Overview of auto_diff module¶
auto_diff module provides Fortran derived types that support automatic
differentiation via operator overloading. Users will not generally
need to interact with this module, but it can be used within
run_star_extras to make derivatives easier to calculate (e.g. in the
implicit hooks like
Usage is by writing
use auto_diff at the top of a module or routine.
This imports types such as
auto_diff_real_4var_order1, which supports first-order derivatives
with respect to up to four independent variables.
A variable of this type could be declared via:
type(auto_diff_real_4var_order1) :: x
This variable then holds five fields:
x%val stores the value of
x%d1val1 stores the derivative of x with respect to the first independent
x%d1val2 is the same for the second independent variable, and so on.
d1val_ fields are initialized to zero when the variable is first set.
Once an auto_diff variable it initialized, all mathematical operations can be performed
as they would be on a
real(dp) variable. auto_diff variables also interoperate with
So for instance in the following
f%d1val1 stores df/dx and
f%d1val2 stores df/dy.:
x = 3d0 x%d1val1 = 1d0 y = 2d0 y%d1val2 = 1d0 f = exp(x) * y + x + 4
Similar types are included supporting higher-order and mixed-partial
derivatives. These derivatives are accessed via e.g.
(\(\partial^2 f/\partial x^2\)),
d1val1_d2val2 (\(\partial^3 f/\partial x \partial y^2\)).
An additional special type
auto_diff_real_star_order1 provides support for first-order derivatives accessed using arrays.
This type contains a value (
x%val) and an array of first partial derivatives with respect to 27 independent variables (
This type is meant to make it easy to write equations and then, after the fact, change the basis of independent variables or re-index them.
The number 27 is chosen to provide as many independent variables as the
MESA/star solver uses,
as this type is meant for use in writing the equations of stellar evolution.