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Статті в журналах з теми "Computational Science, Engineering":
Cunningham, Steve, Sylvia Clark Pulliam, Charles D. Swanson, and Peter R. Turner. "Computational science and engineering." ACM SIGCSE Bulletin 34, no. 1 (March 2002): 135–36. http://dx.doi.org/10.1145/563517.563393.
Sameh, A., G. Cybenko, M. Kalos, K. Neves, J. Rice, D. Sorensen, and F. Sullivan. "Computational science and engineering." ACM Computing Surveys 28, no. 4 (December 1996): 810–17. http://dx.doi.org/10.1145/242223.246865.
Wilson, Greg, and Andrew Lumsdaine. "Software Engineering and Computational Science." Computing in Science & Engineering 11, no. 6 (November 2009): 12–13. http://dx.doi.org/10.1109/mcse.2009.206.
Adler, Joan. "Computational Science and Engineering Education." Computing in Science & Engineering 22, no. 4 (July 1, 2020): 4–6. http://dx.doi.org/10.1109/mcse.2020.2998255.
Liew, K. M., L. W. Zhang, J. N. Reddy, and Shaofan Li. "Computational Methods for Engineering Science." Mathematical Problems in Engineering 2015 (2015): 1. http://dx.doi.org/10.1155/2015/842103.
Carver, Jeffrey C. "Software Engineering for Computational Science and Engineering." Computing in Science & Engineering 14, no. 2 (March 2012): 8–11. http://dx.doi.org/10.1109/mcse.2012.31.
Frank, Michael, Dimitris Drikakis, and Vassilis Charissis. "Machine-Learning Methods for Computational Science and Engineering." Computation 8, no. 1 (March 3, 2020): 15. http://dx.doi.org/10.3390/computation8010015.
Carley, Kathleen M. "Computational organizational science and organizational engineering." Simulation Modelling Practice and Theory 10, no. 5-7 (December 2002): 253–69. http://dx.doi.org/10.1016/s1569-190x(02)00119-3.
Barba, Lorena A. "Computational Science and Engineering in 2020." Computing in Science & Engineering 22, no. 6 (November 1, 2020): 5–7. http://dx.doi.org/10.1109/mcse.2020.3027933.
EDUCATION, SIAM WORKING GROUP ON CSE UNDERGRAD, Peter Turner and Linda Petzold, Co-Chairs, Angela Shiflet, Ignatios Vakalis, Kirk Jordan, and Samuel St John. "Undergraduate Computational Science and Engineering Education." SIAM Review 53, no. 3 (January 2011): 561–74. http://dx.doi.org/10.1137/07070406x.
Дисертації з теми "Computational Science, Engineering":
Sidiropoulos, Anastasios. "Computational metric embeddings." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/44712.
Includes bibliographical references (p. 141-145).
We study the problem of computing a low-distortion embedding between two metric spaces. More precisely given an input metric space M we are interested in computing in polynomial time an embedding into a host space M' with minimum multiplicative distortion. This problem arises naturally in many applications, including geometric optimization, visualization, multi-dimensional scaling, network spanners, and the computation of phylogenetic trees. We focus on the case where the host space is either a euclidean space of constant dimension such as the line and the plane, or a graph metric of simple topological structure such as a tree. For Euclidean spaces, we present the following upper bounds. We give an approximation algorithm that, given a metric space that embeds into R1 with distortion c, computes an embedding with distortion c(1) [delta]3/4 (A denotes the ratio of the maximum over the minimum distance). For higher-dimensional spaces, we obtain an algorithm which, for any fixed d > 2, given an ultrametric that embeds into Rd with distortion c, computes an embedding with distortion co(1). We also present an algorithm achieving distortion c logo(1) [delta] for the same problem. We complement the above upper bounds by proving hardness of computing optimal, or near-optimal embeddings. When the input space is an ultrametric, we show that it is NP-hard to compute an optimal embedding into R2 under the ... norm. Moreover, we prove that for any fixed d > 2, it is NP-hard to approximate the minimum distortion embedding of an n-point metric space into Rd within a factor of Q(n1/(17d)). Finally, we consider the problem of embedding into tree metrics. We give a 0(1)approximation algorithm for the case where the input is the shortest-path metric of an unweighted graph.
(cont.) For general metric spaces, we present an algorithm which, given an n-point metric that embeds into a tree with distortion c, computes an embedding with distortion (clog n)o ... . By composing this algorithm with an algorithm for embedding trees into R1, we obtain an improved algorithm for embedding general metric spaces into R1.
by Anastasios Sidiropoulos.
Ph.D.
Castillo, Andrea R. (Andrea Redwing). "Assessing computational methods and science policy in systems biology." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/51655.
Includes bibliographical references (p. 109-112).
In this thesis, I discuss the development of systems biology and issues in the progression of this science discipline. Traditional molecular biology has been driven by reductionism with the belief that breaking down a biological system into the fundamental biomolecular components will elucidate such phenomena. We have reached limitations with this approach due to the complex and dynamical nature of life and our inability to intuit biological behavior from a modular perspective [37]. Mathematical modeling has been integral to current system biology endeavors since detailed analysis would be invasive if performed on humans experimentally or in clinical trials [17]. The interspecies commonalities in systemic properties and molecular mechanisms suggests that certain behaviors transcend specie differentiation and therefore easily lend to generalizing from simpler organisms to more complex organisms such as humans [7, 17]. Current methodologies in mathematical modeling and analysis have been diverse and numerous, with no standardization to progress the discipline in a collaborative manner. Without collaboration during this formative period, successful development and application of systems biology for societal welfare may be at risk. Furthermore, such collaboration has to be standardized in a fundamental approach to discover generic principles, in the manner of preceding long-standing science disciplines. This study effectively implements and analyzes a mathematical model of a three-protein biochemical network, the Synechococcus elongatus circadian clock.
(cont.) I use mass action theory expressed in kronecker products to exploit the ability to apply numerical methods-including sensitivity analysis via boundary value formulation (BVP) and trapiezoidal integration rule-and experimental techniques-including partial reaction fitting and enzyme-driven activations-when mathematically modeling large-scale biochemical networks. Amidst other applicable methodologies, my approach is grounded in the law of mass action because it is based in experimental data and biomolecular mechanistic properties, yet provides predictive power in the complete delineation of the biological system dynamics for all future time points. The results of my research demonstrate the holistic approach that mass action method-ologies have in determining emergent properties of biological systems. I further stress the necessity to enforce collaboration and standardization in future policymaking, with reconsiderations on current stakeholder incentive to redirect academia and industry focus from new molecular entities to interests in holistic understanding of the complexities and dynamics of life entities. Such redirection away from reductionism could further progress basic and applied scientific research to embetter our circumstances through new treatments and preventive measures for health, and development of new strains and disease control in agriculture and ecology [13].
by Andrea R. Castillo.
S.M.in Technology and Policy
Vanek, Christopher Michael. "Computational hurricane hazard analysis a performance based engineering view." Master's thesis, University of Central Florida, 2010. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4543.
ID: 029050946; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Thesis (M.S.C.E.)--University of Central Florida, 2010.; Includes bibliographical references (p. 183]-186).
M.S.C.E.
Masters
Department of Civil, Environmental, and Construction Engineering
Engineering and Computer Science
Gupta, Gaurav. "Computational material science of carboncarbon : composites based on carbonaceous mesophase matrices." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=83865.
Yadala, Bhavya Sree. "Understanding Participants’ Feedback from Workshop Promoting Diversity and Inclusion in Computational Science and Engineering." Youngstown State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1620987134533065.
Doshi, Manan(Manan Mukesh). "Energy-time optimal path planning in strong dynamic flows." Thesis, Massachusetts Institute of Technology, 2021. https://hdl.handle.net/1721.1/130905.
Cataloged from the official PDF version of thesis.
Includes bibliographical references (pages 55-61).
We develop an exact partial differential equation-based methodology that predicts time-energy optimal paths for autonomous vehicles navigating in dynamic environments. The differential equations solve the multi-objective optimization problem of navigating a vehicle autonomously in a dynamic flow field to any destination with the goal of minimizing travel time and energy use. Based on Hamilton-Jacobi theory for reachability and the level set method, the methodology computes the exact Pareto optimal solutions to the multi-objective path planning problem, numerically solving the equations governing time-energy reachability fronts and optimal paths. Our approach is applicable to path planning in various scenarios, however we primarily present examples of navigating in dynamic marine environments. First, we validate the methodology through a benchmark case of crossing a steady front (a highway flow) for which we compare our results to semi-analytical optimal path solutions. We then consider more complex unsteady environments and solve for time-energy optimal missions in a quasi-geostrophic double-gyre ocean flow field.
by Manan Doshi.
S.M.
S.M. Massachusetts Institute of Technology, Center for Computational Science & Engineering
Wang, Hong Feng. "IGP traffic engineering : a comparison of computational optimization algorithms." Thesis, Stellenbosch : Stellenbosch University, 2008. http://hdl.handle.net/10019.1/20877.
ENGLISH ABSTRACT: Traffic Engineering (TE) is intended to be used in next generation IP networks to optimize the usage of network resources by effecting QoS agreements between the traffic offered to the network and the available network resources. TE is currently performed by the IP community using three methods including (1) IGP TE using connectionless routing optimization (2) MPLS TE using connection-oriented routing optimization and (3) Hybrid TE combining IGP TE with MPLS TE. MPLS has won the battle of the core of the Internet and is making its way into metro, access and even some private networks. However, emerging provider practices are revealing the relevance of using IGP TE in hybrid TE models where IGP TE is combined with MPLS TE to optimize IP routing. This is done by either optimizing IGP routing while setting a few number of MPLS tunnels in the network or optimizing the management of MPLS tunnels to allow growth for the IGP traffic or optimizing both IGP and MPLS routing in a hybrid IGP+MPLS setting. The focus of this thesis is on IGP TE using heuristic algorithms borrowed from the computational intelligence research field. We present four classes of algorithms for Maximum Link Utilization (MLU) minimization. These include Genetic Algorithm (GA), Gene Expression Programming (GEP), Ant Colony Optimization (ACO), and Simulated Annealing (SA). We use these algorithms to compute a set of optimal link weights to achieve IGP TE in different settings where a set of test networks representing Europe, USA, Africa and China are used. Using NS simulation, we compare the performance of these algorithms on the test networks with various traffic profiles.
AFRIKAANSE OPSOMMING: Verkeersingenieurswese (VI) is aangedui vir gebruik in volgende generasie IP netwerke vir die gebruiksoptimering van netwerkbronne deur die daarstelling van kwaliteit van diens ooreenkomste tussen die verkeersaanbod vir die netwerk en die beskikbare netwerkbronne. VI word huidiglik algemeen bewerkstellig deur drie metodes, insluitend (1) IGP VI gebruikmakend van verbindingslose roete-optimering, (2) MPLS VI gebruikmakend van verbindingsvaste roete-optimering en (3) hibriede VI wat IGP VI en MPLS VI kombineer. MPLS is die mees algemene, en word ook aangewend in metro, toegang en selfs sommige privaatnetwerke. Nuwe verskaffer-praktyke toon egter die relevansie van die gebruik van IGP VI in hibriede VI modelle, waar IGP VI gekombineer word met MPLS VI om IP roetering te optimeer. Dit word gedoen deur `of optimering van IGP roetering terwyl ’n paar MPLS tonnels in die netwerk gestel word, `of optimering van die bestuur van MPLS tonnels om toe te laat vir groei in die IGP verkeer `of die optimering van beide IGP en MPLS roetering in ’n hibriede IGP en MPLS situasie. Die fokus van hierdie tesis is op IGP VI gebruikmakend van heuristieke algoritmes wat ontleen word vanuit die berekeningsintelligensie navorsingsveld. Ons beskou vier klasse van algoritmes vir Maksimum Verbindingsgebruik (MVG) minimering. Dit sluit in genetiese algoritmes, geen-uitdrukkingsprogrammering, mierkoloniemaksimering and gesimuleerde temperoptimering. Ons gebruik hierdie algoritmes om ’n versameling optimale verbindingsgewigte te bereken om IGP VI te bereik in verskillende situasies, waar ’n versameling toetsnetwerke gebruik is wat Europa, VSA, Afrika en China verteenwoordig. Gebruikmakende van NS simulasie, vergelyk ons die werkverrigting van hierdie algoritmes op die toetsnetwerke, met verskillende verkeersprofiele.
Dreany, Harry Hayes. "Safety Engineering of Computational Cognitive Architectures within Safety-Critical Systems." Thesis, The George Washington University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10688677.
This paper presents the integration of an intelligent decision support model (IDSM) with a cognitive architecture that controls an autonomous non-deterministic safety-critical system. The IDSM will integrate multi-criteria, decision-making tools via intelligent technologies such as expert systems, fuzzy logic, machine learning, and genetic algorithms.
Cognitive technology is currently simulated within safety-critical systems to highlight variables of interest, interface with intelligent technologies, and provide an environment that improves the system’s cognitive performance. In this study, the IDSM is being applied to an actual safety-critical system, an unmanned surface vehicle (USV) with embedded artificial intelligence (AI) software. The USV’s safety performance is being researched in a simulated and a real-world, maritime based environment. The objective is to build a dynamically changing model to evaluate a cognitive architecture’s ability to ensure safe performance of an intelligent safety-critical system. The IDSM does this by finding a set of key safety performance parameters that can be critiqued via safety measurements, mechanisms, and methodologies. The uniqueness of this research lies in bounding the decision-making associated with the cognitive architecture’s key safety parameters (KSPs). Other real-time applications (RTAs) that would benefit from advancing cognitive science associated with safety are unmanned platforms, transportation technologies, and service robotics. Results will provide cognitive science researchers with a reference for the safety engineering of artificially intelligent safety-critical systems.
Blount, Steven Michael 1958. "Computational methods for stochastic epidemics." Diss., The University of Arizona, 1997. http://hdl.handle.net/10150/288714.
Raina, Priyanka. "Architectures for computational photography." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/82393.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 93-94).
Computational photography refers to a wide range of image capture and processing techniques that extend the capabilities of digital photography and allow users to take photographs that could not have been taken by a traditional camera. Since its inception less than a decade ago, the field today encompasses a wide range of techniques including high dynamic range (HDR) imaging, low light enhancement, panorama stitching, image deblurring and light field photography. These techniques have so far been software based, which leads to high energy consumption and typically no support for real-time processing. This work focuses on hardware architectures for two algorithms - (a) bilateral filtering which is commonly used in computational photography applications such as HDR imaging, low light enhancement and glare reduction and (b) image deblurring. In the first part of this work, digital circuits for three components of a multi-application bilateral filtering processor are implemented - the grid interpolation block, the HDR image creation and contrast adjustment blocks, and the shadow correction block. An on-chip implementation of the complete processor, designed with other team members, performs HDR imaging, low light enhancement and glare reduction. The 40 nm CMOS test chip operates from 98 MHz at 0.9 V to 25 MHz at 0.9 V and processes 13 megapixels/s while consuming 17.8 mW at 98 MHz and 0.9 V, achieving significant energy reduction compared to previous CPU/GPU implementations. In the second part of this work, a complete system architecture for blind image deblurring is proposed. Digital circuits for the component modules are implemented using Bluespec SystemVerilog and verified to be bit accurate with a reference software implementation. Techniques to reduce power and area cost are investigated and synthesis results in 40nm CMOS technology are presented
by Priyanka Raina.
S.M.
Книги з теми "Computational Science, Engineering":
Topping, B. H. V., ed. Computational Methods for Engineering Science. Stirlingshire, UK: Saxe-Coburg Publications, 2012. http://dx.doi.org/10.4203/csets.30.
Mastorakis, Nikos, Aida Bulucea, and George Tsekouras, eds. Computational Problems in Science and Engineering. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15765-8.
Yao, Z. H., and M. W. Yuan. Computational Methods in Engineering & Science. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-48260-4.
Ng, Michael K., Andrei Doncescu, Laurence T. Yang, and Tau Leng, eds. High Performance Computational Science and Engineering. Boston, MA: Springer US, 2005. http://dx.doi.org/10.1007/b104300.
Kim, Tai-hoon, Laurence T. Yang, Jong Hyuk Park, Alan Chin-Chen Chang, Thanos Vasilakos, Yan Zhang, Damien Sauveron, Xingang Wang, and Young-Sik Jeong, eds. Advances in Computational Science and Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10238-7.
Rylander, Thomas. Computational Electromagnetics. 2nd ed. New York, NY: Springer New York, 2013.
Papadrakakis, Manolis. Computational Methods in Earthquake Engineering. Dordrecht: Springer Science+Business Media B.V., 2011.
Koren, Barry, and Kees Vuik. Advanced computational methods in science and engineering. Edited by Technische Universiteit Delft. Delft Centre for Computational Science and Engineering. Berlin: Springer, 2010.
D'Acunto, Berardino. Computational partial differential equations for engineering science. Hauppauge, N.Y: Nova Science Publishers, 2010.
World Congress on Engineering (2008 London, England). Advances in electrical engineering and computational science. Edited by Ao Sio-Iong, Gelman Len, and International Association of Engineers. [Dordrecht: Springer, 2009.
Частини книг з теми "Computational Science, Engineering":
Polizzi, Eric, and Yousef Saad. "Computational Materials Science and Engineering." In Parallel Algorithms in Computational Science and Engineering, 123–50. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43736-7_5.
Farhat, Charbel. "Game Changing Computational Engineering Technology." In Lecture Notes in Computer Science, 30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19328-6_4.
Harman, Mark. "Search Based Software Engineering." In Computational Science – ICCS 2006, 740–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11758549_100.
Siricharoen, Waralak V. "Ontologies and Software Engineering." In Computational Science – ICCS 2007, 1155–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72586-2_161.
Marques, Viriato M., Luís Roseiro, Cecília Reis, and J. A. Tenreiro Machado. "Application of Computational Intelligence to Engineering." In Nonlinear Science and Complexity, 337–45. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-90-481-9884-9_39.
Honma, T. "Boundary Element Methods in Plasma Science and Engineering." In Computational Mechanics ’88, 103–6. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-61381-4_24.
Pollinger, Theresa, Michael Kohlhase, and Harald Köstler. "Knowledge Amalgamation for Computational Science and Engineering." In Lecture Notes in Computer Science, 232–47. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96812-4_20.
Lang, Jens. "Applications from Computational Sciences." In Lecture Notes in Computational Science and Engineering, 79–117. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04484-1_7.
Neittaanmäki, Pekka, and Sergey Repin. "Artificial Intelligence and Computational Science." In Intelligent Systems, Control and Automation: Science and Engineering, 27–35. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70787-3_3.
Moret, Bernard M. E., David A. Bader, and Tandy Warnow. "High-Performance Algorithmic Engineering for Computationa Phylogenetics." In Computational Science - ICCS 2001, 1012–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45718-6_107.
Тези доповідей конференцій з теми "Computational Science, Engineering":
Cunningham, Steve, Sylvia Clark Pulliam, Charles D. Swanson, and Peter R. Turner. "Computational science and engineering." In the 33rd SIGCSE technical symposium. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/563340.563393.
"Computational Science and Engineering Workshop." In 2010 Sixth IEEE International Conference on E-Science Workshops. IEEE, 2010. http://dx.doi.org/10.1109/esciencew.2010.6.
Stevenson, D. E. "Software engineering frontiers in computational science and engineering." In the 33rd annual. New York, New York, USA: ACM Press, 1995. http://dx.doi.org/10.1145/1122018.1122039.
Krzhizhanovskaya, V. V., V. V. Korkhov, A. Tirado-Ramos, D. J. Groen, I. V. Shoshmina, I. A. Valuev, I. V. Morozov, N. V. Malyshkin, Y. E. Gorbachev, and P. M. A. Sloot. "Computational Engineering on the Grid: Crafting a Distributed Virtual Reactor." In 2006 Second IEEE International Conference on e-Science and Grid Computing (e-Science'06). IEEE, 2006. http://dx.doi.org/10.1109/e-science.2006.261185.
Heaton, D. "What software engineering can do for computational science and engineering." In 2012 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC 2012). IEEE, 2012. http://dx.doi.org/10.1109/vlhcc.2012.6344525.
Bartlett, Roscoe A. "Integration strategies for Computational Science & Engineering software." In 2009 ICSE Workshop on Software Engineering for Computational Science and Engineering (SECSE). IEEE, 2009. http://dx.doi.org/10.1109/secse.2009.5069160.
Post, D. E. "The challenge for computational science." In "Software Engineering for High Performance Computing System (HPCS) Applications" W3S Workshop - 26th International Conference on Software Engineering. IEE, 2004. http://dx.doi.org/10.1049/ic:20040410.
Turner, Peter R., Angela B. Shiflet, Steve Cunningham, Kris Stewart, Andrew T. Phillips, and Ignatios E. Vakalis. "Undergraduate computational science and engineering programs and courses." In the 33rd SIGCSE technical symposium. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/563340.563374.
Post, Douglass. "Computational science and engineering requirements for new languages." In the Third Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1809961.1809963.
Carver, Jeffrey C., Roscoe Bartlett, Ian Gorton, Lorin Hochstein, Diane Kelly, and Judith Segal. "Fourth international workshop on software engineering for computational science and engineering." In Proceeding of the 33rd international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1985793.1986058.
Звіти організацій з теми "Computational Science, Engineering":
Martinelli, Luigi. International Symposium on 21st Century Challenges in Computational Engineering and Science. Fort Belvoir, VA: Defense Technical Information Center, February 2010. http://dx.doi.org/10.21236/ada515612.
Babuska, Ivo, and Tinsley Oden. V&V in Computational Engineering and Science. Part 1: Basic Concepts. Fort Belvoir, VA: Defense Technical Information Center, December 2003. http://dx.doi.org/10.21236/ada438174.
Boman, Erik G., Umit V. Catalyurek, Cedric Chevalier, Karen D. Devine, Assefaw H. Gebremedhin, Paul D. Hovland, Alex Pothen, et al. Combinatorial Algorithms to Enable Computational Science and Engineering: Work from the CSCAPES Institute. Office of Scientific and Technical Information (OSTI), January 2015. http://dx.doi.org/10.2172/1167393.
Nail, Julian C., William J. Hurley, Malie E. Smith, and Norman R. Howes. Report of Symposium: Applications of Advanced and Innovative Computational Methods to Defense Science and Engineering. Fort Belvoir, VA: Defense Technical Information Center, November 1994. http://dx.doi.org/10.21236/ada294800.
Saffer, Shelley (Sam). Advanced Artificial Science. The development of an artificial science and engineering research infrastructure to facilitate innovative computational modeling, analysis, and application to interdisciplinary areas of scientific investigation. Office of Scientific and Technical Information (OSTI), December 2014. http://dx.doi.org/10.2172/1164708.
Ueckermann, Mattheus P., Pierre F. Lermusiaux, and Themis P. Sapsis. Numerical Schemes and Computational Studies for Dynamically Orthogonal Equations (Multidisciplinary Simulation, Estimation, and Assimilation Systems: Reports in Ocean Science and Engineering). Fort Belvoir, VA: Defense Technical Information Center, August 2011. http://dx.doi.org/10.21236/ada568415.
Willenbring, James M., Roscoe Ainsworth Bartlett, and Michael Allen Heroux. TriBITS lifecycle model. Version 1.0, a lean/agile software lifecycle model for research-based computational science and engineering and applied mathematical software. Office of Scientific and Technical Information (OSTI), January 2012. http://dx.doi.org/10.2172/1038225.
Jiang, Zhigang. 10th International Conference of Computational Methods in Sciences and Engineering. Fort Belvoir, VA: Defense Technical Information Center, December 2014. http://dx.doi.org/10.21236/ada620077.
Kevrekidis, Ioannis G. Equation-free and variable free modeling for complex/multiscale systems. Coarse-grained computation in science and engineering using fine-grained models. Office of Scientific and Technical Information (OSTI), February 2017. http://dx.doi.org/10.2172/1347549.