algo= limitStateVariable=class=RandomVector implementation=class=CompositeRandomVector function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[x1,x2,x3,x4,y1,y2] evaluationImplementation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x1,x2,x3,x4] outputVariablesNames=[y1,y2] formulas=[(x1*x1+x2^3*x1)/(2*x3*x3+x4^4+1),cos(x2*x2+x4)/(x1*x1+1+x3^4)] gradientImplementation=class=SymbolicGradient name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x1,x2,x3,x4] outputVariablesNames=[y1,y2] formulas=[(x1*x1+x2^3*x1)/(2*x3*x3+x4^4+1),cos(x2*x2+x4)/(x1*x1+1+x3^4)] hessianImplementation=class=SymbolicHessian name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x1,x2,x3,x4] outputVariablesNames=[y1,y2] formulas=[(x1*x1+x2^3*x1)/(2*x3*x3+x4^4+1),cos(x2*x2+x4)/(x1*x1+1+x3^4)] antecedent=class=UsualRandomVector distribution=class=Normal name=Normal dimension=4 mean=class=Point name=Unnamed dimension=4 values=[0.2,0.2,0.2,0.2] sigma=class=Point name=Unnamed dimension=4 values=[0.1,0.2,0.3,0.4] correlationMatrix=class=CorrelationMatrix dimension=4 implementation=class=MatrixImplementation name=Unnamed rows=4 columns=4 values=[1,0.25,0,0,0.25,1,0.25,0,0,0.25,1,0.25,0,0,0.25,1] meanInputVector=class=Point name=Unnamed dimension=0 values=[] hessianAtMean=class=SymmetricTensor implementation=class=TensorImplementation name=Unnamed rows=0 columns=0 sheets=0 values=[] gradientAtMean=class=Matrix implementation=class=MatrixImplementation name=Unnamed rows=0 columns=0 values=[] valueAtMean=class=Point name=Unnamed dimension=0 values=[] meanFirstOrder=class=Point name=Unnamed dimension=0 values=[] meanSecondOrder=class=Point name=Unnamed dimension=0 values=[] covariance=class=CovarianceMatrix dimension=0 implementation=class=MatrixImplementation name=Unnamed rows=0 columns=0 values=[] inputCovariance=class=CovarianceMatrix dimension=0 implementation=class=MatrixImplementation name=Unnamed rows=0 columns=0 values=[] importanceFactors=class=PointWithDescription name=Unnamed dimension=0 description=[] values=[]
First order mean= class=Point name=Unnamed dimension=2 values=[0.0384615,0.932544]
Second order mean= class=Point name=Unnamed dimension=2 values=[0.0466369,0.819419]
Covariance= class=CovarianceMatrix dimension=2 implementation=class=MatrixImplementation name=Unnamed rows=2 columns=2 values=[0.00158239,-0.00130376,-0.00130376,0.01082]
Value at mean= class=Point name=Unnamed dimension=2 values=[0.0384615,0.932544]
Gradient at mean= class=Matrix implementation=class=MatrixImplementation name=Unnamed rows=4 columns=2 values=[0.377219,0.0221893,-0.0284479,-0.00113792,-0.35812,-0.0912837,-0.0286496,-0.228209]
Hessian at mean= class=SymmetricTensor implementation=class=TensorImplementation name=Unnamed rows=4 columns=4 sheets=2 values=[1.84911,0.110947,-0.279008,-0.0111603,0,0.221893,-0.0164122,-0.00065649,0,0,-0.100157,0.00168331,0,0,0,-0.0170014,-1.51555,0.0350552,0.0220043,0.0876379,0,-0.605625,0.00280441,-0.373018,0,0,-0.427983,0.00701103,0,0,0,-0.932544]
Importance factors= class=PointWithDescription name=Unnamed dimension=4 description=[X0,X1,X2,X3] values=[0.925686,0.0329106,0.0406588,0.000744644]
