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.
Results
Showing 1 to 20 of 72 for “"Automatic Differentiation"”.
-
Automatic Differentiation of Parallel Programs
We also describe how the tools and techniques developed to enable AD of parallel programs were applied to a variety of applications, ranging from a simple test problem to a parallel molecular dynamics application. The results confirm the need for and efficacy of several techniques. They also verify …
-
Higher-Order Automatic Differentiation and Its Applications
… in many fields of science and engineering, and automatic differentiation (AD) algorithms are at the heart of differentiable programming. Existing methods to achieve higher-order AD often suffer from one or more of the following problems: (1) exponential scaling with respect to order due to …
-
Advanced Concepts for Automatic Differentiation based on Operator Overloading
Mit Hilfe der Technik des Automatischen Differenzierens (AD) lassen sich für Funktionen, die als Programmquellcode gegeben sind, Ableitungsinformationen rechentechnisch effizient und mit geringem Aufwand für den Nutzer bereitstellen. Eine Variante der Implementierung von AD basiert auf der …
-
Optimal control-based mission simulation using accelerated automatic differentiation
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms
-
Optimization of neural network feedback control systems using automatic differentiation
… an unconstrained optimization routine. By using automatic differentiation on the code that evaluates the cost function, the gradient of the cost with respect to the weights is obtained for the gradient search phase of the optimization process. Automatic differentiation is more efficient than …
-
Automatic differentiation of the CapeML high-level language for process engineering
… called ADiCape has been developed to provide automatically generated CapeML code for derivative computations. These derivatives are determined by means of Automatic Differentiation (AD) techniques. The transformation rules of ADiCape are implemented in eXtensible Style-sheet Language …
-
Algorithmic differentiation of Java programs
… widely used to obtain computer derivatives is Automatic Differentiation (AD). The fact that to date no usable AD tool implementation exists for Java motivated the development of an AD tool for the Java language. Because of the portability and simplicity in terms of standardization provided by …
-
Efficient computation of derivatives for optimal experimental design
… aspects. A novel framework for automatically combining simulation- and optimization software is introduced. This framework, called EFCOSS, treats the simulation, the optimization algorithm, and the mathematical objective function as separated components which can be exchanged …
-
An Integrated Tool For High Speed Circuits with Substrate Coupling.
… implemented in object-oriented fashion and uses automatic differentiation. The same model can be used for DC, transient and harmonic balance analysis.
-
Discrete Adjoints: Theoretical Analysis, Efficient Computation, and Applications
The technique of automatic differentiation provides directional derivatives and discrete adjoints with working accuracy. A complete complexity analysis of the basic modes of automatic differentiation is available. Therefore, the research activities are focused now on different aspects of the …
-
Breaking things so you don’t have to: risk assessment and failure prediction for cyber-physical AI
… systems by using program analysis tools such as automatic differentiation and probabilistic programming to automatically construct mathematical models of the system under test. In particular, I make the following contributions. First, I use automatic differentiation to develop a flexible, …
-
Sensitivity analysis for nonsmooth dynamic systems
… for nonsmooth problems. A vector forward mode of automatic differentiation is developed and implemented to evaluate lexicographic derivatives for finite compositions of simple lexicographically smooth functions, including the standard arithmetic operations, trigonometric functions, exp / log, …
-
Language Evolution for Parallel and Scientific Computing
… programming framework built on top of a general automatic differentiation engine operating on compiler level. The automatic differentiation framework outperforms state-of-the-art, is capable of synthesizing gradient functions from GPU kernels, and can differentiate a wide variety of parallel …
-
In Tension: Computational exploration of the design space of tensile network structures
… a specific design. Recent work has shown that automatic differentiation software packages can be used to efficiently design funicular structures (that is, those that work in pure tension or pure compression) with additional designer driven objectives, but these techniques remain largely …
-
Differentiable visual computing
… processing language Halide with reverse-mode automatic differentiation, and the ability to automatically optimize the gradient computations. This enables automatic generation of the gradients of arbitrary Halide programs, at high performance, with little programmer effort.
-
Sensitivity Calculations For Conservation Laws With Application To Discontinuous Fluid Flows
… calculated using finite difference quotients, automatic differentiation and the sensitivity equation method for a variety of numerical methods. Explanations for the inaccuracies arising in the numerical approximations and implications these inaccuracies have on different applications are …
-
Traversing Rugged Domains: Explorations in Non-convex Optimization Theory and Software
… classes. Its modular architecture integrates automatic differentiation with an extensible plugin system. The framework’s capabilities are demonstrated through a GPU-accelerated hybrid method combining Particle Swarm Optimization with L-BFGS, and an augmented Lagrangian approach with stochastic …
-
DEVELOPMENT AND APPLICATIONS OF MACHINE/DEEP LEARNING TECHNIQUES IN FLUID DYNAMICS
… compared with the tanh-activated ones. Last, the automatic differentiation-based PINNs are improved by the finite difference scheme, and the proposed FD-PINNs are validated using incompressible isothermal and thermal flows.
Page 1 of 4