figured prominently in early and classical AI, and that have partly developed into distinct research programs: first, phenomenal simulations (e.g., Turing’s “imitation game”); second, simulations that explore general-level formal isomorphisms in pursuit of a general theory of intelligence (e.g., logic-based AI); third, simulations as exploratory material models that serve to develop theoretical accounts of cognitive processes (e.g., Marr’s stages of visual processing and classical connectionism); and fourth, simulations as strictly formal models of a theory of computation that postulates cognitive processes to be isomorphic with computational processes (strong symbolic AI). In continuation of pragmatic views of the modes of modeling and simulating world affairs, this taxonomy of approaches to modeling in AI helps to elucidate how available computational concepts and simulational resources contribute to the modes of representation and theory development in AI research—and what made that research program uniquely dependent on them.

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Raji, Kochandra, and Choondal B. Sobhan. "Simulation and modeling of carbon nanotube synthesis: current trends and investigations." Nanotechnology Reviews 2, no. 1 (February 1, 2013): 73–105. http://dx.doi.org/10.1515/ntrev-2012-0038.

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AbstractA review of significant investigations reported on simulating the nucleation and growth processes of carbon nanotubes (CNTs) using different modeling techniques is presented here. Special emphasis is given to the chemical vapor deposition method, being the cheapest and most versatile of the fabrication methods. The modeling methods involve the conventional computational fluid dynamics approaches as well as discrete computation techniques. Some in-house investigations utilizing chemical kinetic modeling and discrete computations to predict the growth of CNTs using the chemical vapor deposition method are also discussed. The modeling and simulation techniques reviewed here are expected to assist in the design of chirality-specific single-walled CNT synthesis systems.

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Kim, Yong Cheon, Dai Young Kwon, and Won Gyu Lee. "Computational Modeling and Simulation for Learning an Automation Concept in Programming Course." International Journal of Computer Theory and Engineering 6, no. 4 (2014): 341–45. http://dx.doi.org/10.7763/ijcte.2014.v6.886.

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Alam, Jahrul M. "Toward a Multiscale Approach for Computational Atmospheric Modeling." Monthly Weather Review 139, no. 12 (December 1, 2011): 3906–22. http://dx.doi.org/10.1175/2011mwr3533.1.

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Abstract Atmospheric motions are generally characterized by a wide range of multiple length and time scales, and a numerical method must use a fine grid to resolve such a wide range of scales. Furthermore, a very fine grid requires an extremely small time step in order to keep explicit time integration schemes stable. Therefore, high-resolution meteorological simulations are very expensive. A novel multiscale modeling approach is, therefore, presented for simulating atmospheric flows. In this approach, a prognostic variable representing a highly intermittent multiscale feature is decomposed into a significant and a nonsignificant part using wavelets, where the significant part is represented by a small fraction of the wavelet modes. The proposed multiscale methodology has been verified for simulating three cases: Smolarkiewicz’s deformational flow model, warm thermals in a dry atmosphere, and the dynamics of a vortex pair with ambient stable stratification. Comparisons with benchmark simulations and with a reference model are evidence for the convergence and stability of the proposed model. The comparison with the reference model has revealed that about 93% of the grid points are not necessary to resolve the significant motion in a warm thermal simulation, saving about 96% of the CPU time. Moreover, the CPU time varies linearly with the number of significant wavelet modes, showing that the present fully implicit adaptive model is asymptotically optimal for this simulation. These primary results point toward the benefit of constructing multiscale atmospheric models using the adaptive wavelet methodology.

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Ziegel, Eric R., C. Taber, and R. Timpone. "Computational Modeling." Technometrics 39, no. 2 (May 1997): 238. http://dx.doi.org/10.2307/1270931.

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Avelar, Nayara Vilela, Ana Augusta Passos Rezende, Antonio Marcos de Oliveira Siqueira, Cláudio Mudadu Silva, and Angélica de Cássia Oliveira Carneiro. "Computational modeling of biosludge drying." International Journal for Innovation Education and Research 9, no. 8 (August 1, 2021): 219–32. http://dx.doi.org/10.31686/ijier.vol9.iss8.3280.

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Considerable increases in industrial and urban wastewater sludge generation in recent years require proper treatment, such as thermal drying, and disposal. The sludge drying is a complex process involving simultaneous and coupled heat and mass transfer, which can be modeled by taking into account mass and heat balances, and assuming that water diffuses according to kinetic laws. This research implemented a simulation model for biosludge drying processes to predict the temperature and moisture distribution inside the biosludge, using the COMSOL Multiphysics® simulation program v5.2. A parametric analysis was carried out to determine the effect of initial moisture content on biosludge final temperature and moisture reduction. The simulated temperature and moisture content were experimentally validated and good agreement was observed between the simulation and experimental results. This model is a useful tool to optimize the drying process and develop better strategies for the control of the system.

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Ceder, Gerbrand, Marc Doyle, Pankaj Arora, and Yuris Fuentes. "Computational Modeling and Simulation for Rechargeable Batteries." MRS Bulletin 27, no. 8 (August 2002): 619–23. http://dx.doi.org/10.1557/mrs2002.198.

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AbstractComputational modeling is playing an increasingly important role in materials research and design. At the system level, the impact of cell design, electrode thickness, electrode morphology, new packaging techniques, and numerous other factors on battery performance can be predicted with battery simulators based on complex electrochemical transport equations. Such simulation tools have allowed the battery industry to optimize the power and energy density that can be achieved with a given set of electrode and electrolyte materials. At the materials level, first-principles calculations, which can be used to predict properties of previously unknown materials ab initio, have now made it possible to design materials for higher capacity and better stability. The state of the art in computational modeling of rechargeable batteries is reviewed.

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Knudsen, Thomas B. "Computational modeling and simulation of developmental toxicity." Toxicology Letters 258 (September 2016): S40. http://dx.doi.org/10.1016/j.toxlet.2016.06.1245.

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Yasar, Osman, and Jose Maliekal. "Computational Pedagogy: A Modeling and Simulation Approach." Computing in Science & Engineering 16, no. 3 (May 2014): 78–88. http://dx.doi.org/10.1109/mcse.2014.60.

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R., Blasco, Diaz G., and Reyes A. "PNEUMATIC SUSPENSION DRYING: MODELING AND COMPUTATIONAL SIMULATION." Drying Technology 16, no. 1-2 (January 1998): 199–215. http://dx.doi.org/10.1080/07373939808917399.

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Dissertations / Theses on the topic "Computational modeling and simulation"

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Emerson, Tonya Lynn. "Ductile fracture mechanics : modeling, experiments, and computational simulation /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2002. http://uclibs.org/PID/11984.

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Le, Xuan Tuan. "Understanding complex systems through computational modeling and simulation." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEP003.

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Les approches de simulation classiques ne sont en général pas adaptées pour traiter les aspects de complexité que présentent les systèmes complexes tels que l'émergence ou l'adaptation. Dans cette thèse, l'auteur s'appuie sur ses travaux menés dans le cadre d'un projet de simulation sur l’épidémie de grippe en France associée à des interventions sur une population en considérant le phénomène étudié comme un processus diffusif sur un réseau complexe d'individus, l'originalité réside dans le fait que la population y est considérée comme un système réactif. La modélisation de tels systèmes nécessite de spécifier explicitement le comportement des individus et les réactions de ceux-cis tout en produisant un modèle informatique qui doit être à la fois flexible et réutilisable. Les diagrammes d'états sont proposés comme une approche de programmation reposant sur une modélisation validée par l'expertise. Ils correspondent également à une spécification du code informatique désormais disponibles dans les outils logiciels de programmation agent. L'approche agent de type bottom-up permet d'obtenir des simulations de scénario "what-if" où le déroulement des actions peut nécessiter que les agents s'adaptent aux changements de contexte. Cette thèse propose également l'apprentissage pour un agent par l'emploi d'arbre de décision afin d'apporter flexibilité et lisibilité pour la définition du modèle de comportement des agents et une prise de décision adaptée au cours de la simulation. Notre approche de modélisation computationnelle est complémentaire aux approches traditionnelles et peut se révéler indispensable pour garantir une approche pluridisciplinaire validable par l'expertise
Traditional approaches are not sufficient, and sometimes impossible in dealing with complexity issues such as emergence, self-organization, evolution and adaptation of complex systems. As illustrated in this thesis by the practical work of the author in a real-life project, the spreading of infectious disease as well as interventions could be considered as difusion processes on complex networks of heterogeneous individuals in a society which is considered as a reactive system. Modeling of this system requires explicitly specifying of each individual’s behaviors and (re)actions, and transforming them into computational model which has to be flexible, reusable, and ease of coding. Statechart, typical for model-based programming, is a good solution that the thesis proposes. Bottom-up agent based simulation finds emergence episodes in what-if scenarios that change rules governing agent’s behaviors that requires agents to learn to adapt with these changes. Decision tree learning is proposed to bring more flexibility and legibility in modeling of agent’s autonomous decision making during simulation runtime. Our proposition for computational models such as agent based models are complementary to traditional ones, and in some case they are unique solutions due to legal, ethical issues

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Lambeth, Melissa Jo. "Computational modeling of skeletal muscle glycogenolysis dynamics /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/8095.

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Barua, Himel Barua. "COMPUTATIONAL MODELING OF CHEMICAL VAPOR DEPOSITION." University of Akron / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=akron1469721885.

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Simoni, Giulia. "Modeling Startegies for Computational Systems Biology." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/254361.

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Mathematical models and their associated computer simulations are nowadays widely used in several research fields, such as natural sciences, engineering, as well as social sciences. In the context of systems biology, they provide a rigorous way to investigate how complex regulatory pathways are connected and how the disruption of these processes may contribute to the develop- ment of a disease, ultimately investigating the suitability of specific molecules as novel therapeutic targets. In the last decade, the launching of the precision medicine initiative has motivated the necessity to define innovative computational techniques that could be used for customizing therapies. In this context, the combination of mathematical models and computer strategies is an essential tool for biologists, which can analyze complex system pathways, as well as for the pharmaceutical industry, which is involved in promoting programs for drug discovery. In this dissertation, we explore different modeling techniques that are used for the simulation and the analysis of complex biological systems. We analyze the state of the art for simulation algorithms both in the stochastic and in the deterministic frameworks. The same dichotomy has been studied in the context of sensitivity analysis, identifying the main pros and cons of the two approaches. Moreover, we studied the quantitative system pharmacology (QSP) modeling approach that elucidates the mechanism of action of a drug on the biological processes underlying a disease. Specifically, we present the definition, calibration and validation of a QSP model describing Gaucher disease type 1 (GD1), one of the most common lysosome storage rare disorders. All of these techniques are finally combined to define a novel computational pipeline for patient stratification. Our approach uses modeling techniques, such as model simulations, sensitivity analysis and QSP modeling, in combination with experimental data to identify the key mechanisms responsible for the stratification. The pipeline has been applied to three test cases in different biological contexts: a whole-body model of dyslipidemia, the QSP model of GD1 and a QSP model of cardiac electrophysiology. In these test cases, the pipeline proved to be accurate and robust, allowing the interpretation of the mechanistic differences underlying the phenotype classification.

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Withrow, Travis P. "Computational Modeling of Atom Probe Tomography." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1525763934302517.

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Yang, Le. "Computational Modeling and Simulation Study of Dermal Wound Healing Proliferative Phase." VCU Scholars Compass, 2011. http://scholarscompass.vcu.edu/etd/278.

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Dermal wound healing proliferative phase is a complicated physiological process in which many growth factors, cell types and matrix components participate. The process must be well coordinated to restore the structural and functional integrity of tissue injured. Many disorders interrupting healing process result in abnormal healing such as chronic wounds or excessive scarring. Mathematical modeling has been used to investigate many aspects of wound healing. Angiogenesis is pertinent for dermal wound healing since the cellular activities involved in tissue repair requires oxygen and nutrients to be delivered to the wound site. By using a hybrid agent-based model, we investigated the interactive dynamics of vasculature growth and collagen network growth. Our model further examine the effects of tissue oxygen tension (hypoxia, normoxia, hyperoxia) on healing process. Wound contraction is generally beneficial for the overall healing since it reduces the wound size thus reduces the chance to be infected. However, contraction going overboard may result in excessive scarring. Our model seeks to investigate the source of driving force during early and late stage of wound contraction. For the first time, skin is modeled as a fiber-reinforced anisotropic soft tissue. The effects of a dynamically orienting collagen matrix on the contraction process are thus shown. The simulation results of the model agree with the hypothesis that scar formation is the byproduct of collagen fiber synthesis and alignment in the presence of the tensile stress field generated by a wound contraction process. Multi-scale modeling is illuminating because it can help explain the phenomena at tissue level by the subcellular level events. We built a multi-scale model of general wound healing proliferative phase by embedding a TGFbeta pathway to each fibroblasts. The subcellular level model is an ODE system and the cellular level model is a hybrid agent-based model of fibroblast migration, proliferationg and collagen production. Our model clearly shows how varying mechanics of the subcellular level system results in varying tissue level pattern (collagen orientation and cell population distribution). The model can be further extended to incorporate subcellular events relating to angiogenesis and wound contraction.

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Venkatachalam, Sangeeta. "Modeling Infectious Disease Spread Using Global Stochastic Field Simulation." Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5335/.

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Susceptibles-infectives-removals (SIR) and its derivatives are the classic mathematical models for the study of infectious diseases in epidemiology. In order to model and simulate epidemics of an infectious disease, a global stochastic field simulation paradigm (GSFS) is proposed, which incorporates geographic and demographic based interactions. The interaction measure between regions is a function of population density and geographical distance, and has been extended to include demographic and migratory constraints. The progression of diseases using GSFS is analyzed, and similar behavior to the SIR model is exhibited by GSFS, using the geographic information systems (GIS) gravity model for interactions. The limitations of the SIR and similar models of homogeneous population with uniform mixing are addressed by the GSFS model. The GSFS model is oriented to heterogeneous population, and can incorporate interactions based on geography, demography, environment and migration patterns. The progression of diseases can be modeled at higher levels of fidelity using the GSFS model, and facilitates optimal deployment of public health resources for prevention, control and surveillance of infectious diseases.

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Wang, Kezhou Denney Thomas Stewart. "Numerical modeling of nasal cavities and air flow simulation." Auburn, Ala., 2006. http://repo.lib.auburn.edu/2006%20Spring/doctoral/WANG_KEZHOU_24.pdf.

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Indrakanti, Saratchandra. "A Global Stochastic Modeling Framework to Simulate and Visualize Epidemics." Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc115099/.

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Epidemics have caused major human and monetary losses through the course of human civilization. It is very important that epidemiologists and public health personnel are prepared to handle an impending infectious disease outbreak. the ever-changing demographics, evolving infrastructural resources of geographic regions, emerging and re-emerging diseases, compel the use of simulation to predict disease dynamics. By the means of simulation, public health personnel and epidemiologists can predict the disease dynamics, population groups at risk and their geographic locations beforehand, so that they are prepared to respond in case of an epidemic outbreak. As a consequence of the large numbers of individuals and inter-personal interactions involved in simulating infectious disease spread in a region such as a county, sizeable amounts of data may be produced that have to be analyzed. Methods to visualize this data would be effective in facilitating people from diverse disciplines understand and analyze the simulation. This thesis proposes a framework to simulate and visualize the spread of an infectious disease in a population of a region such as a county. As real-world populations have a non-homogeneous demographic and spatial distribution, this framework models the spread of an infectious disease based on population of and geographic distance between census blocks; social behavioral parameters for demographic groups. the population is stratified into demographic groups in individual census blocks using census data. Infection spread is modeled by means of local and global contacts generated between groups of population in census blocks. the strength and likelihood of the contacts are based on population, geographic distance and social behavioral parameters of the groups involved. the disease dynamics are represented on a geographic map of the region using a heat map representation, where the intensity of infection is mapped to a color scale. This framework provides a tool for public health personnel and epidemiologists to run what-if analyses on disease spread in specific populations and plan for epidemic response. By the means of demographic stratification of population and incorporation of geographic distance and social behavioral parameters into the modeling of the outbreak, this framework takes into account non-homogeneity in demographic mix and spatial distribution of the population. Generation of contacts per population group instead of individuals contributes to lowering computational overhead. Heat map representation of the intensity of infection provides an intuitive way to visualize the disease dynamics.

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Books on the topic "Computational modeling and simulation"

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Moeller, Dietmar P. F. Mathematical and Computational Modeling and Simulation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18709-4.

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Computational modeling in biomechanics. Dordrecht: Springer, 2010.

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J, Timpone Richard, ed. Computational modeling. Thousand Oaks, Calif: Sage Publications, 1996.

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Beňušková, L̕. Computational neurogenetic modeling. New York: Springer, 2007.

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Workshop on Large Scale Computational Device Modeling (1985 Naperville, Ill.). Large scale computational device modeling. Urbana, Ill: Coordinated Science Laboratory, University of Illinois, 1986.

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American, Mathematical Society Short Course Modeling and Simulation of Biology Networks. Modeling and simulation of biology networks. Providence, RI: American Mathematical Society, 2007.

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service), ScienceDirect (Online, ed. Computational modeling of membrane bilayers. London: Academic Press, 2008.

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Modeling marvels: Computational anticipation of novel molecules. [Dordrecht]: Springer, 2008.

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service), SpringerLink (Online, ed. Introduction to Computational Cardiology: Mathematical Modeling and Computer Simulation. Boston, MA: Springer Science+Business Media, LLC, 2010.

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Fei, Minrui, Shiwei Ma, Xin Li, Xin Sun, Li Jia, and Zhou Su, eds. Advanced Computational Methods in Life System Modeling and Simulation. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6370-1.

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Book chapters on the topic "Computational modeling and simulation"

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Mitsoulis, Evan. "Computational Polymer Processing." In Modeling and Simulation in Polymers, 127–95. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2010. http://dx.doi.org/10.1002/9783527630257.ch4.

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Bandini, Stefania, Sara Manzoni, and Giuseppe Vizzari. "Agent Based Modeling and Simulation." In Computational Complexity, 105–17. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1800-9_7.

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Han, Xu, and Jie Liu. "Computational Inverse for Modeling Parameters." In Numerical Simulation-based Design, 67–87. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-10-3090-1_4.

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Mielke, Roland R., James F. Leathrum, Andrew J. Collins, and Michel Albert Audette. "Overview of Computational Modeling and Simulation." In Healthcare Simulation Research, 39–47. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26837-4_6.

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Moeller, Dietmar P. F. "Simulation Sofware for Computational Modeling and Simulation." In Mathematical and Computational Modeling and Simulation, 161–256. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18709-4_4.

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Zeigler, Bernard, Alexandre Muzy, and Levent Yilmaz. "Artificial Intelligence in Modeling and Simulation." In Computational Complexity, 204–27. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1800-9_14.

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Litvinenko, Galina. "Computational Studies of Polymer Kinetics." In Modeling and Simulation in Polymers, 93–126. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2010. http://dx.doi.org/10.1002/9783527630257.ch3.

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Moeller, Dietmar P. F. "Distributed Simulation." In Mathematical and Computational Modeling and Simulation, 339–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18709-4_7.

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Kogan, Boris Ja. "Mathematical Modeling and Computer Simulation." In Introduction to Computational Cardiology, 11–23. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-76686-7_2.

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Poldneff, Michael J., and Martin W. Heinstein. "Computational Mechanics of Rubber and Tires." In Modeling and Simulation in Polymers, 385–403. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2010. http://dx.doi.org/10.1002/9783527630257.ch8.

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Conference papers on the topic "Computational modeling and simulation"

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Derbal, Youcef. "Computational Modeling of Estrogen Metabolism." In Modelling and Simulation. Calgary,AB,Canada: ACTAPRESS, 2013. http://dx.doi.org/10.2316/p.2013.802-046.

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Ishimaru, Yasuyuki, Keiji Mashimo, Kyota Susai, Yingchao Fu, and Anlin Wang. "Computational Modeling for Fretting Simulation." In 2009 Proceedings of the 55th IEEE Holm Conference on Electrical Contacts. IEEE, 2009. http://dx.doi.org/10.1109/holm.2009.5284411.

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Gross, Matthew, Jonathan D. Rogers, and Mark Costello. "Computational Improvements to Multibody Projectile Dynamics Simulation." In AIAA Modeling and Simulation Technologies Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2014. http://dx.doi.org/10.2514/6.2014-2648.

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Cherednichenko, Alexander, Ernst Assmann, and Heiner Bubb. "Computational Approach for Entry Simulation." In 2006 Digital Human Modeling for Design and Engineering Conference. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2006. http://dx.doi.org/10.4271/2006-01-2358.

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Mancuso, Giulio Mose', Fabio Cremona, Leonardo Mangeruca, Alessandro Ulisse, Alessandro Mignogna, Stefano Boccabella, Alberto Ferrari, and Luigi Di Guglielmo. "A computational interface for Steady-State Models: An FMI Extension." In 2018 Modeling and Simulation Technologies Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2018. http://dx.doi.org/10.2514/6.2018-3893.

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Diviš, Roman, and Antonín Kavička. "Computational optimizations of nested simulations utilized for decision-making support." In THE EUROPEAN MODELING AND SIMULATION SYMPOSIUM. CAL-TEK srl, 2019. http://dx.doi.org/10.46354/i3m.2019.emss.014.

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Durak, Umut, Shafagh Jafer, Rob Wittman, Saurabh Mittal, Sven Hartmann, and Bernard P. Zeigler. "Computational Representation for a Simulation Scenario Definition Language." In 2018 AIAA Modeling and Simulation Technologies Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2018. http://dx.doi.org/10.2514/6.2018-1398.

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Perhinschi, Mario, Steven Mullins, Phil Evans, and Marcello Napolitano. "Computational Environment for the Development of an FAA Compliant Flight Simulator." In AIAA Modeling and Simulation Technologies Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-7779.

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"Computational Biology Modeling across Different Scales." In Special Session on Modelling and Simulation in Biology and Medicine. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004405706170625.

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M., Shwetha, Madathil Suchitra, Vasavi C.S., Radhagayathri K.U., Krishnan Namboori P.K., and Deepa Gopakumar. "Computational Modeling and Simulation of Biomolecular Motors." In 2009 International Conference on Advances in Computing, Control, & Telecommunication Technologies (ACT 2009). IEEE, 2009. http://dx.doi.org/10.1109/act.2009.41.

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Reports on the topic "Computational modeling and simulation"

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Sharma, Atul, and Amit Agrawal. Computational Modeling and Simulation of Film-Condensation. Fort Belvoir, VA: Defense Technical Information Center, January 2013. http://dx.doi.org/10.21236/ada578521.

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Oberkampf, William Louis, Timothy Guy Trucano, and Martin M. Pilch. Predictive Capability Maturity Model for computational modeling and simulation. Office of Scientific and Technical Information (OSTI), October 2007. http://dx.doi.org/10.2172/976951.

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ALVIN, KENNETH F., WILLIAM L. OBERKAMPF, BRIAN M. RUTHERFORD, and KATHLEEN V. DIEGERT. Methodology for characterizing modeling and discretization uncertainties in computational simulation. Office of Scientific and Technical Information (OSTI), March 2000. http://dx.doi.org/10.2172/752055.

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Diachin, L., F. Garaizar, V. Henson, and G. Pope. The Nuclear Energy Advanced Modeling and Simulation Enabling Computational Technologies FY09 Report. Office of Scientific and Technical Information (OSTI), October 2009. http://dx.doi.org/10.2172/971412.

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G. R. Odette and G. E. Lucas. In-Service Design & Performance Prediction of Advanced Fusion Material Systems by Computational Modeling and Simulation. Office of Scientific and Technical Information (OSTI), November 2005. http://dx.doi.org/10.2172/860872.

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Shabana, Ahmed A. Integration of Computational Geometry, Finite Element, and Multibody System Algorithms for the Development of New Computational Methodology for High-Fidelity Vehicle Systems Modeling and Simulation. ADDENDUM. Fort Belvoir, VA: Defense Technical Information Center, November 2013. http://dx.doi.org/10.21236/ada593312.

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Haehnel, Robert, Scott Christensen, J. Whitlow, Andrew Bauer, Ari Meyer, Gautham Rangarajan, Yonghu Wenren, et al. A computational prototyping environment interface for DoD CREATE™-AV Helios simulations. Engineer Research and Development Center (U.S.), May 2021. http://dx.doi.org/10.21079/11681/40582.

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Abstract:

Computational Prototyping Environment (CPE) is a web-based portal designed to simplify running Department of Defense (DoD) modeling and simulation tools on the DoD Supercomputing Resource Center’s (DSRC) High Performance Computing (HPC) systems. The first of these tools to be deployed in the CPE is an application (app) to conduct parametric studies and view results using the CREATE-AV Helios CFD software. Initial capability includes hover (collective sweep) and forward flight (speed sweep) performance calculations. The CPE Helios app allows for job submission to a DSRC’s HPC system and for the viewing of results created by Helios, i.e., time series and volumetric data. Example data input and results viewing are presented. Planned future functionality is also outlined.

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Farkas, Diana. Acquisition of a Computation/Visualization Server for the Center for Modeling and Simulation in Materials Science. Fort Belvoir, VA: Defense Technical Information Center, April 1998. http://dx.doi.org/10.21236/ada382525.

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Naylor, Bruce, David Ress, and Risto Miikkulainen. Neurometric Modeling: Computational Modeling of Individual Brains. Fort Belvoir, VA: Defense Technical Information Center, May 2011. http://dx.doi.org/10.21236/ada547370.

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Schutt, Timothy, and Manoj Shukla. Predicting the impact of aqueous ions on fate and transport of munition compounds. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41481.

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A model framework for natural water has been developed using computational chemistry techniques to elucidate the interactions between solvated munition compounds and eight common ions in naturally occurring water sources. The interaction energies, residence times, coordination statistics, and surface preferences of nine munition related compounds with each ion were evaluated. The propensity of these interactions to increase degradation of the munition compound was predicted using accelerated replica QM/MM simulations. The degradation prediction data qualitatively align with previous quantum mechanical studies. The results suggest that primary ions of interest for fate and transport modeling of munition compounds in natural waters may follow the relative importance of SO₄²⁻, Cl⁻ ≫ HCO₃⁻, Na⁺, Mg²⁺ > Ca²⁺, K⁺, and NH₄⁺.

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