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Add micro-dumux-surrogate participant to two-scale heat conduction #684

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9953198
Initial code to add micro-dumux surrogate for two-scale-heat-conduction
IshaanDesai Aug 6, 2025
0ed5449
Add mechanism to create snapshots using the Micro Manager
IshaanDesai Aug 6, 2025
3f8ffe8
Add more functionality related to creation and training of the surrogate
IshaanDesai Aug 6, 2025
65f8145
Add first working version of the complete surrogate workflow
IshaanDesai Aug 6, 2025
e4ed0db
Pass validation data to the validation of the surrogate
IshaanDesai Aug 7, 2025
79081bd
bugfixes and add runnable surrogate model
Snapex2409 Nov 7, 2025
776655a
add config for model adaptivity
Snapex2409 Nov 7, 2025
7b5c53f
Merge branch 'precice:develop' into micro-dumux-surrogate
IshaanDesai Dec 1, 2025
dae606f
update to new mm version
Snapex2409 Dec 1, 2025
2d6df8b
Merge remote-tracking branch 'origin/micro-dumux-surrogate' into micr…
Snapex2409 Dec 1, 2025
8ffbc3c
Merge branch 'develop' into micro-dumux-surrogate
IshaanDesai Dec 28, 2025
79b8c46
Add changelog entry
IshaanDesai Dec 28, 2025
53d9b2b
autopep8 formatting
IshaanDesai Dec 28, 2025
4cf8e2d
Merge branch 'develop' into micro-dumux-surrogate
IshaanDesai Jan 23, 2026
bf4cfa2
Merge branch 'develop' into micro-dumux-surrogate
IshaanDesai Jul 9, 2026
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autopep8 formatting
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@IshaanDesai
IshaanDesai committed Dec 28, 2025
commit 53d9b2b9066bdc823565e00ad3d6a3e8cd78311d
12 changes: 8 additions & 4 deletions two-scale-heat-conduction/micro-dumux-mada/mada_switcher.py
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@MakisH MakisH Dec 29, 2025

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What is this directory, and how is it related to the micro-dumux-surrogate/?
What does mada stand for?
Why is there no run.sh and clean.sh, if this is a case directory?

Original file line number Diff line number Diff line change
Expand Up @@ -5,16 +5,20 @@ def switching_function(resolution, location, t, input, prev_output):
result = 0
# in the beginning we only want FOM
if t == 0.0:
if resolution > 0: result = -1
else: result = 0
if resolution > 0:
result = -1
else:
result = 0
# after small init phase, we want dynamic model selection
# for test purposes we say ROM is accurate in range
else:
concentration = input['concentration']
is_valid_range = 0.45 < concentration < 0.55
is_fom = resolution == 0

if is_fom and is_valid_range: result = 1
if not is_fom and not is_valid_range: result = -1
if is_fom and is_valid_range:
result = 1
if not is_fom and not is_valid_range:
result = -1

return result
7 changes: 2 additions & 5 deletions two-scale-heat-conduction/micro-dumux-mada/micro_sim_sur.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@ def __init__(self, sim_id):
"""
self._sim_id = sim_id
self._state = None

self._model = None
with open('micro-dumux-surrogate.pkl', 'rb') as input:
self._model = joblib.load(input)
Expand All @@ -38,7 +38,7 @@ def initialize(self):
return output_data

def get_state(self):
return self._state;
return self._state

def set_state(self, state):
self._state = state
Expand All @@ -54,6 +54,3 @@ def solve(self, macro_data, dt):
output_data["grain_size"] = math.sqrt((1 - model_eval["porosity"][0][0]) / math.pi)

return output_data



7 changes: 2 additions & 5 deletions two-scale-heat-conduction/micro-dumux-surrogate/micro_sim_sur.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@ def __init__(self, sim_id):
"""
self._sim_id = sim_id
self._state = None

self._model = None
with open('micro-dumux-surrogate.pkl', 'rb') as input:
self._model = joblib.load(input)
Expand All @@ -38,7 +38,7 @@ def initialize(self):
return output_data

def get_state(self):
return self._state;
return self._state

def set_state(self, state):
self._state = state
Expand All @@ -54,6 +54,3 @@ def solve(self, macro_data, dt):
output_data["grain_size"] = math.sqrt((1 - model_eval["porosity"][0][0]) / math.pi)

return output_data



3 changes: 2 additions & 1 deletion two-scale-heat-conduction/micro-dumux-surrogate/surrogate_workflow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -120,7 +120,7 @@ def create_surrogate(snapshots_dir: str) -> tuple:
# Split the samples into training and validation sets
n_valid = 200
x_train, y_train, x_valid, y_valid = split_samples(x, y, n_valid)

inputs = Input()
inputs.add_marginals(name="concentration", dist_type="unif", parameters=[0, 0.5])

Expand Down Expand Up @@ -182,6 +182,7 @@ def validate_surrogate(x_valid, y_valid, model_name="micro-dumux-surrogate.pkl")
plt.title("IO: con to poro")
plt.show()


def main():
snapshots_dir = "output"
if not os.path.exists(snapshots_dir):
Expand Down