Relevant bibliographies by topics / Multiscale optimization

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  2. Dissertations / Theses
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Academic literature on the topic 'Multiscale optimization'

Author: Grafiati
Published: 19 October 2024

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Journal articles on the topic "Multiscale optimization"

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Xu, Fan, Peter Wai Tat TSE, Yan-Jun Fang, and Jia-Qi Liang. "A fault diagnosis method combined with compound multiscale permutation entropy and particle swarm optimization–support vector machine for roller bearings diagnosis." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 233, no. 4 (July 20, 2018): 615–27. http://dx.doi.org/10.1177/1350650118788929.

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A method based on compound multiscale permutation entropy, support vector machine, and particle swarm optimization for roller bearings fault diagnosis was presented in this study. Firstly, the roller bearings vibration signals under different conditions were decomposed into permutation entropy values by the multiscale permutation entropy and compound multiscale permutation entropy methods. The compound multiscale permutation entropy model combined the different graining sequence information under each scale factor. The average value of each scale factor was regarded as the final entropy value in the compound multiscale permutation entropy model. The compound multiscale permutation entropy model suppressed the shortcomings of poor stability caused by the length of the original signals in the multiscale permutation entropy model. Validity and accuracy are considered in the numerical experiments, and then compared with the computational efficiency of the multiscale permutation entropy method. Secondly, the entropy values of the multiscale permutation entropy/compound multiscale permutation entropy under different scales are regarded as the input of the particle swarm optimization–support vector machine models for fulfilling the fault identification, the classification accuracy is used to verify the effectiveness of the multiscale permutation entropy/compound multiscale permutation entropy with particle swarm optimization–support vector machine. Finally, the experimental results show that the classification accuracy of the compound multiscale permutation entropy model is higher than that of the multiscale permutation entropy.
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Murphy, Ryan, Chikwesiri Imediegwu, Robert Hewson, and Matthew Santer. "Multiscale structural optimization with concurrent coupling between scales." Structural and Multidisciplinary Optimization 63, no. 4 (January 8, 2021): 1721–41. http://dx.doi.org/10.1007/s00158-020-02773-3.

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AbstractA robust three-dimensional multiscale structural optimization framework with concurrent coupling between scales is presented. Concurrent coupling ensures that only the microscale data required to evaluate the macroscale model during each iteration of optimization is collected and results in considerable computational savings. This represents the principal novelty of this framework and permits a previously intractable number of design variables to be used in the parametrization of the microscale geometry, which in turn enables accessibility to a greater range of extremal point properties during optimization. Additionally, the microscale data collected during optimization is stored in a reusable database, further reducing the computational expense of optimization. Application of this methodology enables structures with precise functionally graded mechanical properties over two scales to be derived, which satisfy one or multiple functional objectives. Two classical compliance minimization problems are solved within this paper and benchmarked against a Solid Isotropic Material with Penalization (SIMP)–based topology optimization. Only a small fraction of the microstructure database is required to derive the optimized multiscale solutions, which demonstrates a significant reduction in the computational expense of optimization in comparison to contemporary sequential frameworks. In addition, both cases demonstrate a significant reduction in the compliance functional in comparison to the equivalent SIMP-based optimizations.
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Mjolsness, E., C. D. Garrett, and W. L. Miranker. "Multiscale optimization in neural nets." IEEE Transactions on Neural Networks 2, no. 2 (March 1991): 263–74. http://dx.doi.org/10.1109/72.80337.

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Han, Zhenyu, Shouzheng Sun, Zhongxi Shao, and Hongya Fu. "Multiscale Collaborative Optimization of Processing Parameters for Carbon Fiber/Epoxy Laminates Fabricated by High-Speed Automated Fiber Placement." Advances in Materials Science and Engineering 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/5480352.

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Processing optimization is an important means to inhibit manufacturing defects efficiently. However, processing optimization used by experiments or macroscopic theories in high-speed automated fiber placement (AFP) suffers from some restrictions, because multiscale effect of laying tows and their manufacturing defects could not be considered. In this paper, processing parameters, including compaction force, laying speed, and preheating temperature, are optimized by multiscale collaborative optimization in AFP process. Firstly, rational model between cracks and strain energy is revealed in order that the formative possibility of cracks could be assessed by using strain energy or its density. Following that, an antisequential hierarchical multiscale collaborative optimization method is presented to resolve multiscale effect of structure and mechanical properties for laying tows or cracks in high-speed automated fiber placement process. According to the above method and taking carbon fiber/epoxy tow as an example, multiscale mechanical properties of laying tow under different processing parameters are investigated through simulation, which includes recoverable strain energy (ALLSE) of macroscale, strain energy density (SED) of mesoscale, and interface absorbability and matrix fluidity of microscale. Finally, response surface method (RSM) is used to optimize the processing parameters. Two groups of processing parameters, which have higher desirability, are obtained to achieve the purpose of multiscale collaborative optimization.
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Sivapuram, Raghavendra, Peter D. Dunning, and H. Alicia Kim. "Simultaneous material and structural optimization by multiscale topology optimization." Structural and Multidisciplinary Optimization 54, no. 5 (July 1, 2016): 1267–81. http://dx.doi.org/10.1007/s00158-016-1519-x.

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Fritzen, Felix, Liang Xia, Matthias Leuschner, and Piotr Breitkopf. "Topology optimization of multiscale elastoviscoplastic structures." International Journal for Numerical Methods in Engineering 106, no. 6 (October 6, 2015): 430–53. http://dx.doi.org/10.1002/nme.5122.

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Boucard, P. A., S. Buytet, and P. A. Guidault. "A multiscale strategy for structural optimization." International Journal for Numerical Methods in Engineering 78, no. 1 (April 2, 2009): 101–26. http://dx.doi.org/10.1002/nme.2484.

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Zhao, Ang, Pei Li, Yehui Cui, Zhendong Hu, and Vincent Beng Chye Tan. "Multiscale topology optimization with Direct FE2." Computer Methods in Applied Mechanics and Engineering 419 (February 2024): 116662. http://dx.doi.org/10.1016/j.cma.2023.116662.

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Oliveira, D. F., and A. C. Reynolds. "Hierarchical Multiscale Methods for Life-Cycle-Production Optimization: A Field Case Study." SPE Journal 20, no. 05 (October 20, 2015): 896–907. http://dx.doi.org/10.2118/173273-pa.

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Summary We apply hierarchical multiscale techniques previously developed by the authors to estimate the well controls that maximize the net present value of the long-term production from a real field offshore Brazil. This field has been in production for several years, and it represents a significant share of the overall oil production for the country. The production-optimization step is preceded by a 10-year historical period, where seismic and production data were history matched by use of ensemble-based approaches. The well controls on a sequence of control steps (time intervals) are optimized for the next 10 years of production by use of the hierarchical-multiscale-optimization and the refinement-indicator-based hierarchical-multiscale-optimization techniques, which refine the control steps as the optimization proceeds. The performance of our approaches is compared with that of a reference case, which applies the well rates used to forecast the production of the real field, as well as with the performance of a standard optimization procedure that uses a fixed set of well controls and a simple procedure to refine control steps.
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Pal, Saloni, Richard Clare, Andrew Lambert, and Stephen Weddell. "Multiscale optimization of the geometric wavefront sensor." Applied Optics 60, no. 25 (August 23, 2021): 7536. http://dx.doi.org/10.1364/ao.423536.

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Dissertations / Theses on the topic "Multiscale optimization"

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Lalanne, Jean-Benoît. "Multiscale dissection of bacterial proteome optimization." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/130217.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, May, 2020
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 315-348).
The quantitative composition of proteomes results from biophysical and biochemical selective pressures acting under system-level resource allocation constraints. The nature and strength of these evolutionary driving forces remain obscure. Through the development of analytical tools and precision measurement platforms spanning biological scales, we found evidence of optimization in bacterial gene expression programs. We compared protein synthesis rates across distant lineages and found tight conservation of in-pathway enzyme expression stoichiometry, suggesting generic selective pressures on expression setpoints. Beyond conservation, we used high-resolution transcriptomics to identify numerous examples of stoichiometry preserving cis-elements compensation in pathway operons. Genome-wide mapping of transcription termination sites also led to the discovery of a phylogenetically widespread mode of bacterial gene expression, 'runaway transcription', whereby RNA polymerases are functionally uncoupled from pioneering ribosomes on mRNAs. To delineate biophysical rationales underlying these pressures, we formulated a parsimonious ribosome allocation model capturing the trade-off between reaction flux and protein production cost. The model correctly predicts the expression hierarchy of key translation factors. We then directly measured the quantitative relationship between expression and fitness for specific translation factors in the Gram-positive species Bacillus subtilis. These precision measurements confirmed that endogenous expression maximizes growth rate. Idiosyncratic transcriptional changes in regulons were however observed away from endogenous expression. The resulting physiological burdens sharpened the fitness landscapes. Spurious system-level responses to targeted expression perturbations, called 'regulatory entrenchment', thus exacerbate the requirement for precisely set expression stoichiometry.
by Jean-Benoît Lalanne.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Physics
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Fitriani. "Multiscale Dynamic Time and Space Warping." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45279.

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Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2008.
Includes bibliographical references (p. 149-151).
Dynamic Time and Space Warping (DTSW) is a technique used in video matching applications to find the optimal alignment between two videos. Because DTSW requires O(N4) time and space complexity, it is only suitable for short and coarse resolution videos. In this thesis, we introduce Multiscale DTSW: a modification of DTSW that has linear time and space complexity (O(N)) with good accuracy. The first step in Multiscale DTSW is to apply the DTSW algorithm to coarse resolution input videos. In the next step, Multiscale DTSW projects the solution from coarse resolution to finer resolution. A solution for finer resolution can be found effectively by refining the projected solution. Multiscale DTSW then repeatedly projects a solution from the current resolution to finer resolution and refines it until the desired resolution is reached. I have explored the linear time and space complexity (O(N)) of Multiscale DTSW both theoretically and empirically. I also have shown that Multiscale DTSW achieves almost the same accuracy as DTSW. Because of its efficiency in computational cost, Multiscale DTSW is suitable for video detection and video classification applications. We have developed a Multiscale-DTSW-based video classification framework that achieves the same accuracy as a DTSW-based video classification framework with greater than 50 percent reduction in the execution time. We have also developed a video detection application that is based on Dynamic Space Warping (DSW) and Multiscale DTSW methods and is able to detect a query video inside a target video in a short time.
by Fitriani.
S.M.
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Yourdkhani, Mostafa. "Multiscale modeling and optimization of seashell structure and material." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66991.

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The vast majority of mollusks grow a hard shell for protection. Typical seashells are composed of two distinct layers, with an outer layer made of calcite (a hard but brittle material) and an inner layer made of a tough and ductile material called nacre. Nacre is a biocomposite material that consists of more than 95% of tablet-shape aragonite, CaCO3, and a soft organic material as matrix. Although the brittle ceramic aragonite composes a high volume fraction of nacre, its mechanical prop-erties are found to be surprisingly higher than those of its constituents. It has been suggested that calcite and nacre, two materials with distinct structures and proper-ties, are arranged in an optimal fashion to defeat attacks from predators. This re-search aims at exploring this hypothesis by capturing the design rules of a gastro-pod seashell using multiscale modeling and optimization techniques. At the mi-croscale, a representative volume element of the microstructure of nacre was used to formulate an analytical solution for the elastic modulus of nacre, and a multiax-ial failure criterion as a function of the microstructure. At the macroscale, a two-layer finite element model of the seashell was developed to include shell thick-ness, curvature and calcite/nacre thickness ratio as geometric parameters. The maximum load that the shell can carry at its apex was obtained. A multiscale op-timization approach was also employed to evaluate whether the natural seashell is optimally designed. Finally, actual penetration experiments were performed on red abalone shells to validate the results.
Une vaste majorité des mollusques développent une coquille dure pour leur pro-tection. Une coquille typique est constitué de deux couches distinctes. La couche externe est faite de calcite (un matériau dur mais fragile), tandis que la couche in-terne est composée de nacre, un matériau plus résiliant et ductile. La nacre est un matériau biocomposite constitué de plus de 95% d'aragonite sous forme de ta-blette et d'un matériel organique souple qui forme la matrice. Bien que la cérami-que aragonite constitue une grande portion de la nacre, ses propriétés mécaniques sont étonnamment plus élevées de celles de ses constituants. La calcite et la nacre, deux matériaux avec des propriétés et des structures différentes, sont supposément étalonnées de façon optimale pour combattre les attaques de prédateurs. Cette étude cherche à déterminer les règles de construction d'une coquille de gastropode en utilisant la modélisation multi-échelle et des techniques d'optimisation. À l'échelle microscopique, un volume représentatif de la microstructure de la nacre a été utilisé pour formuler une solution analytique de son module d'élasticité et un critère de fracture multiaxial fonction des dimensions de la microstructure. À l'échelle macroscopique, un modèle d'éléments finis à deux couches de la co-quille à été utilisé pour représenter la curvature et le ratio calcite/nacre en fonction des paramètres géométriques. La charge maximale que la coquille peut supporter à son apex a été déterminée. Une approche d'optimisation multi-échelle a aussi été employée pour évaluer la reconstruction optimale du coquillage naturel. Fina-lement, plusieurs tests ont été effectués sur une coquille d'abalone rouge pour valider les résultats.
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Umoh, Utibe Godwin. "Multiscale analysis of cohesive fluidization." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/28988.

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Fluidization of a granular assembly of solid particles is a process where particles are suspended in a fluid by the upward flow of fluid through the bed. This process is important in industry as it has a wide range of applications due to the high mixing and mass transfer rates present as a result of the rapid movement of particles which occurs in the bed. The dynamics of fluidization is heavily dependent on the particle scale physics and the forces acting at a particle level. For particles with sizes and densities less than 100μm and 103 kg/m3, the importance of interparticle forces such as cohesion to the fluidization phenomena observed increases compared to larger particles where phenomena observed are more dependent on hydrodynamic forces. These smaller sized particles are increasingly in high demand in industrial processes due to the increasing surface area per unit volume obtained by decreasing the particle size. Decreasing particle however leads to an increase in the impact of cohesive interparticle forces present between particles thus altering fluidization phenomena. It is thus necessary to get a greater understanding of how these cohesive forces alter fluidization behaviour both at the particle and also at the bulk scale. This work begins with an experimental study of a fluidized bed using high speed imaging. The applicability of particle image velocimetry for a dense bed is examined with verification and validation studies showing that particle image velocimetry is able to accurately capture averaged velocity profiles for particles at the front wall. A digital image analysis algorithm which is capable of accurately extracting particle solid fraction data for a dense bed at non-optimum lighting conditions was also developed. Together both experimental techniques were used to extract averaged particle mass flux data capable of accurately capturing and probing fluidization phenomena for a dense fluidized bed. This simulation studies carried out for this work looks to examine the impact of cohesive forces introduced using a van der waal cohesion model on phenomena observed at different length scales using DEM-CFD simulations. Numerical simulations were run for Geldart A sized particles at different cohesion levels represented by the bond number and at different inlet gas velocities encompassing the different regimes fluidization regimes present. A stress analysis was used to examine the mechanical state of the expanded bed at different cohesion levels with the vertical component of the total stress showing negative tensile stresses observed at the center of the bed. Further analysis of the contact and cohesive components of the stress together with a kcore and microstructural analysis focusing on the solid fraction and coordination number profiles indicated that this negative total stress was caused by a decrease in the contact stress due to breakage of mechanical contacts as cohesive forces are introduced and increased. A pressure overshoot analysis was also conducted with the magnitude of the overshoot in pressure seen during the pressure drop analysis of a cohesive bed shown to be of equivalent magnitude to the gradient of the total negative stress profile. The in-homogeneous nature of the bed was probed with the focus on how introducing cohesion levels increase the degree of inhomogeneity present in the expanded bed and how local mesoscopic structures change with cohesion and gas velocity. It was shown that increasing cohesion increases the degree of inhomogeneity in the bed as well as increasing the degree of clustering between particles. A majority of particles were shown to be present in a single macroscopic cluster in the mechanical network with distinct local mesoscopic structures forming within the macroscopic cluster. The cohesive bed also expanded as distinct dense regions with low mechanical contact zones in between these regions. A macroscopic cluster analysis showed that the majority of particles are in strong enduring mechanical and cohesive contact. Increasing cohesive forces were also shown to not only create a cohesive support network around the mechanical network but also strengthen the mechanical contact network as well. The significance of the strong and weak mechanical and cohesive forces on fluidization phenomena was also examined with analysis showing that the weak mechanical forces act to support the weak mechanical forces. The cohesive force network however was non coherent with strong forces significantly greater than weak forces. Fluidization phenomena was shown to be driven by the magnitude of the strong cohesive forces set by the minimum particle cutoff distance. This also called into question the significance of the cohesive coordination number which is dependent on the maximum cohesive cutoff. The value of the maximum cutoff was shown to be less significant as no significant changes were observed in the stress and microstructure data as the maximum cutoff was altered. Simulations with different ratios of cohesive and non cohesive particles were also undertaken and showed that a disruption in the cohesive force network leads to changes in the stress state and microstructure of the bed thus changing the fluidization phenomena observed at all length scales. The nature of the strong cohesive force network thus drives fluidization phenomena seen in the bed.
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Sorrentino, Luigi. "Simulation and optimization of crowd dynamics using a multiscale model." Doctoral thesis, Universita degli studi di Salerno, 2012. http://hdl.handle.net/10556/318.

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2010 - 2011
In the last decades, the modeling of crowd motion and pedestrian .ow has attracted the attention of applied mathematicians, because of an increasing num- ber of applications, in engineering and social sciences, dealing with this or similar complex systems, for design and optimization purposes. The crowd has caused many disasters, in the stadiums during some major sporting events as the "Hillsborough disaster" occurred on 15 April 1989 at Hills- borough, a football stadium, in She¢ eld, England, resulting in the deaths of 96 people, and 766 being injured that remains the deadliest stadium-related disaster in British history and one of the worst ever international football accidents. Other example is the "Heysel Stadium disaster" occurred on 29 May 1985 when escaping, fans were pressed against a wall in the Heysel Stadium in Brussels, Belgium, as a result of rioting before the start of the 1985 European Cup Final between Liv- erpool of England and Juventus of Italy. Thirty-nine Juventus fans died and 600 were injured. It is well know the case of the London Millennium Footbridge, that was closed the very day of its opening due to macroscopic lateral oscillations of the structure developing while pedestrians crossed the bridge. This phenomenon renewed the interest toward the investigation of these issues by means of mathe- matical modeling techniques. Other examples are emergency situations in crowded areas as airports or railway stations. In some cases, as the pedestrian disaster in Jamarat Bridge located in South Arabia, mathematical modeling and numerical simulation have already been successfully employed to study the dynamics of the .ow of pilgrims, so as to highlight critical circumstances under which crowd ac- cidents tend to occur and suggest counter-measures to improve the safety of the event. In the existing literature on mathematical modeling of human crowds we can distinguish two approaches: microscopic and macroscopic models. In model at microscopic scale pedestrians are described individually in their motion by ordinary di¤erential equations and problems are usually set in two-dimensional domains delimiting the walking area under consideration, with the presence of obstacles within the domain and a target. The basic modeling framework relies on classical Newtonian laws of point. The model at the macroscopic scale consists in using partial di¤erential equations, that is in describing the evolution in time and space of pedestrians supplemented by either suitable closure relations linking the velocity of the latter to their density or analogous balance law for the momentum. Again, typical guidelines in devising this kind of models are the concepts of preferred direction of motion and discomfort at high densities. In the framework of scalar conservation laws, a macroscopic onedimensional model has been proposed by Colombo and Rosini, resorting to some common ideas to vehicular tra¢ c modeling, with the speci.c aim of describing the transition from normal to panic conditions. Piccoli and Tosin propose to adopt a di¤erent macroscopic point of view, based on a measure-theoretical framework which has recently been introduced by Canuto et al. for coordination problems (rendez-vous) of multiagent systems. This approach consists in a discrete-time Eulerian macroscopic representation of the system via a family of measures which, pushed forward by some motion mappings, provide an estimate of the space occupancy by pedestrians at successive time steps. From the modeling point of view, this setting is particularly suitable to treat nonlocal interactions among pedestrians, obstacles, and wall boundary conditions. A microscopic approach is advantageous when one wants to model di¤erences among the individuals, random disturbances, or small environments. Moreover, it is the only reliable approach when one wants to track exactly the position of a few walkers. On the other hand, it may not be convenient to use a microscopic approach to model pedestrian .ow in large environments, due to the high com- putational e¤ort required. A macroscopic approach may be preferable to address optimization problems and analytical issues, as well as to handle experimental data. Nonetheless, despite the fact that self-organization phenomena are often visible only in large crowds, they are a consequence of strategical behaviors devel- oped by individual pedestrians. The two scales may reproduce the same features of the group behavior, thus providing a perfect matching between the results of the simulations for the micro- scopic and the macroscopic model in some test cases. This motivated the multiscale approach proposed by Cristiani, Piccoli and Tosin. Such an approach allows one to keep a macroscopic view without losing the right amount of .granularity,.which is crucial for the emergence of some self-organized patterns. Furthermore, the method allows one to introduce in a macroscopic (averaged) context some micro- scopic e¤ects, such as random disturbances or di¤erences among the individuals, in a fully justi.able manner from both the physical and the mathematical perspec- tive. In the model, microscopic and macroscopic scales coexist and continuously share information on the overall dynamics. More precisely, the microscopic part tracks the trajectories of single pedestrians and the macroscopic part the density of pedestrians using the same evolution equation duly interpreted in the sense of measures. In this respect, the two scales are indivisible. Starting from model of Cristiani, Piccoli and Tosin we have implemented algo- rithms to simulate the pedestrians motion toward a target to reach in a bounded area, with one or more obstacles inside. In this work di¤erent scenarios have been analyzed in order to .nd the obstacle con.guration which minimizes the pedes- trian average exit time. The optimization is achieved using to algorithms. The .rst one is based on the exhaustive exploration of all positions: the average exit time for all scenarios is computed and then the best one is chosen. The second algorithm is of steepest descent type according to which the obstacle con.guration corresponding to the minimum exit time is found using an iterative method. A variant has been introduced to the algorithm so to obtain a more e¢ cient proce- dure. The latter allows to .nd better solutions in few steps than other algorithms. Finally we performed other simulations with bounded domains like a classical .at with .ve rooms and two exits, comparing the results of three di¤erent scenario changing the positions of exit doors. [edited by author]
X n.s.
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Parno, Matthew David. "A multiscale framework for Bayesian inference in elliptic problems." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/65322.

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Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2011.
Page 118 blank. Cataloged from PDF version of thesis.
Includes bibliographical references (p. 112-117).
The Bayesian approach to inference problems provides a systematic way of updating prior knowledge with data. A likelihood function involving a forward model of the problem is used to incorporate data into a posterior distribution. The standard method of sampling this distribution is Markov chain Monte Carlo which can become inefficient in high dimensions, wasting many evaluations of the likelihood function. In many applications the likelihood function involves the solution of a partial differential equation so the large number of evaluations required by Markov chain Monte Carlo can quickly become computationally intractable. This work aims to reduce the computational cost of sampling the posterior by introducing a multiscale framework for inference problems involving elliptic forward problems. Through the construction of a low dimensional prior on a coarse scale and the use of iterative conditioning technique the scales are decouples and efficient inference can proceed. This work considers nonlinear mappings from a fine scale to a coarse scale based on the Multiscale Finite Element Method. Permeability characterization is the primary focus but a discussion of other applications is also provided. After some theoretical justification, several test problems are shown that demonstrate the efficiency of the multiscale framework.
by Matthew David Parno.
S.M.
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MEJIAS, TUNI JESUS ALBERTO. "Multiscale approach applied to fires in tunnels, Model optimization and development." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2960751.

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Chen, Minghan. "Stochastic Modeling and Simulation of Multiscale Biochemical Systems." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/90898.

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Numerous challenges arise in modeling and simulation as biochemical networks are discovered with increasing complexities and unknown mechanisms. With the improvement in experimental techniques, biologists are able to quantify genes and proteins and their dynamics in a single cell, which calls for quantitative stochastic models for gene and protein networks at cellular levels that match well with the data and account for cellular noise. This dissertation studies a stochastic spatiotemporal model of the Caulobacter crescentus cell cycle. A two-dimensional model based on a Turing mechanism is investigated to illustrate the bipolar localization of the protein PopZ. However, stochastic simulations are often impeded by expensive computational cost for large and complex biochemical networks. The hybrid stochastic simulation algorithm is a combination of differential equations for traditional deterministic models and Gillespie's algorithm (SSA) for stochastic models. The hybrid method can significantly improve the efficiency of stochastic simulations for biochemical networks with multiscale features, which contain both species populations and reaction rates with widely varying magnitude. The populations of some reactant species might be driven negative if they are involved in both deterministic and stochastic systems. This dissertation investigates the negativity problem of the hybrid method, proposes several remedies, and tests them with several models including a realistic biological system. As a key factor that affects the quality of biological models, parameter estimation in stochastic models is challenging because the amount of empirical data must be large enough to obtain statistically valid parameter estimates. To optimize system parameters, a quasi-Newton algorithm for stochastic optimization (QNSTOP) was studied and applied to a stochastic budding yeast cell cycle model by matching multivariate probability distributions between simulated results and empirical data. Furthermore, to reduce model complexity, this dissertation simplifies the fundamental cooperative binding mechanism by a stochastic Hill equation model with optimized system parameters. Considering that many parameter vectors generate similar system dynamics and results, this dissertation proposes a general α-β-γ rule to return an acceptable parameter region of the stochastic Hill equation based on QNSTOP. Different objective functions are explored targeting different features of the empirical data.
Doctor of Philosophy
Modeling and simulation of biochemical networks faces numerous challenges as biochemical networks are discovered with increased complexity and unknown mechanisms. With improvement in experimental techniques, biologists are able to quantify genes and proteins and their dynamics in a single cell, which calls for quantitative stochastic models, or numerical models based on probability distributions, for gene and protein networks at cellular levels that match well with the data and account for randomness. This dissertation studies a stochastic model in space and time of a bacterium’s life cycle— Caulobacter. A two-dimensional model based on a natural pattern mechanism is investigated to illustrate the changes in space and time of a key protein population. However, stochastic simulations are often complicated by the expensive computational cost for large and sophisticated biochemical networks. The hybrid stochastic simulation algorithm is a combination of traditional deterministic models, or analytical models with a single output for a given input, and stochastic models. The hybrid method can significantly improve the efficiency of stochastic simulations for biochemical networks that contain both species populations and reaction rates with widely varying magnitude. The populations of some species may become negative in the simulation under some circumstances. This dissertation investigates negative population estimates from the hybrid method, proposes several remedies, and tests them with several cases including a realistic biological system. As a key factor that affects the quality of biological models, parameter estimation in stochastic models is challenging because the amount of observed data must be large enough to obtain valid results. To optimize system parameters, the quasi-Newton algorithm for stochastic optimization (QNSTOP) was studied and applied to a stochastic (budding) yeast life cycle model by matching different distributions between simulated results and observed data. Furthermore, to reduce model complexity, this dissertation simplifies the fundamental molecular binding mechanism by the stochastic Hill equation model with optimized system parameters. Considering that many parameter vectors generate similar system dynamics and results, this dissertation proposes a general α-β-γ rule to return an acceptable parameter region of the stochastic Hill equation based on QNSTOP. Different optimization strategies are explored targeting different features of the observed data.
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Ahamad, Intan Salwani. "Multiscale line search in interior point methods for nonlinear optimization and applications." Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612762.

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Arabnejad, Sajad. "Multiscale mechanics and multiobjective optimization of cellular hip implants with variable stiffness." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=119630.

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Bone resorption and bone-implant interface instability are two bottlenecks of current orthopaedic hip implant designs. Bone resorption is often triggered by mechanical bio-incompatibility of the implant with the surrounding bone. It has serious clinical consequences in both primary and revision surgery of hip replacements. After primary surgery, bone resorption can cause periprosthetic fractures, leading to implant loosening. For the revision surgery, the loss of bone stock compromises the ability of bone to adequately fix the implant. Interface instability, on the other hand, occurs as a result of excessive micromotion and stress at the bone-implant interface, which prevents implant fixation. As a result, the implant fails, and revision surgery is required.Many studies have been performed to design an implant minimizing both bone resorption and interface instability. However, the results have not been effective since minimizing one objective would penalize the other. As a result, among all designs available in the market, there is no implant that can concurrently minimize these two conflicting objectives. The goal of this thesis is to design an orthopaedic hip replacement implant that can simultaneously minimize bone resorption and implant instability. We propose a novel concept of a variable stiffness implant that is implemented through the use of graded lattice materials. A design methodology based on multiscale mechanics and multiobjective optimization is developed for the analysis and design of a fully porous implant with a lattice microstructure. The mechanical properties of the implant are locally optimized to minimize bone resorption and interface instability. Asymptotic homogenization (AH) theory is used to capture stress distribution for failure analysis throughout the implant and its lattice microstructure. For the implant lattice microstructure, a library of 2D cell topologies is developed, and their effective mechanical properties, including elastic moduli and yield strength, are computed using AH. Since orthopaedic hip implants are generally expected to support dynamic forces generated by human activities, they should be also designed against fatigue fracture to avoid progressive damage. A methodology for fatigue design of cellular materials is proposed and applied to a two dimensional implant, with Kagome and square cell topologies. A lattice implant with an optimum distribution of material properties is proved to considerably reduce the amount of bone resorption and interface shear stress compared to a fully dense titanium implant. The manufacturability of the lattice implants is demonstrated by fabricating a set of 2D proof-of-concept prototypes using Electron Beam Melting (EBM) with Ti6Al4V powder. Optical microscopy is used to measure the morphological parameters of the cellular microstructure. The numerical analysis and the manufacturability tests performed in this preliminary study suggest that the developed methodology can be used for the design and manufacturing of novel orthopaedic implants that can significantly contribute to reducing some clinical consequences of current implants.
La résorption osseuse et l'instabilité de l'interface os-implant sont deux goulots d'étranglement de modèles actuels d'implants orthopédiques de hanche. La résorption osseuse est souvent déclenchée par une bio-incompatibilité mécanique de l'implant avec l'os environnant. Il en résulte de graves conséquences cliniques à la fois en chirurgie primaire et en chirurgie de révision des arthroplasties de la hanche. Après la chirurgie primaire, la résorption osseuse peut entraîner des fractures périprothétiques, conduisant au descellement de l'implant. Pour la chirurgie de révision, la perte de substance osseuse compromet la capacité de l'os à bien fixer l'implant. L'instabilité de l'interface, d'autre part, se produit à la suite d'un stress excessif et de micromouvements à l'interface os-implant, ce qui empêche la fixation des implants. De ce fait, l'implant échoue, et la chirurgie de révision est nécessaire.De nombreuses études ont été réalisées pour concevoir un implant qui minimise la résorption osseuse et l'instabilité de l'interface. Cependant, les résultats n'ont pas été efficaces, car minimiser un objectif pénaliserait l'autre. En conséquence, parmi tous les modèles disponibles sur le marché, il n'y a pas d'implant qui puisse en même temps réduire ces deux objectifs contradictoires. L'objectif de cette thèse est de concevoir une prothèse orthopédique de la hanche qui puisse simultanément réduire la résorption osseuse et l'instabilité de l'implant. Nous proposons un nouveau concept d'implant à raideur variable qui est mis en œuvre grâce à l'utilisation de matériaux assemblés en treillis.Une méthodologie de conception basée sur la mécanique multi-échelle et l'optimisation multiobjectif est développé pour l'analyse et la conception d'un implant totalement poreux avec une microstructure en treillis. Les propriétés mécaniques de l'implant sont localement optimisés pour minimiser la résorption osseuse et l'instabilité d'interface. La théorie de l'homogénéisation asymptotique (HA) est utilisée pour capturer la distribution des contraintes pour l'analyse des défaillances tout le long de l'implant et de sa microstructure en treillis. Concernant cette microstructure en treillis, une bibliothèque de topologies de cellules 2D est développée, et leurs propriétés mécaniques efficaces, y compris les modules d'élasticité et la limite d'élasticité, sont calculées en utilisant le théorie HA. Puisque les prothèses orthopédiques de hanche sont généralement censées soutenir les forces dynamiques générées par les activités humaines, elles doivent être également conçues contre les fractures de fatigue pour éviter des dommages progressifs. Une méthodologie pour la conception en fatigue des matériaux cellulaires est proposée et appliquée à un implant en deux dimensions, et aux topologies de cellules carrées et de Kagome. Il est prouvé qu'un implant en treillis avec une répartition optimale des propriétés des matériaux réduit considérablement la quantité de la résorption osseuse et la contrainte de cisaillement de l'interface par rapport à un implant en titane totalement dense. La fabricabilité des implants en treillis est démontrée par la fabrication d'un ensemble de concepts de prototypes utilisant la fusion par faisceau d'électronsde poudre Ti6Al4V. La microscopie optique est utilisée pour mesurer les paramètres morphologiques de la microstructure cellulaire. L'analyse numérique et les tests de fabricabilité effectués dans cette étude préliminaire suggèrent que la méthodologie développée peut être utilisée pour la conception et la fabrication d'implants orthopédiques innovants qui peuvent contribuer de manière significative à la réduction des conséquences cliniques des implants actuels.
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Books on the topic "Multiscale optimization"

1

Hager, William W., Shu-Jen Huang, Panos M. Pardalos, and Oleg A. Prokopyev, eds. Multiscale Optimization Methods and Applications. Boston, MA: Springer US, 2006. http://dx.doi.org/10.1007/0-387-29550-x.

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Günther, Michael, ed. Coupled Multiscale Simulation and Optimization in Nanoelectronics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46672-8.

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Christofides, Panagiotis D., Antonios Armaou, Yiming Lou, and Amit Varshney. Control and Optimization of Multiscale Process Systems. Boston: Birkhäuser Boston, 2009. http://dx.doi.org/10.1007/978-0-8176-4793-3.

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Lou. Control and Optimization of Multiscale Process Systems. Boston: Birkhäuser Boston, 2009.

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Multiscale Structural Topology Optimization. Elsevier, 2016. http://dx.doi.org/10.1016/c2015-0-01254-0.

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Xia, Liang. Multiscale Structural Topology Optimization. Elsevier, 2016.

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Xia, Liang. Multiscale Structural Topology Optimization. Elsevier, 2016.

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Cheng, Gengdong, and Jun Yan. Multiscale Optimization and Material Design. World Scientific Publishing Co Pte Ltd, 2020.

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Pardalos, P. M. Multiscale Optimization Methods and Applications. Springer, 2014.

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Pardalos, P. M. Multiscale Optimization Methods and Applications. Springer, 2006.

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Book chapters on the topic "Multiscale optimization"

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Lewis, Robert Michael, and Stephen G. Nash. "Practical Aspects of Multiscale Optimization Methods for VLSICAD." In Combinatorial Optimization, 265–91. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4757-3748-6_7.

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Christofides, Panagiotis D., Antonios Amaou, Yiming Lou, and Amit Varsheny. "Optimization of Multiscale Process Systmes." In Control and Optimization of Multiscale Process Systems, 1–33. Boston: Birkhäuser Boston, 2008. http://dx.doi.org/10.1007/978-0-8176-4793-3_6.

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Kuś, Wacław, and Tadeusz Burczyński. "Bioinspired Algorithms in Multiscale Optimization." In Advanced Structured Materials, 183–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-05241-5_10.

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Wang, Yanfei, and Qinghua Ma. "Iterated Adaptive Regularization for the Operator Equations of the First Kind." In Multiscale Optimization Methods and Applications, 367–77. Boston, MA: Springer US, 2006. http://dx.doi.org/10.1007/0-387-29550-x_19.

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Binder, Thomas, Luise Blank, Wolfgang Dahmen, and Wolfgang Marquardt. "Multiscale Concepts for Moving Horizon Optimization." In Online Optimization of Large Scale Systems, 341–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04331-8_19.

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Christofides, Panagiotis D., Antonios Amaou, Yiming Lou, and Amit Varsheny. "Multiscale Process Modeling and Simulation." In Control and Optimization of Multiscale Process Systems, 1–15. Boston: Birkhäuser Boston, 2008. http://dx.doi.org/10.1007/978-0-8176-4793-3_2.

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Binder, Thomas, Luise Blank, Wolfgang Dahmen, and Wolfgang Marquardt. "Iterative Multiscale Methods for Process Monitoring." In Fast Solution of Discretized Optimization Problems, 19–34. Basel: Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8233-0_2.

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Ramm, Ekkehard, Andrea Erhart, Thomas Hettich, Ingrid Bruss, Frédéric Hilchenbach, and Junji Kato. "Damage Propagation in Composites – Multiscale Modeling and Optimization." In Multiscale Methods in Computational Mechanics, 281–304. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9809-2_15.

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de Wit, Albert, and Fred van Keulen. "Framework for Multi-Level Optimization of Complex Systems." In Multiscale Methods in Computational Mechanics, 347–77. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9809-2_18.

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Yuan, Xiaohui, Jing Peng, and Yasumasa Nishiura. "Particle Swarm Optimization with Multiscale Searching Method." In Computational Intelligence and Security, 669–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11596448_99.

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Conference papers on the topic "Multiscale optimization"

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Dong, Junjie, Ran Liu, Xiaolin Zhang, and Jing Meng. "Cellular Image Segmentation Model Based on Multiscale and Attention Mechanisms." In 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS), 619–24. IEEE, 2024. http://dx.doi.org/10.1109/docs63458.2024.10704422.

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Liu, Bo feng, Haiyang Yu, Xiaojuan Hu, and Yanfeng Li. "Multiscale network combined with multiloss optimization for low-light image super-resolution reconstruction." In 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), edited by Xin Zhang and Minghao Yin, 80. SPIE, 2024. http://dx.doi.org/10.1117/12.3048271.

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Kakodkar, Rahul, Betsie Montano Flores, Marco De Sousa, Yilun Lin, and Efstratios N. Pistikopoulos. "Towards Energy and Material Transition Integration � A Systematic Multi-scale Modeling and Optimization Framework." In Foundations of Computer-Aided Process Design, 461–68. Hamilton, Canada: PSE Press, 2024. http://dx.doi.org/10.69997/sct.171988.

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The energy transition is driven both by the motivation to decarbonize as well as the decrease in cost of low carbon technology. Net-carbon neutrality over the lifetime of technology use can neither be quantitatively assessed nor realized without accounting for the flows of carbon comprehensively from cradle to grave. Sources of emission are disparate with contributions from resource procurement, process establishment and function, and material refining. The synergies between the constituent value chains are especially apparent in the mobility transition which involves (i) power generation, storage and dispatch, (ii) synthesis of polymeric materials, (iii) manufacturing of vehicles and establishment of infrastructure. Decision-making frameworks that can coordinate these aspects and provide cooperative sustainable solutions are needed. To this end, we present a multiscale modeling and optimization framework for the simultaneous resolution of the material and energy value chains. A case study focusing on the transition of mobility technology towards electric vehicles in Texas is presented. The key contributions of the proposed framework are (i) integrated network design and operational scheduling, (ii) the tracking of disparate emissions, (ii) simultaneous modeling of the material and energy supply chains, (iv) implementation on energiapy, a python package for the multiscale modeling and optimization of energy systems.
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Dowling, Alexander W. "Artificial Intelligence and Machine Learning for Sustainable Molecular-to-Systems Engineering." In Foundations of Computer-Aided Process Design, 22–31. Hamilton, Canada: PSE Press, 2024. http://dx.doi.org/10.69997/sct.114705.

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Sustainability encompasses many wicked problems involving complex interdependencies across social, natural, and engineered systems. We argue holistic multiscale modeling and decision-support frameworks are needed to address multifaceted interdisciplinary aspects of these wicked problems. This review highlights three emerging research areas for artificial intelligence (AI) and machine learning (ML) in molecular-to-systems engineering for sustainability: (1) molecular discovery and materials design, (2) automation and self-driving laboratories, (3) process and systems-of-systems optimization. Recent advances in AI and ML are highlighted in four contemporary application areas in chemical engineering design: (1) equitable energy systems, (2) decarbonizing the power sector, (3) circular economies for critical materials, and (4) next-generation heating and cooling. These examples illustrate how AI and ML enable more sophisticated interdisciplinary multiscale models, faster optimization algorithms, more accurate uncertainty quantification, smarter and faster data collection, and incorporation of diverse stakeholders into decision-making processes, improving the robustness of engineering and policy designs while focusing on the multifaceted goals and constraints in wicked problems.
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Bonnier, N., and E. P. Simoncelli. "Locally adaptive multiscale contrast optimization." In 2005 International Conference on Image Processing. IEEE, 2005. http://dx.doi.org/10.1109/icip.2005.1529909.

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Kim, Yoon Young, and Dong Hoon Jung. "Multiscale Paradigm in Genetic Algorithm." In 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-5428.

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Oh, Yoonho, Myeongsu Seong, Sungchul Kim, Seonghyun Kim, and Jae Gwan Kim. "Optimization of DRS-DCS system for measurement of tissue metabolism (Conference Presentation)." In Multiscale Imaging and Spectroscopy, edited by Kristen C. Maitland, Darren M. Roblyer, and Paul J. Campagnola. SPIE, 2020. http://dx.doi.org/10.1117/12.2545913.

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Zhai, Jingmei, and Xiao Xu. "Multiscale rough set model and optimization." In 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2010. http://dx.doi.org/10.1109/fskd.2010.5569348.

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Wang, Lijun, Kaijian He, Yingchao Zou, and Zhimeng Feng. "Multiscale Fractal Analysis of Electricity Markets." In 2014 Seventh International Joint Conference on Computational Sciences and Optimization (CSO). IEEE, 2014. http://dx.doi.org/10.1109/cso.2014.79.

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Burblies, Andreas, Matthias Busse, Glaucio H. Paulino, Marek-Jerzy Pindera, Robert H. Dodds, Fernando A. Rochinha, Eshan Dave, and Linfeng Chen. "Computer Based Porosity Design by Multi Phase Topology Optimization." In MULTISCALE AND FUNCTIONALLY GRADED MATERIALS 2006. AIP, 2008. http://dx.doi.org/10.1063/1.2896791.

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Reports on the topic "Multiscale optimization"

1

Bhattacharya, Kaushik. Multiscale Modeling and Process Optimization for Engineered Microstructural Complexity. Fort Belvoir, VA: Defense Technical Information Center, October 2007. http://dx.doi.org/10.21236/ada490968.

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Haile, S. M., M. Ortiz, G. Ravichandran, E. Ustandag, R. M. Murray, D. G. Godwin, K. Bhattacharya, H. A. Atwater, and W. A. Goddard III. Multiscale Modeling and Process Optimization for Engineered Microstructural Complexity. Fort Belvoir, VA: Defense Technical Information Center, October 2007. http://dx.doi.org/10.21236/ada572383.

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Becker, R., M. McElfresh, C. Lee, R. Balhorn, and D. White. Multiscale Modeling of Nano-scale Phenomena: Towards a Multiphysics Simulation Capability for Design and Optimization of Sensor Systems. Office of Scientific and Technical Information (OSTI), December 2003. http://dx.doi.org/10.2172/15013766.

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Al-Jassim, Mowafak, and Steve Harvey. Addressing Critical Problems in Materials Science Through Multiscale and Multimode Characterization (Project 1); Characterization and Optimization of Novel Triple-Conducting Oxide Materials for Energy Applications (Project 2): Cooperative Research and Development Final Report, CRADA Number CRD-17-00711. Office of Scientific and Technical Information (OSTI), February 2024. http://dx.doi.org/10.2172/2318703.

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