Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 42 for “"reduced models"”.
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Studies of protein designability using reduced models
… attempted to solve the problem have relied upon reduced models of proteins. In particular, the 2D square and the 3D cubic lattices together with reduced amino acid alphabets have been examined extensively and have lead to interesting results that shed some light on evolutionary relationship among …
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Generalised nonlinear stability of stratified shear flows: adjoint-based optimisation, Koopman modes, and reduced models
In this thesis I investigate a number of problems in the nonlinear stability of density stratified plane Couette flow. I begin by describing the history of transient growth phenomena, and in particular the recent application of adjoint based optimisation to find nonlinear optimal perturbations and …
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A Procedure To Develop Scalable Reduced Models For The Transient Response of Sleepers In Conventional And High-speed Railway Lines.
<p>This work presents the development of scalable reduced models for the through-the soil interaction and traveling wave effects of distant sleepers in a long railway track. For development purposes, and, without loss of generality, the geometry of the sleepers is consistent with the UIC-60 track …
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Methods for the direct simulation of nanoscale film breakup and contact angles
… times for direct simulations. For these reasons, reduced models are preferable in many contexts, even when it is not clear that such reduced models strictly apply. Recent advances in nanotechnology motivate the comparison between direct simulations and reduced models by presenting situations in …
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Hessian-based model reduction with applications to initial-condition inverse problems
(cont.) Reduced-order models that are able to approximate output quantities of interest of high-fidelity computational models over a wide range of input parameters play an important role in making tractable large-scale optimal design, optimal control, and inverse problem applications. We consider …
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PREDICTORS OF SLEEP QUANTITY AND QUALITY IN COLLEGE STUDENTS
… physical wellbeing. Using path analysis, three reduced models, one for each of the three dependent variables (weekday sleep length, weekend day sleep length, and overall sleep quality, were produced. Through ×2 testing, reduced models for all three models fit the full model's data; deleted paths …
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Parametric Dynamical Systems: Transient Analysis and Data Driven Modeling
… parametric dynamical systems. Since these models need to be simulated for a variety of parameter values, the computational burden they incur becomes increasingly difficult. To address these issues, parametric reduced models have encountered increased popularity in recent years. We are …
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Reduced order power system models for transient stability studies
… cannot be performed on the full power system. A reduced model must be used. In this thesis, various methods of obtaining reduced models for use in the relay will be explored. The models will be verified with a full system model using Electric Power Research Institute's (EPRI) Extended …
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From data to dynamics: discovering governing equations from data
… successful at discovering governing laws and reduced models from data, many challenges still remain. In this work, we focus on the discovery of nonlinear dynamical systems models from data. We present several methods based on the sparse identification of nonlinear dynamics (SINDy) algorithm. …
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Adaptive Finite Element Methods for Optimization in Partial Differential Equations
… element method. The mesh design in the resulting reduced models is controlled by residual-based a posteriori error estimates. These are derived by duality arguments employing the cost functional of the optimization problem for controlling the discretization error. In this case, the computed state …
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Interpolation Methods for the Model Reduction of Bilinear Systems
… of the underlying physical phenomenon, these models frequently have state-spaces of very large dimension, resulting in the need for model reduction. In this work, we introduce two new methods for the model reduction of bilinear systems in an interpolation framework. Our first approach is to …
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An error-controlled adaptive chemistry method for reacting flow simulations
… range of reaction conditions. As a result, reduced models that contain fewer reactions and/or species while still capturing the "important" kinetics are often used in place of the full comprehensive reaction model in modeling complex reacting flows. "Adaptive Chemistry" - a method that uses …
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Experimental and theoretical investigation of optimal control methods with model reduction
… the application a correction method to the reduced models developed for the second structure. The correction method was shown to work with good results on one reduced model and with poor results on the second reduced model. Two direct rate feedback control laws and a linear quadratic …
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Stability-preserving model reduction for linear and nonlinear systems arising in analog circuit applications
… and slow. The ability to generate parameterized reduced order models of analog systems could serve as a first step toward the automatic and accurate characterization of geometrically complex components and subcircuits, eventually enabling their synthesis and optimization. This thesis presents …
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Model Reduction Using Semidefinite Programming
… time invariant systems are investigated. The reduced models are computed using semidefinite programming. Two ways of imposing the stability constraint are considered. However, both approaches add a positivity constraint to the program. The input to the algorithms is a number of frequency …
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An application of diagnostic modeling to a situational judgment test assessing emotional intelligence
… best reproduced the SJT data among the other reduced models due to its statistical generality. However, a higher order structure of EI was not found in the CDM analysis. Among other commonly used methodologies, the CDM approach fully reflected the theoretical framework of EI and provided …
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Model reduction in physical domain
… computer algorithms can produce very detailed models for complex systems with little time and effort. However, over complicated models may not be efficient. Therefore, reducing a model to a more manageable size has become an attractive research topic. A very useful type of reduced models is …
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Reduced-Order Modeling and Design Optimization of Thermomechanical Shape Memory Alloy-Based Bistable Microactuators and Hyperelastic Material Systems
… actuator design. To reduce computational cost, reduced-order modeling techniques based on proper orthogonal decomposition (POD) are introduced. These methods significantly decrease the number of degrees of freedom while maintaining high accuracy. The reduced models show strong agreement with …
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A shifting method for dynamic system Model Order Reduction
… the same time, the need for more comprehensive models of systems is generating problems with increasing numbers of outputs and inputs. Classical methods, which were developed for Single-Input Single-Output (SISO) systems, generate reduced models that are too computationally inefficient for large …
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Data-Driven Deep Learning Methods for Physically-Based Simulations
… Fracture Networks, training Deep Learning models as reduced models for Uncertainty Quantification. In particular, we look for trained Neural Networks able to predict the outflowing fluxes of a Discrete Fracture Network model. These Neural Networks are also exploited to define a new backbone …
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