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DAMASK_EICMD
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use the Box-Muller transform instead of random sampling 

still needs testing.
This commit is contained in:
Martin Diehl 2021-12-16 18:22:42 +01:00
parent 430b947c7e
commit f40d731fe1
2 changed files with 31 additions and 40 deletions
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47
src/math.f90
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@ -961,39 +961,36 @@ pure function math_3333toVoigt66(m3333)
end function math_3333toVoigt66 end function math_3333toVoigt66
!-------------------------------------------------------------------------------------------------- !--------------------------------------------------------------------------------------------------
!> @brief draw a random sample from Gauss variable !> @brief Draw a sample from a normal distribution.
!> @details https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform
!> https://masuday.github.io/fortran_tutorial/random.html
!-------------------------------------------------------------------------------------------------- !--------------------------------------------------------------------------------------------------
real(pReal) function math_sampleGaussVar(mu, sigma, width) impure elemental subroutine math_normal(x,mu,sigma)
real(pReal), intent(in) :: mu, & !< mean real(pReal), intent(out) :: x
sigma !< standard deviation real(pReal), intent(in), optional :: mu, sigma
real(pReal), intent(in), optional :: width !< cut off as multiples of standard deviation
real(pReal) :: sigma_, mu_
real(pReal), dimension(2) :: rnd
real(pReal), dimension(2) :: rnd ! random numbers
real(pReal) :: scatter, & ! normalized scatter around mean if (present(mu)) then
width_ mu_ = mu
if (abs(sigma) < tol_math_check) then
math_sampleGaussVar = mu
else else
if (present(width)) then mu_ = 0.0_pReal
width_ = width end if
else
width_ = 3.0_pReal ! use +-3*sigma as default scatter
endif
do if (present(sigma)) then
call random_number(rnd) sigma_ = sigma
scatter = width_ * (2.0_pReal * rnd(1) - 1.0_pReal) else
if (rnd(2) <= exp(-0.5_pReal * scatter**2)) exit ! test if scattered value is drawn sigma_ = 1.0_pReal
enddo end if
math_sampleGaussVar = scatter * sigma call random_number(rnd)
endif x = mu_ + sigma_ * sqrt(-2.0_pReal*log(1.0_pReal-rnd(1)))*cos(2.0_pReal*PI*(1.0_pReal - rnd(2)))
end function math_sampleGaussVar end subroutine math_normal
!-------------------------------------------------------------------------------------------------- !--------------------------------------------------------------------------------------------------

24
src/phase_mechanical_plastic_nonlocal.f90
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@ -1592,21 +1592,15 @@ subroutine stateInit(ini,phase,Nentries)
stt%rhoSglMobile(s,e) = densityBinning stt%rhoSglMobile(s,e) = densityBinning
end do end do
else ! homogeneous distribution with noise else ! homogeneous distribution with noise
do e = 1, Nentries do f = 1,size(ini%N_sl,1)
do f = 1,size(ini%N_sl,1) from = 1 + sum(ini%N_sl(1:f-1))
from = 1 + sum(ini%N_sl(1:f-1)) upto = sum(ini%N_sl(1:f))
upto = sum(ini%N_sl(1:f)) call math_normal(stt%rho_sgl_mob_edg_pos(from:upto,:),ini%rho_u_ed_pos_0(f),ini%sigma_rho_u)
do s = from,upto call math_normal(stt%rho_sgl_mob_edg_neg(from:upto,:),ini%rho_u_ed_neg_0(f),ini%sigma_rho_u)
noise = [math_sampleGaussVar(0.0_pReal, ini%sigma_rho_u), & call math_normal(stt%rho_sgl_mob_scr_pos(from:upto,:),ini%rho_u_sc_pos_0(f),ini%sigma_rho_u)
math_sampleGaussVar(0.0_pReal, ini%sigma_rho_u)] call math_normal(stt%rho_sgl_mob_scr_neg(from:upto,:),ini%rho_u_sc_neg_0(f),ini%sigma_rho_u)
stt%rho_sgl_mob_edg_pos(s,e) = ini%rho_u_ed_pos_0(f) + noise(1) stt%rho_dip_edg(from:upto,:) = ini%rho_d_ed_0(f)
stt%rho_sgl_mob_edg_neg(s,e) = ini%rho_u_ed_neg_0(f) + noise(1) stt%rho_dip_scr(from:upto,:) = ini%rho_d_sc_0(f)
stt%rho_sgl_mob_scr_pos(s,e) = ini%rho_u_sc_pos_0(f) + noise(2)
stt%rho_sgl_mob_scr_neg(s,e) = ini%rho_u_sc_neg_0(f) + noise(2)
end do
stt%rho_dip_edg(from:upto,e) = ini%rho_d_ed_0(f)
stt%rho_dip_scr(from:upto,e) = ini%rho_d_sc_0(f)
end do
end do end do
end if end if