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DAMASK_EICMD
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add the convergence criterion, the method is : 

1. calculate the L2 norm of the residual of all the stress points
2. store the relative errors of the L2 norm
3. if the standard deviation of the relative errors of last five fittings is less than 0.05, that it is considered that the relative errors is stabilized, so the fitting is finished.
This commit is contained in:
Haiming Zhang 2015-04-08 17:48:26 +00:00
parent b77768fd4d
commit 976647b9e4
1 changed files with 10 additions and 3 deletions
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13
processing/misc/yieldSurface.py
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@ -1098,7 +1098,7 @@ class Criterion(object):
print('fitting to the %s criterion'%name) print('fitting to the %s criterion'%name)
def fit(self,stress): def fit(self,stress):
global fitResults; fitErrors global fitResults; fitErrors; fitResidual
if options.exponent > 0.0: nExponent = nExpo if options.exponent > 0.0: nExponent = nExpo
else: nExponent = 0 else: nExponent = 0
nameCriterion = self.name.lower() nameCriterion = self.name.lower()
@ -1120,6 +1120,9 @@ class Criterion(object):
bounds=bounds, Dfun=criteria.jac, full_output=True) bounds=bounds, Dfun=criteria.jac, full_output=True)
if ierr not in [1, 2, 3, 4]: if ierr not in [1, 2, 3, 4]:
raise RuntimeError("Optimal parameters not found: " + errmsg) raise RuntimeError("Optimal parameters not found: " + errmsg)
else:
residual = criteria.fun(popt, ydata, stress)
fitResidual.append(np.linalg.norm(residual)/len(residual))
if (len(ydata) > len(initialguess)) and pcov is not None: if (len(ydata) > len(initialguess)) and pcov is not None:
s_sq = (criteria.fun(popt, *(ydata,stress))**2).sum()/(len(ydata)-len(initialguess)) s_sq = (criteria.fun(popt, *(ydata,stress))**2).sum()/(len(ydata)-len(initialguess))
pcov = pcov * s_sq pcov = pcov * s_sq
@ -1257,8 +1260,12 @@ def loadcaseNo():
return N_simulations return N_simulations
def converged(): # is there any meaningfull stopping criterion? def converged(): # is there any meaningfull stopping criterion?
global N_simulations global N_simulations; fitResidual
if N_simulations < options.max: if N_simulations < options.max:
if len(fitResidual) > 5:
residualList = np.array(fitResidual[len(fitResidual)-5:])
if np.std(residualList)/np.max(residualList) < 0.05: return True
return False return False
else: else:
return True return True
@ -1364,7 +1371,7 @@ myLoad = Loadcase(options.load[0],options.load[1],options.load[2],
stressAll= [np.zeros(0,'d').reshape(0,0) for i in xrange(int(options.yieldValue[2]))] stressAll= [np.zeros(0,'d').reshape(0,0) for i in xrange(int(options.yieldValue[2]))]
strainAll= [np.zeros(0,'d').reshape(0,0) for i in xrange(int(options.yieldValue[2]))] strainAll= [np.zeros(0,'d').reshape(0,0) for i in xrange(int(options.yieldValue[2]))]
fitResults = []; fitErrors = []; threads=[] fitResults = []; fitErrors = []; fitResidual = []; threads=[]
myFit = Criterion(options.exponent,options.eqStress, options.dimension, options.criterion) myFit = Criterion(options.exponent,options.eqStress, options.dimension, options.criterion)
for i in range(options.threads): for i in range(options.threads):
threads.append(myThread(i)) threads.append(myThread(i))