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 74 for “"Gradient-based optimization"”.
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Regular sensitivity calculation and gradient-based optimization of chaotic dynamical systems
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-12-01
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Gradient-Based Optimization of ReaxFF Parameters Using Pytorch for the Study of Silica Precipitation
… fit the parameters, which results in excessive optimization times. Recent advances in machine learning made it possible to drastically speed up the process by utilizing the gradient of the potential. In this work, the gradient-based optimization of reactive force-field parameters using Pytorch …
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Real-Time Trajectory Optimization for High-Performance Guidance & Control
… presents trajectory generation methods, using gradient-based optimization and set-based dynamic programming, for a large class of optimal, robust, and resilient control problems. These methods are intended for adoption onboard agile autonomous systems—such as reusable rockets—that mandate …
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Sensitivities in Option Pricing Models
… the model and the market observations. Efficient gradient based optimization requires accurate gradient estimation of the cost function. In this thesis we highlight the adjoint method for computing gradients of the cost function in the context of gradient based optimization and show its …
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Density-to-Potential Inversions in Density Functional Theory
… The inversion methods use classical gradient-based optimization routines that are constrained to satisfy the governing partial differential equations. Numerous examples are given to illustrate the strengths and weaknesses of the different inversion methods.
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Algorithmic Modifications to a Multidisciplinary Design Optimization Model of Containerships
… ship should meet. The multidisciplinary design optimization model is a tool that combines an analysis and an optimization process and uses a measure of merit to obtain what it infers to be the best design. All that the designer has to know is the range of values of certain design variables that …
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FITTING A PARAMETRIC MODEL TO A CLOUD OF POINTS VIA OPTIMIZATION METHODS
… to a cloud of</p> <p> points. The process uses a gradient-based optimization technique,</p> <p> which is applied to the whole cloud, without the need to segment or</p> <p> classify the points in the cloud a priori.</p> <p> First, for the points associated with any component, a variant of</p> <p> …
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Centrifugal compressor return channel shape optimization using adjoint method
… describes the construction of an automated gradient-based optimization process using the adjoint method and its application to centrifugal compressor return channel loss reduction. A proper objective function definition and a generalized geometry parametrization and manipulation algorithm …
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MULTIDISCIPLINARY OPTIMIZATION OF NON-SPHERICAL, BLUNT-BODY HEAT SHIELDS FOR A PLANETARY ENTRY VEHICLE
Gradient-based optimization of the aerodynamic performance, static stability, and stagnation-point heat transfer has been completed to find optimal heat shield geometries for blunt-body planetary entry vehicles. In the parametric study, performance trends have been identified by varying geometric …
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Modeling and Optimization of Turbine-Based Combined-Cycle Engine Performance
… combining the global accuracy of probabilistic optimization with the local efficiency of gradient-based optimization. Trade studies are performed to determine the sensitivity of TBCC performance to various design variables and engine parameters. The optimization is quite effective, producing …
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Simultaneous packing and routing optimization of thermal-fluid systems
… design of thermo-mechanical systems. The optimization simultaneously finds the best placement of devices as well as placement and size of routing segments. The layout must satisfy geometric constraints to avoid interference between components. Constraints based on physics models are also …
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Genetic optimization applied to via and route strategy
… OrbitIO platform. We evaluated multiple non-gradient-based optimization strategies and compiled data of their performance. From these tests, a genetic algorithm-based strategy was sought due to its fast convergence and the ability to substitute cost functions. In this study, we converted the …
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On the Effect of Numerical Noise in Simulation-Based Optimization
… noise is a prevalent concern in many practical optimization problems. Convergence of gradient based optimization algorithms in the presence of numerical noise is not always assured. One way to improve optimization algorithm performance in the presence of numerical noise is to adjust the method …
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A generalized approach for calculation of the eigenvector sensitivity for various eigenvector normalizations
Sensitivity analysis is an important step in any gradient based optimization problem. Eigenvalue and Eigenvector Sensitivity Analysis has been a major area for more than three decades in structural optimization. An efficient and generalized method is required to do the sensitivity analysis as it …
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The ChainQueen differentiable physics engine
… favored, as they can be incorporated into gradient-based optimization algorithms that are efficient in solving inverse problems such as optimal control and motion planning. Simulating deformable objects is, however, more challenging compared to rigid body dynamics. The underlying physical …
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Theory and Practice of Large-scale Logistics: Offline Contextual Bandits and Decomposition Methods
… Regret (ESR), a novel loss function for value-based learning that addresses limitations of accuracy-based approaches in misspecified settings. Unlike standard methods that fail when reward models are poorly specified, ESR provably yields policies that asymptotically achieve optimal performance …
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Dynamical systems view of acceleration in first order optimization
Gradient based optimization algorithms are among the most fundamental algorithms in optimization and machine learning, yet they suffer from slow convergence. Consequently, accelerating gradient based methods have become an important recent topic of study. In this thesis, we focus on explaining and …
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Multi-variable optimization of pressurized oxy-coal combustion
Simultaneous multi-variable gradient-based optimization with multi-start is performed on a 300 MWe wet-recycling pressurized oxy-coal combustion process with carbon capture and sequestration. The model accounts for realistic component behavior such as heat losses, steam leaks, pressure drops, cycle …
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Investigation of Source Extension Methods, the Discrepancy Algorithm, and Noise Estimation to Overcome Cycle Skipping in Full Waveform Inversion
… The for- ward problem in FWI predicts the data based on a given model of the subsurface, while the inverse problem estimates subsurface parameters, such as sound velocity, by minimizing the difference between recorded data and data we predict from solving our mathematical model (the wave …
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Inverse-Problem Inspired Approaches in the Design of Solids for Frequency-Domain Dynamics
… in many engineered systems. Computational optimization methods can usefully guide the design of structures and solid systems to obtain layouts with desired dynamic behaviors, such as minimized or tailored vibration response, while accounting for additional constraints. Due to resonance …
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