diff --git a/kadmos/cmdows/schemas/0.9/cmdows.xml b/kadmos/cmdows/schemas/0.9/cmdows.xml
index cff469de9a1e9216da3b7acdf634ffb964d0fec7..84c9ba8e0a4ec9e278ba7c39b398fe674eb219cf 100644
--- a/kadmos/cmdows/schemas/0.9/cmdows.xml
+++ b/kadmos/cmdows/schemas/0.9/cmdows.xml
@@ -830,8 +830,7 @@
<couplingWeights>
<!--1 or more repetitions:-->
<couplingWeight uID="string">
- <!--You have a CHOICE of the next 2 items at this level-->
- <relatedParameterUID>string</relatedParameterUID>
+ <!--Optional:-->
<relatedInstanceUID>string</relatedInstanceUID>
<label>string</label>
<instanceID>3</instanceID>
@@ -953,24 +952,61 @@
<!--Optional:-->
<surrogateModels>
<!--1 or more repetitions:-->
- <surrogateModel>
- <relatedExecutableBlockUID>string</relatedExecutableBlockUID>
+ <surrogateModel uID="string">
+ <label>string</label>
+ <!--Optional:-->
+ <training>
+ <inputs>
+ <!--1 or more repetitions:-->
+ <input>
+ <parameterUID>anyType</parameterUID>
+ </input>
+ </inputs>
+ <!--1 or more repetitions:-->
+ <outputs>
+ <!--1 or more repetitions:-->
+ <output>
+ <parameterUID>string</parameterUID>
+ </output>
+ </outputs>
+ <settings>
+ <fittingMethod>string</fittingMethod>
+ </settings>
+ </training>
+ <!--Optional:-->
+ <prediction>
+ <inputs>
+ <!--1 or more repetitions:-->
+ <input>
+ <parameterUID>anyType</parameterUID>
+ </input>
+ </inputs>
+ <!--1 or more repetitions:-->
+ <outputs>
+ <!--1 or more repetitions:-->
+ <output>
+ <parameterUID>string</parameterUID>
+ </output>
+ </outputs>
+ </prediction>
</surrogateModel>
</surrogateModels>
<!--Optional:-->
- <boundaryDeterminators>
- <!--1 or more repetitions:-->
- <boundaryDeterminator>
- <relatedExecutableBlockUID>string</relatedExecutableBlockUID>
- </boundaryDeterminator>
- </boundaryDeterminators>
- <!--Optional:-->
- <surrogateModelBuilders>
+ <distributedSystemConvergers>
<!--1 or more repetitions:-->
- <surrogateModelBuilder>
- <relatedExecutableBlockUID>string</relatedExecutableBlockUID>
- </surrogateModelBuilder>
- </surrogateModelBuilders>
+ <distributedSystemConverger uID="string">
+ <!--Optional:-->
+ <label>string</label>
+ <!--Optional:-->
+ <settings>
+ <method>string</method>
+ <lastIterationsToConsider>3</lastIterationsToConsider>
+ <maximumIterations>3</maximumIterations>
+ <convergenceToleranceRelative>1.5E2</convergenceToleranceRelative>
+ <convergenceToleranceAbsolute>1.5E2</convergenceToleranceAbsolute>
+ </settings>
+ </distributedSystemConverger>
+ </distributedSystemConvergers>
<!--Optional:-->
<preCouplingAnalyses>
<!--1 or more repetitions:-->
diff --git a/kadmos/cmdows/schemas/0.9/cmdows.xsd b/kadmos/cmdows/schemas/0.9/cmdows.xsd
index 86a1e455d5f11539d5325fca92e33d1160d58c93..13ce0e229d5fd52bc00dba030c33abf1c9732f1f 100644
--- a/kadmos/cmdows/schemas/0.9/cmdows.xsd
+++ b/kadmos/cmdows/schemas/0.9/cmdows.xsd
@@ -1005,7 +1005,17 @@
<xs:element name="couplingWeights" minOccurs="0">
<xs:complexType>
<xs:sequence>
- <xs:element name="couplingWeight" type="simpleArchitectureElementsVariableType" maxOccurs="unbounded"/>
+ <xs:element name="couplingWeight" maxOccurs="unbounded">
+ <xs:complexType>
+ <xs:sequence>
+ <xs:element name="relatedInstanceUID" type="xs:string" minOccurs="0"/>
+ <xs:element name="label" type="xs:string"/>
+ <xs:element name="instanceID" type="xs:int"/>
+ <xs:element name="schemaPath" type="xs:string" minOccurs="0"/>
+ </xs:sequence>
+ <xs:attribute name="uID" type="xs:string"/>
+ </xs:complexType>
+ </xs:element>
</xs:sequence>
</xs:complexType>
</xs:element>
@@ -1149,21 +1159,70 @@
<xs:element name="surrogateModels" minOccurs="0">
<xs:complexType>
<xs:sequence>
- <xs:element name="surrogateModel" type="simpleArchitectureElementsRelatedBlockType" maxOccurs="unbounded"/>
- </xs:sequence>
- </xs:complexType>
- </xs:element>
- <xs:element name="boundaryDeterminators" minOccurs="0">
- <xs:complexType>
- <xs:sequence>
- <xs:element name="boundaryDeterminator" type="simpleArchitectureElementsRelatedBlockType" maxOccurs="unbounded"/>
+ <xs:element name="surrogateModel" maxOccurs="unbounded">
+ <xs:complexType>
+ <xs:complexContent>
+ <xs:extension base="simplerArchitectureElementsBlockType">
+ <xs:sequence>
+ <xs:element name="training" minOccurs="0">
+ <xs:complexType>
+ <xs:sequence>
+ <xs:element name="inputs" maxOccurs="1">
+ <xs:complexType>
+ <xs:sequence>
+ <xs:element name="input" maxOccurs="unbounded">
+ <xs:complexType>
+ <xs:sequence>
+ <xs:element name="parameterUID" maxOccurs="1"/>
+ </xs:sequence>
+ </xs:complexType>
+ </xs:element>
+ </xs:sequence>
+ </xs:complexType>
+ </xs:element>
+ <xs:element name="outputs" type="outputsType" maxOccurs="unbounded"/>
+ <xs:element name="settings">
+ <xs:complexType>
+ <xs:sequence>
+ <xs:element name="fittingMethod" type="xs:string"/>
+ </xs:sequence>
+ </xs:complexType>
+ </xs:element>
+ </xs:sequence>
+ </xs:complexType>
+ </xs:element>
+ <xs:element name="prediction" minOccurs="0">
+ <xs:complexType>
+ <xs:sequence>
+ <xs:element name="inputs" maxOccurs="1">
+ <xs:complexType>
+ <xs:sequence>
+ <xs:element name="input" maxOccurs="unbounded">
+ <xs:complexType>
+ <xs:sequence>
+ <xs:element name="parameterUID" maxOccurs="1"/>
+ </xs:sequence>
+ </xs:complexType>
+ </xs:element>
+ </xs:sequence>
+ </xs:complexType>
+ </xs:element>
+ <xs:element name="outputs" type="outputsType" maxOccurs="unbounded"/>
+ </xs:sequence>
+ </xs:complexType>
+ </xs:element>
+ </xs:sequence>
+ </xs:extension>
+ </xs:complexContent>
+ </xs:complexType>
+ </xs:element>
</xs:sequence>
</xs:complexType>
</xs:element>
- <xs:element name="surrogateModelBuilders" minOccurs="0">
+ <xs:element name="distributedSystemConvergers" minOccurs="0">
<xs:complexType>
<xs:sequence>
- <xs:element name="surrogateModelBuilder" type="simpleArchitectureElementsRelatedBlockType" maxOccurs="unbounded"/>
+ <xs:element name="distributedSystemConverger" type="distributedSystemConvergerType" maxOccurs="unbounded"/>
</xs:sequence>
</xs:complexType>
</xs:element>
@@ -1337,6 +1396,13 @@
</xs:sequence>
<xs:attribute name="uID" type="xs:string"/>
</xs:complexType>
+ <xs:complexType name="distributedSystemConvergerType">
+ <xs:sequence>
+ <xs:element name="label" type="xs:string" minOccurs="0"/>
+ <xs:element name="settings" type="solverSettingsType" minOccurs="0"/>
+ </xs:sequence>
+ <xs:attribute name="uID" type="xs:string"/>
+ </xs:complexType>
<xs:complexType name="convergerType">
<xs:sequence>
<xs:element name="label" type="xs:string" minOccurs="0"/>
diff --git a/kadmos/graph/graph_data.py b/kadmos/graph/graph_data.py
index 4e2bca7798a8cecbe9a5fc36518e39662f67575d..3411fd19999cb804395ac1e49e566ed05387ef67 100644
--- a/kadmos/graph/graph_data.py
+++ b/kadmos/graph/graph_data.py
@@ -5179,6 +5179,31 @@ class MdaoDataGraph(DataGraph, MdaoMixin):
graph_des_vars = [{'designVariableUID': self.PROBLEM_ROLES_VAR_SUFFIXES[0] + var} for var in
self.nodes[graph_node].get('design_variables')]
cmdows_executable_block_elem.add('designVariables', graph_des_vars)
+ elif architecture_roles_fun == 'surrogate model':
+ training_settings = self.nodes[graph_node].get('training')
+ if training_settings is not None:
+ cmdows_training = cmdows_executable_block_elem.add('training')
+ cmdows_training_inps = cmdows_training.add('inputs')
+ for tr_inp in self.nodes[graph_node]['training']['inputs']:
+ el_inp = cmdows_training_inps.add('input')
+ el_inp.add('parameterUID', tr_inp)
+ cmdows_training_outs = cmdows_training.add('outputs')
+ for tr_out in self.nodes[graph_node]['training']['outputs']:
+ el_out = cmdows_training_outs.add('output')
+ el_out.add('parameterUID', tr_out)
+ cmdows_training.add('settings', self.nodes[graph_node]['training']['settings'],
+ camel_case_conversion=True)
+ prediction_settings = self.nodes[graph_node].get('prediction')
+ if prediction_settings is not None:
+ cmdows_prediction = cmdows_executable_block_elem.add('prediction')
+ cmdows_prediction_inps = cmdows_prediction.add('inputs')
+ for pr_inp in self.nodes[graph_node]['prediction']['inputs']:
+ el_inp = cmdows_prediction_inps.add('input')
+ el_inp.add('parameterUID', pr_inp)
+ cmdows_prediction_outs = cmdows_prediction.add('outputs')
+ for pr_out in self.nodes[graph_node]['prediction']['outputs']:
+ el_out = cmdows_prediction_outs.add('output')
+ el_out.add('parameterUID', pr_out)
else:
cmdows_executable_block_elem.add('settings', self.nodes[graph_node].get('settings'),
camel_case_conversion=True)
@@ -5232,10 +5257,10 @@ class MdaoDataGraph(DataGraph, MdaoMixin):
cmdows_des_vars = cmdows_single_architecture_exe_block.findall('designVariables/designVariable')
graph_des_vars = [var.findtext('designVariableUID')[10:] for var in list(cmdows_des_vars)]
self.nodes[cmdows_uid]['design_variables'] = graph_des_vars
- if role == 'converger' or role == 'optimizer':
+ if role == 'converger' or role == 'optimizer' or role == 'distributed system converger':
if 'settings' not in self.nodes[cmdows_uid] or self.nodes[cmdows_uid]['settings'] is None:
self.nodes[cmdows_uid]['settings'] = {}
- if role == 'converger':
+ if role == 'converger' or role == 'distributed system converger':
setting_options = OrderedDict([('linear_solver', 'linearSolver'),
('nonlinear_solver', 'nonlinearSolver')])
solver_settings_options = \
@@ -5270,6 +5295,11 @@ class MdaoDataGraph(DataGraph, MdaoMixin):
OrderedDict((((key, cmdows_single_architecture_exe_block.findtext('settings/' +
setting_options[setting_option] + '/' + solver_settings_options[key])) for
key in solver_settings_options)))
+ elif role == 'distributed system converger':
+ self.nodes[cmdows_uid]['settings'] = \
+ OrderedDict((((key, cmdows_single_architecture_exe_block
+ .findtext('settings/' + solver_settings_options[key]))
+ for key in solver_settings_options)))
if role == 'optimizer':
cmdows_des_vars = \
cmdows_single_architecture_exe_block.findall('objectiveVariables/objectiveVariable')
@@ -5306,6 +5336,21 @@ class MdaoDataGraph(DataGraph, MdaoMixin):
cmdows_single_architecture_exe_block.findtext('settings/seed')
self.nodes[cmdows_uid]['settings']['levels'] = \
cmdows_single_architecture_exe_block.findtext('settings/levels')
+ elif role == 'surrogate model':
+ training_inputs = cmdows_single_architecture_exe_block.findall(
+ 'training/inputs/input/parameterUID')
+ training_outputs = cmdows_single_architecture_exe_block.findall(
+ 'training/outputs/output/parameterUID')
+ training_fitting_method = cmdows_single_architecture_exe_block.findtext(
+ 'training/settings/fittingMethod')
+ self.nodes[cmdows_uid]['training'] = OrderedDict((('inputs', [tr_inp.text for tr_inp in training_inputs]),
+ ('outputs', [tr_out.text for tr_out in training_outputs]),
+ ('settings', OrderedDict((('fitting_method', training_fitting_method),)))))
+ prediction_inputs = cmdows_single_architecture_exe_block.findall('prediction/inputs/input/parameterUID')
+ prediction_outputs = cmdows_single_architecture_exe_block.findall('prediction/outputs/output/parameterUID')
+ self.nodes[cmdows_uid]['prediction'] = OrderedDict(
+ (('inputs', [pr_inp.text for pr_inp in prediction_inputs]),
+ ('outputs', [pr_out.text for pr_out in prediction_outputs])))
else:
for role in self.ARCHITECTURE_ROLES_FUNS:
cmdows_role_name = make_camel_case(role)
@@ -5890,7 +5935,12 @@ class MdaoDataGraph(DataGraph, MdaoMixin):
category='function',
architecture_role=self.ARCHITECTURE_ROLES_FUNS[10], # distributed system converger
label=label,
- instance=1)
+ instance=1,
+ settings=OrderedDict((('method', 'BLISS-2000'),
+ ('last_iterations_to_consider', 1),
+ ('maximum_iterations', 20),
+ ('convergence_tolerance_relative', 1e-4),
+ ('convergence_tolerance_absolute', 1e-4))))
assert isinstance(inputs, list)
self.assert_node_exists(inputs)
@@ -6163,6 +6213,9 @@ class MdaoDataGraph(DataGraph, MdaoMixin):
self.nodes[doe_block]['design_variables'][des_var]['samples'] = self.nodes[des_var]['samples']
else:
self.nodes[doe_block]['design_variables'][des_var]['samples'] = None
+ if 'problem_role' not in self.nodes[des_var] or \
+ self.nodes[des_var]['problem_role'] != self.PROBLEM_ROLES_VARS[0]:
+ self.nodes[des_var]['problem_role'] = self.PROBLEM_ROLES_VARS[0]
self.nodes[doe_block]['quantities_of_interest'] = qoi_nodes
# For the custom design table, add the table with values to the settings
@@ -6240,7 +6293,10 @@ class MdaoDataGraph(DataGraph, MdaoMixin):
category='function',
architecture_role=self.ARCHITECTURE_ROLES_FUNS[9], # Surrogate model
label=label,
- instance=1)
+ instance=1,
+ training=OrderedDict((('inputs', train_inputs), ('outputs', train_outputs),
+ ('settings', OrderedDict((('fitting_method', 'Kriging'),))))),
+ prediction=OrderedDict((('inputs', sm_inputs), ('outputs', []))))
# Connect the surrogate model
# Connect train data as input
@@ -6257,6 +6313,7 @@ class MdaoDataGraph(DataGraph, MdaoMixin):
node_apprs = []
for sm_out_or in sm_out_originals:
node_appr = self.copy_node_as(sm_out_or, self.ARCHITECTURE_ROLES_VARS[10])
+ self.nodes[sm]['prediction']['outputs'].append(node_appr)
self.add_edge(sm, node_appr)
node_apprs.append(node_appr)
diff --git a/kadmos/graph/graph_kadmos.py b/kadmos/graph/graph_kadmos.py
index 0624a5413e5736ca261acac8058d988cf968db30..b85d1dc573ba3486e8cb6a9479f87a4ad7085df2 100644
--- a/kadmos/graph/graph_kadmos.py
+++ b/kadmos/graph/graph_kadmos.py
@@ -128,7 +128,7 @@ class KadmosGraph(nx.DiGraph, EquationMixin, VistomsMixin):
'PostAnalysis', # 8
'Metamodel', # 9
'Converger'] # 10
- CMDOWS_ARCHITECTURE_ROLE_SPLITTER = get_list_entries(ARCHITECTURE_ROLES_FUNS, 0, 1, 2, 3)
+ CMDOWS_ARCHITECTURE_ROLE_SPLITTER = get_list_entries(ARCHITECTURE_ROLES_FUNS, 0, 1, 2, 3, 9, 10)
SYS_PREFIX = 'Sys-'
SUBSYS_PREFIX = 'Sub-'
SUBSYS_SUFFIX = '-'