曲面拟合调用库函数问题

天马行空 · 发表于 2024-6-13 11:11:51

在IVF2013中调用MSL6.0函数库中的B样条拟合曲面函数，Surface_Fitting函数，编译的时候出现如下报错：
1>------ 已启动生成:  项目: SurfaceFitting, 配置: Debug Win32 ------
1>Compiling with Intel(R) Visual Fortran Compiler XE 14.0.1.139 [IA-32]...
1>SurfaceFitting.f90
1>fortcom: Fatal: There has been an internal compiler error (C0000005).
1>compilation aborted for E:\IVF_File\SurfaceFitting\SurfaceFitting\SurfaceFitting.f90 (code 1)
1>
1>Build log written to  "file://E:\IVF_File\SurfaceFitting\SurfaceFitting\Debug\BuildLog.htm"
1>SurfaceFitting - 1 error(s), 0 warning(s)
========== 生成:  成功 0 个，失败 1 个，最新 0 个，跳过 0 个 ==========

改装IMSL7.0后，该问题同样存在，请问如何解决呢？大家有没有方法，跪谢了！

天马行空 · 发表于 2024-6-13 16:55:04

另外，怀疑是ISML版本的问题，我尝试了IMSL6.0，IMSL7.0两个版本，都是同样的问题，只要注释掉代码： coeff = surface_fitting(spline_data, knotsx, knotsy) ，编译就可以通过，不知道是啥原因？路径和环境变量设置都是正确的；

天马行空 · 发表于 2024-6-13 16:53:05

谢谢楼上的高手：代码粘贴如下，案例就是IMSL安装目录下的自带的算例，请您查阅，不胜感激！

[Fortran] 纯文本查看 复制代码

program Test_SurfaceFitting
      INCLUDE 'link_fnl_shared.h'
      use numerical_libraries
  
      USE surface_fitting_int
      USE rand_int
      USE norm_int

      implicit none

! This is Example 1 for SURFACE_FITTING, tensor product
! B-splines approximation.  Use the function
! exp(-x**2-y**2) on the square (0, 2) x (0, 2) for samples.
! The spline order is "nord" and the number of cells is
! "(ngrid-1)**2".  There are "ndata" data values in the square.

      integer :: i
      integer, parameter :: ngrid=9, nord=4, ndegree=nord-1, &
      nbkpt=ngrid+2*ndegree, ndata = 2000, nvalues=100
      real(kind(1d0)), parameter :: zero=0d0, one=1d0, two=2d0
      real(kind(1d0)), parameter :: TOLERANCE=1d-3
      real(kind(1d0)), target :: spline_data (4, ndata), bkpt(nbkpt), &
             & coeff(ngrid+ndegree-1,ngrid+ndegree-1), delta, sizev, &
             & x(nvalues), y(nvalues), values(nvalues, nvalues)
      real(kind(1d0)), pointer :: pointer_bkpt(:)
      type (d_spline_knots) knotsx, knotsy

! Set Random Number generator seed
                                             
      real(kind(1d0)),parameter::zero_par=0.d0
      real(kind(1d0))::dummy_par(0)
      integer iseed_par,lev_par
      type(d_options)::iopti_par(2)

      iseed_par = 53976279
      iopti_par(1)=d_options(d_rand_gen_generator_seed,zero_par)
      iopti_par(2)=d_options(iseed_par,zero_par)

      call rand_gen(dummy_par,iopt=iopti_par)

! Generate random (x,y) pairs and evaluate the
! example exponential function at these values.   
      spline_data(1:2,:)=two*rand(spline_data(1:2,:))
      spline_data(3,:)=exp(-sum(spline_data(1:2,:)**2,dim=1))
      spline_data(4,:)=one

! Define the knots for the tensor product data fitting problem.
      delta = two/(ngrid-1)
      bkpt(1:ndegree) = zero
      bkpt(nbkpt-ndegree+1:nbkpt) =  two
      bkpt(nord:nbkpt-ndegree)=(/(i*delta,i=0,ngrid-1)/)

! Assign the degree of the polynomial and the knots.
      knotsx%spline_degree=ndegree
      knotsx%d_knots=>bkpt
      knotsy%spline_degree=knotsx%spline_degree
      knotsy%d_knots=>knotsx%d_knots

! Fit the data and obtain the coefficients.
      coeff = surface_fitting(spline_data, knotsx, knotsy)

! Evaluate the residual = spline - function
! at a grid of points inside the square.
      delta=two/(nvalues+1)
      x=(/(i*delta,i=1,nvalues)/); y=x

      values=exp(-spread(x**2,1,nvalues)-spread(y**2,2,nvalues))
      values=surface_values((/0,0/), x, y, knotsx, knotsy, coeff)-values
            
! Compute the R.M.S. error:
      sizev=norm(pack(values, (values == values)))/nvalues

      if (sizev <= TOLERANCE) then
         write (*,*) 'SURFACE_FITTING_EX1 Passed on pcdsms.'
      else
         write(*,*) 'SURFACE_FITTING_EX1 ********** Failed on pcdsms **********'
      end if
end program