graphmdo 0.1.2

A dynamic, graph-driven Multidisciplinary Design Optimization (MDO) framework integrating FalkorDB, OpenMDAO, and multi-fidelity surrogate models.

GraphMDO: Dynamic Multi-Fidelity MDO Framework

GraphMDO bridges data engineering and MDO. It extracts topological data (solvers, variables, fidelity levels) to form an oriented graph, specifically utilizing KADMOS for semantic formulation and exporting to CMDOWS. The execution is handled by OpenMDAO and the Surrogate Modeling Toolbox (SMT), driven by constrained Bayesian optimization (ax-platform) or evolutionary algorithms (pymoo). The primary operational goal is to isolate and maximize a single target performance metric while strictly holding all other performance metrics constant.

Key Features

• Native Graph Formulation: Uses FalkorDB to store problem definitions (variables, tools, dependencies) as a property graph.
• Dynamic Problem Construction: Automatically translates the graph topology into an executable OpenMDAO problem.
• Multi-Fidelity Surrogates: Integrates SMT for Co-Kriging and other surrogate models.
• Constrained Bayesian Optimization: Leverages Ax Platform for robust optimization, easily managing KADMOS multi-objective targets, fidelity, and discrete/continuous parameters.

Project Architecture

1. FalkorDB: Stores the "Fundamental Problem Graph" (FPG).
2. Graph Manager: Python API to manipulate the graph structure.
3. Translator: Converts the graph into an OpenMDAO System.
4. Optimizer: Drivers (Ax, Pymoo) that execute the OpenMDAO problem holding constraints constant.

Installation

This project uses uv for dependency management.

1. Install uv (if not installed): See astral.sh/uv.

2. Clone and Install:

   git clone https://github.com/jultou-raa/GraphMDO.git
   cd GraphMDO
   uv sync
   

3. FalkorDB: Ensure you have a running FalkorDB instance (e.g., via Docker):

   docker run -p 6379:6379 -it falkordb/falkordb
   

Usage

1. Defining a Problem (Python API)

You can programmatically build your MDO problem graph:

from mdo_framework.db.graph_manager import GraphManager

gm = GraphManager()
gm.clear_graph()

# Define Variables
gm.add_variable("x", value=1.0, lower=0.0, upper=10.0)
gm.add_variable("y", value=2.0, lower=0.0, upper=10.0)
gm.add_variable("z", value=0.0)

# Define Tool
gm.add_tool("MyTool")

# Define Connections
gm.connect_input_to_tool("x", "MyTool")
gm.connect_input_to_tool("y", "MyTool")
gm.connect_tool_to_output("MyTool", "z")

2. Running Optimization

Once the graph is populated, you can run the optimization workflow. You need to provide the actual Python functions corresponding to the tool names in the graph.

from mdo_framework.core.translator import GraphProblemBuilder
from mdo_framework.optimization.optimizer import BayesianOptimizer, LocalEvaluator
from mdo_framework.core.topology import TopologicalAnalyzer

# Define tool implementation
def my_tool_func(x, y):
    return x + y  # Simple example

# Registry maps graph tool names to Python callables
tool_registry = {
    "MyTool": my_tool_func
}

# Build OpenMDAO Problem from Graph
schema = gm.get_graph_schema()
builder = GraphProblemBuilder(schema)
prob = builder.build_problem(tool_registry)

# Resolve Topology mapping design_vars automatically from KADMOS graph
analyzer = TopologicalAnalyzer(schema)
design_vars, _ = analyzer.resolve_dependencies(["z"])
parameters = analyzer.extract_parameters(design_vars)

# Run Optimization
evaluator = LocalEvaluator(prob)
optimizer = BayesianOptimizer(
    evaluator=evaluator,
    parameters=parameters,
    objectives=[{"name": "z", "minimize": True}],
)

result = optimizer.optimize(n_steps=10)
print(f"Best Result: {result['best_objectives']} at {result['best_parameters']}")

3. Running Tests

uv run pytest tests/

Contributing

1. Follow PEP 8 guidelines.
2. Ensure 100% test coverage for new features.
3. Use uv run pre-commit run --all-files before committing.