From: Wolfgang Bangerth Date: Thu, 16 May 2019 15:32:33 +0000 (-0600) Subject: Minor updates to the AD page. X-Git-Tag: v9.1.0-rc3~9^2 X-Git-Url: https://gitweb.dealii.org/cgi-bin/gitweb.cgi?a=commitdiff_plain;h=refs%2Fpull%2F8232%2Fhead;p=dealii.git Minor updates to the AD page. Just trying to educate myself about this page. --- diff --git a/doc/doxygen/headers/automatic_and_symbolic_differentiation.h b/doc/doxygen/headers/automatic_and_symbolic_differentiation.h index cf70525179..6802c014c5 100644 --- a/doc/doxygen/headers/automatic_and_symbolic_differentiation.h +++ b/doc/doxygen/headers/automatic_and_symbolic_differentiation.h @@ -20,8 +20,38 @@ * to automatic and symbolic differentiation. * * Below we provide a very brief introduction as to what automatic and symbolic differentiation are, - * what variations of these computational / numerical schemes exist, and how they are integrated - * within deal.II's framework. + * what variations of these computational/numerical schemes exist, and how they are integrated + * within deal.II's framework. The purpose of all of these schemes is to automatically compute + * the derivative of functions, or approximations of it, in cases where one does not want to + * compute them by hand. Common examples are situations in the finite element context is where + * one wants to solve a nonlinear problem that is given by requiring that some residual + * $F(u,\nabla u)=0$ where $F$ is a complicated function that needs to be differentiated to + * apply Newton's method; and situations where one is given a parameter dependent problem + * ${\cal A}(q,u,\nabla u) = f$ and wants to form derivatives with regards to the parameters $q$, for example + * to optimize an output functional with regards to $q$, or for a sensitivity analysis with + * regards to $q$. One should think of $q$ as design parameters: say, the width + * or shape of a wing, the stiffness coefficients of a material chosen to + * build an object, the power sent to a device, the chemical composition of the + * gases sent to a burner. In all of these cases, one should think of $F$ and $\cal A$ as complicated + * and cumbersome to differentiate -- at least when doing it by hand. A relatively simple case of + * a nonlinear problem that already highlights the tedium of computing derivatives by hand is shown in + * step-15. However, in reality, one might, for example, + * think about problems such as chemically reactive flows where the fluid equations have coefficients + * such as the density and viscosity that depend strongly and nonlinearly on the chemical composition, + * temperature, and pressure of the fluid at each point; and where the chemical species react with + * each other based on reaction coefficients that also depend nonlinearly and in complicated + * ways on the chemical composition, temperature, and pressure. In many cases, the exact formulas + * for all of these coefficients can take several lines to write out, may include exponentials + * and (harmonic or geometric) averages of several nonlinear terms, and/or may contain table + * lookup of and interpolation between data points. Just getting these terms right is difficult + * enough; computing derivatives of these terms is impractical in most applications and, in + * reality, impossible to get right. Higher derivatives are even more impossible to do + * without computer aid. Automatic or symbolic differentiation is a way out of this: + * One only has to implement the function that computes these coefficients in terms + * of their inputs only once, and gets the (correct!) derivatives without + * further coding effort (though at a non-negligible computational cost either at run time, compile + * time, or both). + * * * @section auto_diff_1 Automatic differentiation * @@ -33,10 +63,12 @@ * significant. When used correctly the derivatives of often complicated functions can be computed * to a very high accuracy. Although the exact accuracy achievable by these frameworks largely * depends on their underlying mathematical formulation, some implementations compute with a precision - * on the order of machine accuracy. Note that this is different to classical numerical differentiation, + * on the order of machine accuracy. Note that this is different to classical numerical + * differentiation (using, for example, a finite difference approximation of a function by + * evaluating it at different points), * which has an accuracy that depends on both the perturbation size as well as the chosen - * finite-difference scheme (and is measurably larger than well-formulated automatic differentiation - * approaches). + * finite-difference scheme; the error of these methods is measurably larger than + * well-formulated automatic differentiation approaches. * * Three practical examples of auto-differentiation use within a finite-element context * would then be @@ -71,7 +103,7 @@ * Implementations of specialized frameworks based on operator overloading typically fall into * one of three categories. In each, some customized data classes representing the floating point value * of an evaluated function and its derivative(s) by - * -# exploiting dual / complex-step / hyper-dual formulations (occasionally + * -# exploiting dual/complex-step/hyper-dual formulations (occasionally * called tapeless methods), * -# those utilizing taping strategies, and * -# those using compile-time optimization through expression templates. @@ -91,9 +123,9 @@ * numerical perturbation. The dual number approach thus produces exact first derivatives, while the * complex-step approximation does not. The standard implementation of the dual numbers, however, cannot yield * exact values for second derivatives. Hyper-dual numbers take a different view of this idea, with numbers - * begin represented in a form similar to quaternions (i.e. carrying additional non-real components) and the + * being represented in a form similar to quaternions (i.e. carrying additional non-real components) and the * derivatives being computed from a high-order truncation of the Taylor series all four components. The outcome - * that, with the appropriate implementation, both first and second derivatives can be computed exactly. + * is that, with the appropriate implementation, both first and second derivatives can be computed exactly. * -# With taped approaches, a specified subregion of code is selected as one for which all * operations executed with active (marked) input variables are tracked and recorded in a data structure * referred to as a tape. At the end of the taped region, the recorded function(s) may be reevaluated @@ -113,7 +145,9 @@ * The outermost leaves on the tree represent the independent variables or constants, and are transformed by unary * operators and connected by binary operators (in the most simple case). Therefore, the operations performed on * the function inputs is known at compile time, and with that the associated derivative operation can also be defined - * at the same time. The compiled output type returned by this operator need not be generic, but can rather be + * at the same time using the well-known rules of computing the derivative of an operation (such as + * the associativity of derivatives under addition and subtraction, the product rule, and the chain + * rule). The compiled output type returned by this operator need not be generic, but can rather be * specialized based on the specific inputs (possibly carrying a differential history) given to that specific * operator on the vertex of the DAG. In this way, a compile-time optimized set of instructions can be generated * for the very specialized individual operations used to evaluate each intermediate result of the dependent @@ -166,6 +200,8 @@ * * With the aid of the diagram below (it and some of the listed details courtesy of this * Wikipedia article), + * let us think about the represention of the calculation of the function + * $f (\mathbf{x}) = \sin (x_{1}) + x_{1} x_{2}$ and its derivatives: * *
*
@@ -190,8 +226,7 @@ *
*
* - * representing the calculation of the function $f (\mathbf{x}) = x_{1} \times x_{2} + \sin (x_{1})$, - * we will briefly describe what forward and reverse auto-differentiation are. + * Specifically, we will briefly describe what forward and reverse auto-differentiation are. * Note that in the diagram, along the edges of the graph in text are the directional * derivative of function $w$ with respect to the $i$-th variable, represented by * the notation $\dot{w} = \dfrac{d w}{d x_{i}}$.