sensitivityMatrix Namespace Reference

Variables
bool	do_simplifications = False
	Symbolic generation settings. More...
string	mode = "c"
int	nnodes = 3
int	dim = 2
bool	impose_partion_of_unity = False
	N
	DNDe
	x = DefineMatrix('x', nnodes, dim)
	p = DefineVector('p', nnodes)
	J = sympy.simplify(x.transpose()*DNDe)
	JInverse = sympy.simplify((J)**(-1))
	DNDx = sympy.simplify(DNDe*JInverse)
	det_J = sympy.simplify(0.5*J.det())
	A = sympy.simplify(DNDx*DNDx.transpose())
	K = sympy.simplify(A*det_J)
	B = sympy.simplify(-K * p)
	DB_Dx = DefineMatrix('DB_Dx',dim*nnodes,nnodes)
	dB_Dx_out = OutputMatrix_CollectingFactors(DB_Dx, "rOutput", mode)

Variable Documentation

A

sensitivityMatrix.A = sympy.simplify(DNDx*DNDx.transpose())

B

sensitivityMatrix.B = sympy.simplify(-K * p)

DB_Dx

sensitivityMatrix.DB_Dx = DefineMatrix('DB_Dx',dim*nnodes,nnodes)

dB_Dx_out

sensitivityMatrix.dB_Dx_out = OutputMatrix_CollectingFactors(DB_Dx, "rOutput", mode)

det_J

sensitivityMatrix.det_J = sympy.simplify(0.5*J.det())

dim

int sensitivityMatrix.dim = 2

DNDe

sensitivityMatrix.DNDe

DNDx

sensitivityMatrix.DNDx = sympy.simplify(DNDe*JInverse)

do_simplifications

bool sensitivityMatrix.do_simplifications = False

Symbolic generation settings.

impose_partion_of_unity

bool sensitivityMatrix.impose_partion_of_unity = False

J

sensitivityMatrix.J = sympy.simplify(x.transpose()*DNDe)

JInverse

sensitivityMatrix.JInverse = sympy.simplify((J)**(-1))

K

sensitivityMatrix.K = sympy.simplify(A*det_J)

mode

string sensitivityMatrix.mode = "c"

N

sensitivityMatrix.N

nnodes

int sensitivityMatrix.nnodes = 3

p

sensitivityMatrix.p = DefineVector('p', nnodes)

x

sensitivityMatrix.x = DefineMatrix('x', nnodes, dim)