68,624 research outputs found
A Hitchhiker's Guide to Automatic Differentiation
This article provides an overview of some of the mathematical prin-
ciples of Automatic Differentiation (AD). In particular, we summarise
different descriptions of the Forward Mode of AD, like the matrix-vector
product based approach, the idea of lifting functions to the algebra of
dual numbers, the method of Taylor series expansion on dual numbers
and the application of the push-forward operator, and explain why they
all reduce to the same actual chain of computations. We further give a
short mathematical description of some methods of higher-order Forward
AD and, at the end of this paper, brie
y describe the Reverse Mode of
Automatic Differentiation
A Hitchhiker's Guide to Automatic Differentiation
This article provides an overview of some of the mathematical prin-
ciples of Automatic Differentiation (AD). In particular, we summarise
different descriptions of the Forward Mode of AD, like the matrix-vector
product based approach, the idea of lifting functions to the algebra of
dual numbers, the method of Taylor series expansion on dual numbers
and the application of the push-forward operator, and explain why they
all reduce to the same actual chain of computations. We further give a
short mathematical description of some methods of higher-order Forward
AD and, at the end of this paper, brie
y describe the Reverse Mode of
Automatic Differentiation
Fluctuation-Response Relations for Multi-Time Correlations
We show that time-correlation functions of arbitrary order for any random
variable in a statistical dynamical system can be calculated as higher-order
response functions of the mean history of the variable. The response is to a
``control term'' added as a modification to the master equation for statistical
distributions. The proof of the relations is based upon a variational
characterization of the generating functional of the time-correlations. The
same fluctuation-response relations are preserved within moment-closures for
the statistical dynamical system, when these are constructed via the
variational Rayleigh-Ritz procedure. For the 2-time correlations of the
moment-variables themselves, the fluctuation-response relation is equivalent to
an ``Onsager regression hypothesis'' for the small fluctuations. For
correlations of higher-order, there is a new effect in addition to such linear
propagation of fluctuations present instantaneously: the dynamical generation
of correlations by nonlinear interaction of fluctuations. In general, we
discuss some physical and mathematical aspects of the {\it Ans\"{a}tze}
required for an accurate calculation of the time correlations. We also comment
briefly upon the computational use of these relations, which is well-suited for
automatic differentiation tools. An example will be given of a simple closure
for turbulent energy decay, which illustrates the numerical application of the
relations.Comment: 28 pages, 1 figure, submitted to Phys. Rev.
Study of automatic differentiation in topology optimization
This bachelor final thesis presents a study on the integration of automatic differentiation functions into basic topology optimisation algorithms to improve not only computation speed, but also efficiency and accuracy. The main goal is to develop a fully functional automatic differentiation script capable of deriving topological expression, linear or non linear ones, aiming to find the optimal distribution. Moreover, the objectives of this research are to explore the application of automatic differentiation in fields related to topology optimisation, analyse the benefits of applying this methods and its disadvantages and review its computational efficiency. The thesis begins by introducing the fundamentals of automatic differentiation. Beforehand, during the initial stages of the project extensive practice of Matlab programming and object oriented programming was taught but it is not included in this report. A literature review is conducted to examine existing studies and approaches that utilize AD techniques. Furthermore, we dig into different AD methods, including forward mode and reverse mode, highlighting its approach with Matlab language and their advantages and limitations. Additionally, specific topologic optimisation tools and software packages commonly used are reviewed but are not included in this report. The report continues by presenting the different discretisation cases in finite element method and developing an AD based case to solve the discretisation of different 2 dimension problems. Deriving its shape functions and testing AD for a future, more sophisticated, topological problem. To achieve the main objective of performing, at least, one topology case using automatic differentiation, an AD based algorithm using different iterative methods is developed and implemented. The algorithm is tested with basic shapes and problems to be improved and more efficient, including all the possible casuistry of a mathematical expression. Ending with the research, we test different topological cases using different iterative methods. One of these methods, newton iteration method, will need an improvement to higher order gradients of the automatic differentiation algorithm. With this improvement we will test and compare both methods for several cases to conclude about the efficiency, accuracy and computing time of both iterative methods and automatic differentiation algorithm applied to topological problems. The results of the study demonstrate that automatic differentiation significantly enhances the efficiency and accuracy of topological optimisation for a certain type of problems. For these cases, AD exhibits faster convergence, improved accuracy in gradient computation, and reduced computational time. Moreover, the AD-based approach proves to be robust and applicable to not only deriving structural function, but also different problem domains, highlighting its versatility and practicality. Overall, The research highlights the potential of AD in many fields
Automatic differentiation in machine learning: a survey
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in
machine learning. Automatic differentiation (AD), also called algorithmic
differentiation or simply "autodiff", is a family of techniques similar to but
more general than backpropagation for efficiently and accurately evaluating
derivatives of numeric functions expressed as computer programs. AD is a small
but established field with applications in areas including computational fluid
dynamics, atmospheric sciences, and engineering design optimization. Until very
recently, the fields of machine learning and AD have largely been unaware of
each other and, in some cases, have independently discovered each other's
results. Despite its relevance, general-purpose AD has been missing from the
machine learning toolbox, a situation slowly changing with its ongoing adoption
under the names "dynamic computational graphs" and "differentiable
programming". We survey the intersection of AD and machine learning, cover
applications where AD has direct relevance, and address the main implementation
techniques. By precisely defining the main differentiation techniques and their
interrelationships, we aim to bring clarity to the usage of the terms
"autodiff", "automatic differentiation", and "symbolic differentiation" as
these are encountered more and more in machine learning settings.Comment: 43 pages, 5 figure
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