Готові списки джерел за темами / Dynamics of optimization

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Добірка наукової літератури з теми "Dynamics of optimization"

Автор: Grafiati

Опубліковано: 29 березня 2025

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Статті в журналах з теми "Dynamics of optimization"

Boettcher, Stefan, and Allon G. Percus. "Optimization with Extremal Dynamics." Physical Review Letters 86, no. 23 (June 4, 2001): 5211–14. http://dx.doi.org/10.1103/physrevlett.86.5211.

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Bennett, J. A., and G. J. Park. "Automotive Occupant Dynamics Optimization." Shock and Vibration 2, no. 6 (1995): 471–79. http://dx.doi.org/10.1155/1995/682694.

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One of the more difficult optimal design tasks occurs when the data describing the system to be optimized is either highly nonlinear or noisy or both. This situation arises when trying to design restraint systems for automotive crashworthiness using the traditional lumped parameter analysis methods. The nonlinearities in the response can come from either abrupt changes in the occupants interaction with the interior or from relatively minor fluctuation in the response due to the interactions of two restraint systems such as belts and airbags. In addition the calculated response measures are usually highly nonlinear functions of the accelerations. Two approaches using an approximate problem formulation strategy are proposed. One approach uses a first-order approximation based on finite difference derivatives with a nonlocal step size. The second and more effective approach uses a second-order curve fitting strategy. Successful example problems of up to 16 design variables are demonstrated. A conservative design strategy using a derivative-based constraint padding is also discussed. The approach proves effective because analytical expressions are available for the second-order terms.

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Tamura, Kenichi, and Keiichiro Yasuda. "Spiral Dynamics Inspired Optimization." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 8 (October 20, 2011): 1116–22. http://dx.doi.org/10.20965/jaciii.2011.p1116.

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We recently proposed a new multipoint search method for 2-dimensional continuous optimization problems based on an analogy of spiral phenomena called 2-dimensional spiral optimization. Focused spiral phenomena, which appear frequently in nature, are approximated to logarithmic spirals. Two-dimensional spiral optimization used a feature of logarithmic spirals. In this paper, we proposen-dimensional spiral optimization by extending the 2-dimensional one. The n-dimensional spiral model is constructed based on rotation matrices defined inn-dimensional space. Simulation results for different benchmark problems show the effectiveness of our proposal compared to other metaheuristics.

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Gay-Balmaz, François, Darryl D. Holm, and Tudor S. Ratiu. "Geometric dynamics of optimization." Communications in Mathematical Sciences 11, no. 1 (2013): 163–231. http://dx.doi.org/10.4310/cms.2013.v11.n1.a6.

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Barettin, Daniele, and Paolo Sibani. "Optimization by record dynamics." Computer Physics Communications 185, no. 3 (March 2014): 730–35. http://dx.doi.org/10.1016/j.cpc.2013.10.030.

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Boettcher, Stefan, and Allon G. Percus. "Optimization with extremal dynamics." Complexity 8, no. 2 (November 2002): 57–62. http://dx.doi.org/10.1002/cplx.10072.

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Schuster, Peter, and Karl Sigmund. "Dynamics of Evolutionary Optimization." Berichte der Bunsengesellschaft für physikalische Chemie 89, no. 6 (June 1985): 668–82. http://dx.doi.org/10.1002/bbpc.19850890620.

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Zhang, Jing, Xiaokai Zhu, Te Chen, and Guowei Dou. "Optimal Dynamics Control in Trajectory Tracking of Industrial Robots Based on Adaptive Gaussian Pseudo-Spectral Algorithm." Algorithms 18, no. 1 (January 3, 2025): 18. https://doi.org/10.3390/a18010018.

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A pseudo-spectral control algorithm based on adaptive Gauss collocation point reconstruction is proposed to efficiently solve the optimal dynamics control problem of industrial robots. A mathematical model for the kinematic relationship and dynamic optimization control of industrial robots has been established. On the basis of deriving the Legendre–Gauss collocation formula, a two-stage adaptive Gauss collocation strategy for industrial robot dynamics control variables was designed to improve the dynamics optimization control effect of industrial robot by improving the solution efficiency of constrained optimization problems. The results show that compared with the control variable parameterization method and the traditional Gaussian pseudo-spectral method, the proposed dynamic optimal control method based on an adaptive Gaussian point reconstruction algorithm can effectively improve the solving time and efficiency of constrained optimization problems, thereby further enhancing the dynamic optimization control and trajectory tracking effect of industrial robots.

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Lurie, K. A. "MATERIAL OPTIMIZATION AND DYNAMIC MATERIALS." Cybernetics and Physics, Volume 10, 2021, Number 2 (October 1, 2021): 84–87. http://dx.doi.org/10.35470/2226-4116-2021-10-2-84-87.

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The paper is about the connection between material optimization in dynamics and a novel concept of dynamic materials (DM) defined as inseparable union of a framework and the fluxes of mass, momentum, and energy existing in time dependent material formations. An example of a spatial-temporal material geometry is discussed as illustration of a DM capable of accumulating wave energy. Finding the optimal material layouts in dynamics demonstrates conceptual difference from a similar procedure in statics. In the first case, the original constituents are distributed in space-time, whereas in the second - in space alone. The habitual understanding of a material as an isolated framework has come from statics, but a transition to dynamics brings in a new component - the fluxes of mass, momentum, and energy. Based on Noether theorem, these fluxes connect the framework with the environment into inseparable entity termed dynamic material (DM). The key role of DM is that they support controls that may purposefully change the material properties in both space and time, which is the main goal of optimization.

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Jiang, Shuai, Yuanpeng Lin, Jianan Liu, Linjing Xiao, and Shuaishuai Zhang. "Dynamics Optimization Research and Dynamics Accuracy and Reliability Analysis of a Multi-Link Mechanism with Clearances." Machines 10, no. 8 (August 16, 2022): 698. http://dx.doi.org/10.3390/machines10080698.

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With the development of high-speed and lightweight mechanisms, and the continuous improvement of manufacturing accuracy requirements in industrial production, clearance joints have increasingly become one of the key factors affecting dynamics performance. Poor clearance will seriously compromise stability, accuracy, and dynamics performance. Based on a genetic algorithm, an efficient modeling methodology for the dynamics optimization of a planar complex multi-link mechanism containing multiple clearance joints is put forward. The model comprises a 2-degree of freedom (DOF) nine-bar mechanism that can be used as the main transmission mechanism of a hybrid drive multi-link press, which is taken as the research object. The optimization objective is to minimize the maximum acceleration of the slider and minimize the difference between the actual central trajectory and the ideal trajectory. By optimizing the quality parameters of key components, an optimal solution for the design parameters is obtained, and the effects of the different optimizations of the objective functions on dynamics response are compared and analyzed. At the same time, a new modeling and calculation methodology of the dynamics accuracy and reliability of a complex multi-link mechanism in terms of multiple clearances is proposed, and the effect of optimization on dynamics accuracy and the reliability of the mechanism is analyzed. Based on the optimization results obtained by taking the minimum difference between the actual center trajectory and the ideal trajectory as an optimization objective, the nonlinear characteristics before and after optimization are analyzed through a phase diagram and Poincaré map. A test platform was built to study the dynamics of the mechanism with clearances. Research not only provides a basis for the dynamics optimization of a multi-link mechanism containing clearances but also provides reference significance for the reliability analysis of a multi-link mechanism containing clearances.

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Дисертації з теми "Dynamics of optimization"

Marsden, Christopher J. "Nonlinear dynamics of pattern recognition and optimization." Thesis, Loughborough University, 2012. https://dspace.lboro.ac.uk/2134/10694.

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We associate learning in living systems with the shaping of the velocity vector field of a dynamical system in response to external, generally random, stimuli. We consider various approaches to implement a system that is able to adapt the whole vector field, rather than just parts of it - a drawback of the most common current learning systems: artificial neural networks. This leads us to propose the mathematical concept of self-shaping dynamical systems. To begin, there is an empty phase space with no attractors, and thus a zero velocity vector field. Upon receiving the random stimulus, the vector field deforms and eventually becomes smooth and deterministic, despite the random nature of the applied force, while the phase space develops various geometrical objects. We consider the simplest of these - gradient self-shaping systems, whose vector field is the gradient of some energy function, which under certain conditions develops into the multi-dimensional probability density distribution of the input. We explain how self-shaping systems are relevant to artificial neural networks. Firstly, we show that they can potentially perform pattern recognition tasks typically implemented by Hopfield neural networks, but without any supervision and on-line, and without developing spurious minima in the phase space. Secondly, they can reconstruct the probability density distribution of input signals, like probabilistic neural networks, but without the need for new training patterns to have to enter the network as new hardware units. We therefore regard self-shaping systems as a generalisation of the neural network concept, achieved by abandoning the "rigid units - flexible couplings'' paradigm and making the vector field fully flexible and amenable to external force. It is not clear how such systems could be implemented in hardware, and so this new concept presents an engineering challenge. It could also become an alternative paradigm for the modelling of both living and learning systems. Mathematically it is interesting to find how a self shaping system could develop non-trivial objects in the phase space such as periodic orbits or chaotic attractors. We investigate how a delayed vector field could form such objects. We show that this method produces chaos in a class systems which have very simple dynamics in the non-delayed case. We also demonstrate the coexistence of bounded and unbounded solutions dependent on the initial conditions and the value of the delay. Finally, we speculate about how such a method could be used in global optimization.

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Zhu, Yitao. "Sensitivity Analysis and Optimization of Multibody Systems." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/71649.

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Multibody dynamics simulations are currently widely accepted as valuable means for dynamic performance analysis of mechanical systems. The evolution of theoretical and computational aspects of the multibody dynamics discipline make it conducive these days for other types of applications, in addition to pure simulations. One very important such application is design optimization for multibody systems. Sensitivity analysis of multibody system dynamics, which is performed before optimization or in parallel, is essential for optimization. Current sensitivity approaches have limitations in terms of efficiently performing sensitivity analysis for complex systems with respect to multiple design parameters. Thus, we bring new contributions to the state-of-the-art in analytical sensitivity approaches in this study. A direct differentiation method is developed for multibody dynamic models that employ Maggi's formulation. An adjoint variable method is developed for explicit and implicit first order Maggi's formulations, second order Maggi's formulation, and first and second order penalty formulations. The resulting sensitivities are employed to perform optimization of different multibody systems case studies. The collection of benchmark problems includes a five-bar mechanism, a full vehicle model, and a passive dynamic robot. The five-bar mechanism is used to test and validate the sensitivity approaches derived in this paper by comparing them with other sensitivity approaches. The full vehicle system is used to demonstrate the capability of the adjoint variable method based on the penalty formulation to perform sensitivity analysis and optimization for large and complex multibody systems with respect to multiple design parameters with high efficiency. In addition, a new multibody dynamics software library MBSVT (Multibody Systems at Virginia Tech) is developed in Fortran 2003, with forward kinematics and dynamics, sensitivity analysis, and optimization capabilities. Several different contact and friction models, which can be used to model point contact and surface contact, are developed and included in MBSVT. Finally, this study employs reference point coordinates and the penalty formulation to perform dynamic analysis for the passive dynamic robot, simplifying the modeling stage and making the robotic system more stable. The passive dynamic robot is also used to test and validate all the point contact and surface contact models developed in MBSVT.
Ph. D.

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Lei, Zhen. "Isogeometric shell analysis and optimization for structural dynamics." Thesis, Ecully, Ecole centrale de Lyon, 2015. http://www.theses.fr/2015ECDL0028/document.

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Cette thèse présente des travaux effectués dans le cadre de l'optimisation de forme de pièces mécaniques, sous critère dynamique, par approche isogéométrique. Pour réaliser une telle optimisation nous mettons en place dans un premier temps les éléments coque au travers des formulations Kirchhoff-Love puis Reissner-Minlin. Nous présentons une méthode permettant d'atteindre les vecteurs normaux aux fibres dans ces formulations au travers de l'utilisation d'une grille mixte de fonctions de base interpolantes, traditionnellement utilisées en éléments finis, et de fonction non interpolantes issues de la description isogéométrique des coques. Par la suite, nous détaillons une méthode pour le couplage de patch puis nous mettons en place la méthode de synthèse modale classique dans le cadre de structures en dynamique décrites par des éléments isogéometriques. Ce travail établit une base pour l'optimisation de forme sous critères dynamique de telles structures. Enfin, nous développons une méthode d'optimisation de forme basée sur le calcul du gradient de la fonction objectif envisagée. La sensibilité de conception est extraite de l'analyse de sensibilité au niveau même du maillage du modèle, qui est obtenue par l'analyse discrète de sensibilité. Des exemples d'application permettent de montrer la pertinence et l'exactitude des approches proposées
Isogeometric method is a promising method in bridging the gap between the computer aided design and computer aided analysis. No information is lost when transferring the design model to the analysis model. It is a great advantage over the traditional finite element method, where the analysis model is only an approximation of the design model. It is advantageous for structural optimization, the optimal structure obtained will be a design model. In this thesis, the research is focused on the fast three dimensional free shape optimization with isogeometric shell elements. The related research, the development of isogeometric shell elements, the patch coupling in isogeometric analysis, the modal synthesis with isogeometric elements are also studied. We proposed a series of mixed grid Reissner-Minlin shell formulations. It adopts both the interpolatory basis functions, which are from the traditional FEM, and the non-interpolatory basis functions, which are from IGA, to approximate the unknown elds. It gives a natural way to define the fiber vectors in IGA Reissner-Mindlin shell formulations, where the non-interpolatory nature of IGA basis functions causes complexity. It is also advantageous for applying the rotational boundary conditions. A modified reduce quadrature scheme was also proposed to improve the quadrature eficiency, at the same time, relieve the locking in the shell formulations. We gave a method for patch coupling in isogeometric analysis. It is used to connect the adjacent patches. The classical modal synthesis method, the fixed interface Craig-Bampton method, is also used as well as the isogeometric Kirchhoff-Love shell elements. The key problem is also the connection between adjacent patches. The modal synthesis method can largely reduce the time costs in analysis concerning structural dynamics. This part of work lays a foundation for the fast shape optimization of built-up structures, where the design variables are only relevant to certain substructures. We developed a fast shape optimization framework for three dimensional thin wall structure design. The thin wall structure is modelled with isogeometric Kirchhoff-Love shell elements. The analytical sensitivity analysis is the key focus, since the gradient base optimization is normally more fast. There are two models in most optimization problem, the design model and the analysis model. The design variables are defined in the design model, however the analytical sensitivity is normally obtained from the analysis model. Although it is possible to use the same model in analysis and design under isogeomeric framework, it might give either a highly distorted optimum structure or a unreliable structural response. We developed a sensitivity mapping scheme to resolve this problem. The design sensitivity is extracted from the analysis model mesh level sensitivity, which is obtained by the discrete analytical sensitivity analysis. It provides exibility for the design variable definition. The correctness of structure response is also ensured. The modal synthesis method is also used to further improve the optimization eficiency for the built-up structure optimization concerning structural dynamics criteria

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Lundvall, Johan. "Data Assimilation in Fluid Dynamics using Adjoint Optimization." Doctoral thesis, Linköping : Matematiska institutionen, Linköpings universitet, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-9684.

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ROUSSEAU, Yannick, Igor MEN'SHOV, and Yoshiaki NAKAMURA. "Morphing-Based Shape Optimization in Computational Fluid Dynamics." 日本航空宇宙学会, 2007. http://hdl.handle.net/2237/13876.

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Munro, Bruce C. "Airplane trajectory expansion for dynamics inversion." Thesis, This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-07102009-040551/.

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Wu, Kailiang. "Modeling the semiconductor industry dynamics." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45280.

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Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2008.
Includes bibliographical references (p. 89-92).
The semiconductor industry is an exciting and challenging industry. Strong demand at the application end, plus the high capital intensity and rapid technological innovation in manufacturing, makes it difficult to manage supply chain planning and investment in technology transitions. Better understanding the essence of the industry dynamics will help firms win competitive advantages in this turbulent market. In this thesis, we will study semiconductor industry dynamics from three different angles: quantitative modeling, industry dynamics simulation, and strategic analysis. First, we develop a stochastic linear optimization model to address the supplier's "order fulfillment dilemma" suggested by previous empirical studies. The model provides optimal equipment production decisions that minimize the total cost under stochastic demand. To solve the large scale problem, we introduce the Bender's Decomposition, which is proven to outperform the pure Simplex method. Furthermore, we extend the basic model to multiple periods, allowing equipment inventory planning over a period of time. Second, we build a macro-level industry dynamic model using the methodology of System Dynamics. The model includes components of electronics demand projection, fabrication capacity allocation, fabrication cost structure, technology roadmapping as well as equipment production and R&D. The model generates projections of demand , industry productivity, schedule of building new fabrication, adoption of the latest process technology, etc., which are validated by actual industry data. In addition, we devise a control panel in the software that enables the users to implement flexible scenario and sensitivity analysis. Third, we propose a strategic framework for companies to pinpoint the root causes of the supply-demand mismatch problem.
(cont.) This framework considers long lead times, fast clockspeeds, Moore's Law, and risky product and technology, which transitions contribute to the pronounced volatility amplification occurring in the semiconductor industry. This framework, along with several industry successful practices, will assist companies to mitigate the demand volatility and improve their supply chain performance.
by Kailiang Wu.
S.M.

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Williams, Nathan A. "Drag optimization of light trucks using computational fluid dynamics." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03sep%5FWilliams%5FNathan.pdf.

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Thesis (M.S. in Mechanical Engineering and M.S. in Information Technology Management)--Naval Postgraduate School, September 2003.
Thesis advisor(s): Joshua H. Gordis, Dan Boger. Includes bibliographical references (p. 157-158). Also available online.

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Kwok, Terence 1973. "Neural networks with nonlinear system dynamics for combinatorial optimization." Monash University, School of Business Systems, 2001. http://arrow.monash.edu.au/hdl/1959.1/8928.

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Fahrenkopf, Max A. "Optimization, Dynamics and Stability of Non-Linear Separation Processes." Research Showcase @ CMU, 2014. http://repository.cmu.edu/dissertations/390.

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In this thesis we develop a non convex non-linear programming problem that determines the minimum run time of a rapid, gel-free DNA separation technique called micelle end-labeled free solution electrophoresis (ELFSE). Micelle ELFSE is typically performed in capillary electrophoresis where the capillary length, electric field strength, and micelle drag tag size are the primary tuning variables. Using optimization, we demonstrate that capillary electrophoresis can be used to separate up to 600 bases in under 50 minutes. A significant improvement in performance is then shown to be achievable by using parallel capillaries which can separate up to 600 bases in under 5 minutes. Even more improvement is shown to be possible by using alternative separation modes, such as using an EOF counter- ow which enables 600 bases to be separated in under 4.5 minutes using a single capillary, and microfluidics utilizing snapshot detection to yield 600 bases in under 3.5 minutes. Long DNA, above 5000 bases, is particularly challenging to separate quickly. Using Brownian dynamics simulations we show the viability of integrating two DNA separation techniques: end-labeled DNA electrophoresis and entropic trapping. We present simulation results that demonstrate improved performance of the integrated device over entropic trapping alone. Brownian dynamics simulations are very computationally expensive, often taking over 24 hours per data point. We present an acceleration technique called projective integration which may be useful for simulations with a large amount of integration steps. We show that, using a model built from linear regression, periodic extrapolations can be used to decrease computational time. Finally we present the stability of the multi-component distillation column. We demonstrate, through the use of thermodynamics, that the distillation column is asymptotically stable when using pressure, temperature, and level control on the reboiler and condenser.

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Книги з теми "Dynamics of optimization"

Thévenin, Dominique, and Gábor Janiga, eds. Optimization and Computational Fluid Dynamics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-72153-6.

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Dockner, Engelbert J., Richard F. Hartl, Mikulas Luptačik, and Gerhard Sorger, eds. Optimization, Dynamics, and Economic Analysis. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-642-57684-3.

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1966-, Thévenin Dominique, and Janiga Gábor, eds. Optimization and computational fluid dynamics. Berlin: Springer Verlag, 2008.

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Pinto, Alberto, and David Zilberman, eds. Modeling, Dynamics, Optimization and Bioeconomics IV. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78163-7.

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Pinto, Alberto Adrego, and David Zilberman, eds. Modeling, Dynamics, Optimization and Bioeconomics I. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04849-9.

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Junge, Oliver, Oliver Schütze, Gary Froyland, Sina Ober-Blöbaum, and Kathrin Padberg-Gehle, eds. Advances in Dynamics, Optimization and Computation. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51264-4.

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Matsumoto, Akio, ed. Optimization and Dynamics with Their Applications. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4214-0.

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Pinto, Alberto A., and David Zilberman, eds. Modeling, Dynamics, Optimization and Bioeconomics III. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74086-7.

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Pinto, Alberto A., and David Zilberman, eds. Modeling, Dynamics, Optimization and Bioeconomics II. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55236-1.

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N, Bolotnik N., and Gradet͡s︡kiǐ V. G, eds. Manipulation robots: Dynamics, control, and optimization. Boca Raton: CRC Press, 1994.

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Частини книг з теми "Dynamics of optimization"

Stavroulakis, Georgios E. "Transient Dynamics." In Applied Optimization, 187–223. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-0019-3_7.

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Jazar, Reza N. "Suspension Optimization." In Vehicle Dynamics, 939–84. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-8544-5_14.

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Gandolfo, Giancarlo. "Dynamic Optimization." In Economic Dynamics, 597–642. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03871-6_27.

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Jazar, Reza N. "Suspension Optimization." In Vehicle Dynamics, 883–943. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53441-1_13.

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Jazar, Reza N. "Suspension Optimization." In Vehicle Dynamics, 1033–94. Cham: Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-74458-7_13.

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Stavroulakis, Georgios E. "Steady-State Dynamics." In Applied Optimization, 157–86. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-0019-3_6.

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Ghafil, Hazim Nasir, and Károly Jármai. "Dynamics." In Optimization for Robot Modelling with MATLAB, 157–73. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40410-9_7.

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Hritonenko, Natali, and Yuri Yatsenko. "Aggregate Models of Economic Dynamics." In Applied Optimization, 27–40. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4419-9733-3_2.

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Lu, Yong-Zai, Yu-Wang Chen, Min-Rong Chen, Peng Chen, and Guo-Qiang Chen. "Multiobjective Optimization with Extremal Dynamics." In Extremal Optimization, 165–212. Boca Raton : Auerbach Publications, 2015.: Auerbach Publications, 2018. http://dx.doi.org/10.1201/b19572-6.

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Coyle, R. G. "Optimization in practice." In System Dynamics Modelling, 249–96. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4899-2935-8_9.

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Тези доповідей конференцій з теми "Dynamics of optimization"

Karpov, N. S., I. A. Alexandrov, and A. K. Lampezhev. "Optimization of Group Loading of Equipment for Multiproduct Manufacturing." In 2024 Dynamics of Systems, Mechanisms and Machines (Dynamics), 1–5. IEEE, 2024. https://doi.org/10.1109/dynamics64718.2024.10838665.

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Khusainov, Emil, and Vasily Anikin. "Methodology of Construction of Heuristic Algorithm for Optimization of Electrical Engineering Complexes." In 2024 Dynamics of Systems, Mechanisms and Machines (Dynamics), 1–5. IEEE, 2024. https://doi.org/10.1109/dynamics64718.2024.10838661.

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Kulbida, U. N., O. N. Kaneva, and A. V. Zykina. "Media planning optimization treatment." In 2014 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2014. http://dx.doi.org/10.1109/dynamics.2014.7005673.

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Semenikhin, Sviatoslav, and Liudmila Denisova. "Learning to rank based on multi-criteria optimization." In 2017 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2017. http://dx.doi.org/10.1109/dynamics.2017.8239503.

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Denisova, Liudmila A., and Vitalii A. Meshcheryakov. "Control system synthesis based on multicriteria optimization using genetic algorithm." In 2017 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2017. http://dx.doi.org/10.1109/dynamics.2017.8239446.

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Zadorozhnyi, V. N., and T. R. Zakharenkova. "Optimization of channel distribution over nodes in networks with fractal traffic." In 2016 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2016. http://dx.doi.org/10.1109/dynamics.2016.7819112.

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Makkapati, Satheesh, Steve Poe, Kim Ku, and James Dopirak. "Valvetrain dynamics optimization." In 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1998. http://dx.doi.org/10.2514/6.1998-4785.

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Anfilofiev, A. E., I. A. Hodashinsky, and O. O. Evsutin. "Algorithm for tuning fuzzy network attack classifiers based on invasive weed optimization." In 2014 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2014. http://dx.doi.org/10.1109/dynamics.2014.7005632.

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Kolokolov, Alexander A., Alexandra V. Artemova, and Irina E. Kan. "Computer-aided design of some assortment groups of complex products using discrete optimization." In 2014 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2014. http://dx.doi.org/10.1109/dynamics.2014.7005667.

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Mashkov, Yury K., Dmitry N. Korotaev, Marina Yu Baybaratskaya, and Botagoz Sh Alimbaeva. "Research and optimization of technological modes of electro-spark processing details of tribosistem." In 2014 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2014. http://dx.doi.org/10.1109/dynamics.2014.7005682.

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Звіти організацій з теми "Dynamics of optimization"

Arguello, Bryan, Nathan Stewart, Matthew Hoffman, Bethany Nicholson, Richard Garrett, and Emily Moog. Dynamics Informed Optimization forResilient Energy Systems. Office of Scientific and Technical Information (OSTI), October 2022. http://dx.doi.org/10.2172/1893998.

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Negre, Christian, Anders Niklasson, Joshua Finkelstein, and Michael Wall. Next Generation Quantum Based Molecular Dynamics: Hybrid Performance Optimization. Office of Scientific and Technical Information (OSTI), March 2023. http://dx.doi.org/10.2172/1963617.

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Ramamurti, Ravi, and William C. Sandberg. Computational Fluid Dynamics Study for Optimization of a Fin Design. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada441476.

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Bewley, Thomas R. Adjoint-Based Optimization and Control of Complex Dynamics in Fluid Systems. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada441360.

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Pulay, Peter, and Jon Baker. Efficient Modeling of Large Molecules: Geometry Optimization Dynamics and Correlation Energy. Fort Belvoir, VA: Defense Technical Information Center, April 2003. http://dx.doi.org/10.21236/ada416248.

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Shanbhag, Uday V., Tamer Basar, Sean Meyn, and Prashant Mehta. EXTENDING THE REALM OF OPTIMIZATION FOR COMPLEX SYSTEMS: UNCERTAINTY, COMPETITION, AND DYNAMICS. Office of Scientific and Technical Information (OSTI), October 2013. http://dx.doi.org/10.2172/1095695.

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Pasupuleti, Murali Krishna. Mathematical Modeling for Machine Learning: Theory, Simulation, and Scientific Computing. National Education Services, March 2025. https://doi.org/10.62311/nesx/rriv125.

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Анотація:

Abstract Mathematical modeling serves as a fundamental framework for advancing machine learning (ML) and artificial intelligence (AI) by integrating theoretical, computational, and simulation-based approaches. This research explores how numerical optimization, differential equations, variational inference, and scientific computing contribute to the development of scalable, interpretable, and efficient AI systems. Key topics include convex and non-convex optimization, physics-informed machine learning (PIML), partial differential equation (PDE)-constrained AI, and Bayesian modeling for uncertainty quantification. By leveraging finite element methods (FEM), computational fluid dynamics (CFD), and reinforcement learning (RL), this study demonstrates how mathematical modeling enhances AI-driven scientific discovery, engineering simulations, climate modeling, and drug discovery. The findings highlight the importance of high-performance computing (HPC), parallelized ML training, and hybrid AI approaches that integrate data-driven and model-based learning for solving complex real-world problems. Keywords Mathematical modeling, machine learning, scientific computing, numerical optimization, differential equations, PDE-constrained AI, variational inference, Bayesian modeling, convex optimization, non-convex optimization, reinforcement learning, high-performance computing, hybrid AI, physics-informed machine learning, finite element methods, computational fluid dynamics, uncertainty quantification, simulation-based AI, interpretable AI, scalable AI.

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Bylsma, Wesley. Simplified Dynamics and Mobility Factors for Multi-Disciplinary Optimization of an Occupant Centric Platform. Fort Belvoir, VA: Defense Technical Information Center, April 2012. http://dx.doi.org/10.21236/ada559920.

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Slapikas, Robert, Anindya Ghoshal, Luis Bravo, Muthuvel Murugan, and Douglas Wolfe. Molecular Dynamics Analysis and Optimization of Ultra-High-Temperature Ceramic (UHTC)Compositions for Propulsion. Aberdeen Proving Ground, MD: DEVCOM Army Research Laboratory, June 2022. http://dx.doi.org/10.21236/ad1171344.

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Sundaryanto, Bagus, and Yanis C. Yortsos. Optimization of Fluid Front Dynamics in Porous Media Using Rate Control: I. Equal Mobility Fluids. Office of Scientific and Technical Information (OSTI), October 1999. http://dx.doi.org/10.2172/13828.

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