SmartMDAO 1.4.0

Extensible MDAO framework with zero boilerplate integration and plug-and-play optimization.

SmartMDAO 🚀

Extensible MDAO framework with zero boilerplate integration and plug-and-play optimization.

SmartMDAO is a lightweight, purely Pythonic framework for Multidisciplinary Design Analysis and Optimization (MDAO). Define your disciplines as plain Python functions, and SmartMDAO maps the dependency graph, converges cyclic feedback loops, caches expensive calls, and bridges straight into your optimizer of choice.

🔍 See It In Action

Without SmartMDAO — you hand-write the convergence loop and track state yourself:

y2 = 1.0  # initial guess
for _ in range(100):
    y1 = z1 ** 2 + z2 + x1 - 0.2 * y2
    y2_next = math.sqrt(abs(y1)) + z1 + z2
    if abs(y2_next - y2) < 1e-6:
        break
    y2 = y2_next

With SmartMDAO — declare each discipline once; the y1 ↔ y2 cycle is found and converged for you:

@pipeline.step(outputs=["y1"])
def discipline_1(z1, z2, x1, y2): return z1 ** 2 + z2 + x1 - 0.2 * y2

@pipeline.step(outputs=["y2"])
def discipline_2(z1, z2, y1): return math.sqrt(abs(y1)) + z1 + z2

pipeline.run(z1=1.0, z2=1.0, x1=1.0, y2=1.0)

🌟 Why SmartMDAO?

• Effortless MDA — no DSL, just @pipeline.step and standard type hints. Works with plain types, dataclasses, or your own objects.
• Built-in Caching — layer @cached (RAM, HDF5, Pickle) onto expensive functions for instant speedups.
• Agnostic MDO — define your OptimizationProblem once, then run it through any backend with a single string: optimize(problem, backend="scipy") or backend="openturns". Bring your own via @register_backend, or drop straight to PipelineEvaluator for full control.
• Type-Safe — static validation catches mismatched disciplines before a single step runs; opt-in runtime checks catch the rest.
• Built Also for Researchers — solvers are plain Protocol classes, so custom MDA convergence algorithms drop in without touching the core framework.

⚡ Full Quick Start: Caching, Constraints & Optimization (click to expand)

Here is how easily you can solve the classic Sellar coupled problem end-to-end, from caching through optimization - swapping the solver backend with a single string.

import math
import logging
from smartmdao import (
    Pipeline,
    HybridSolver,
    PipelineEvaluator,
    OptimizationProblem,
    ConstraintSpec,
    optimize,
    cached,
    MemoryBackend,
    configure_logging
)

# --- Setup Logging and Cache ---
configure_logging(level=logging.WARNING)
mem_cache = MemoryBackend() # HDF5 and Pickle also available

# ==============================================================================
# PART 1: Initialize the Pipeline with the HybridSolver
# ==============================================================================
# The HybridSolver automatically detects and converges cyclic dependencies
pipeline = Pipeline(
    solver=HybridSolver(max_iterations=100, tolerance=1e-6)
)

# ==============================================================================
# PART 2: Define the Sellar Disciplines (MDA)
# ==============================================================================
@pipeline.step(outputs=["y1"])
@cached(mem_cache) # Instantly cache this discipline to speed up evaluations
def discipline_1(z1: float, z2: float, x1: float, y2: float) -> float:
    return (z1 ** 2) + z2 + x1 - (0.2 * y2)

@pipeline.step(outputs=["y2"])
@cached(mem_cache) 
def discipline_2(z1: float, z2: float, y1: float) -> float:
    return math.sqrt(abs(y1)) + z1 + z2

@pipeline.step(outputs=["objective"])
@cached(mem_cache) 
def compute_objective(x1: float, z2: float, y1: float, y2: float) -> float:
    return (x1 ** 2) + z2 + (y1 ** 2) + math.exp(-y2)

@pipeline.step(outputs=["constraint_1"])
@cached(mem_cache) 
def compute_constraint_1(y1: float) -> float:
    """Constraint formulation: 3.16 - y1 <= 0"""
    return 3.16 - y1

@pipeline.step(outputs=["constraint_2"])
@cached(mem_cache) 
def compute_constraint_2(y2: float) -> float:
    """Constraint formulation: y2 - 24.0 <= 0"""
    return y2 - 24.0

# ==============================================================================
# PART 3: Bridge the Pipeline to an Optimizer-Agnostic Problem
# ==============================================================================
evaluator = PipelineEvaluator(
    pipeline=pipeline,
    design_vars=["z1", "z2", "x1"],
    constants={"y2": 1.0} # Initial guess to kick off the cycle
)

# Both backends expect h(x) >= 0; Sellar's constraints are naturally written
# as g(x) <= 0, so we flip the sign with multiplier=-1.0.
problem = OptimizationProblem(
    evaluator=evaluator,
    initial_guess=[1.0, 1.0, 1.0],
    bounds=[(-10.0, 10.0), (0.0, 10.0), (0.0, 10.0)],
    objective="objective",
    constraints=[
        ConstraintSpec(name="constraint_1", multiplier=-1.0),
        ConstraintSpec(name="constraint_2", multiplier=-1.0),
    ],
)

# ==============================================================================
# PART 4: Run the *same* problem through two different backends
# ==============================================================================
for backend_name in ("scipy", "openturns"):
    result = optimize(problem, backend=backend_name)
    print(f"[{backend_name:>9}] objective={result.objective_value:.4f}")

🧠 Advanced Examples

The Quick Start above just scratches the surface! Notably:

• readme_quick_start.py — the complete version of the Quick Start above, including SciPy's raw minimize() call, full state extraction, and pipeline visualization.
• optimizer_backends_demo.py — define one OptimizationProblem and run the exact same Sellar problem through scipy, openturns, and a custom registered backend, just by swapping a string.
• type_validation_demo.py — static and runtime type validation, Optional/Union support, and writing a custom TypeChecker.

For deeper nesting, custom convergence solvers, or more complex multidisciplinary systems, check out the scripts folder in our GitHub repository.

📦 Installation

SmartMDAO is available on PyPI. We recommend using uv for lightning-fast installation, but standard pip works perfectly.

Using uv:

uv add smartmdao

Using pip:

pip install smartmdao

(Note: Visualization features require the graphviz system binary to be installed on your OS).

Installing Graphviz (Optional System Requirement)

While smartmdao works perfectly on its own, generating pipeline diagrams requires the graphviz system binary to be installed on your OS.

macOS (Homebrew):

brew install graphviz

Linux (Ubuntu/Debian):

sudo apt-get install graphviz

Windows (winget):

winget install graphviz

(Alternatively, you can download the Windows installer directly from the official Graphviz website)

🤝 Contributing & License

Contributions are welcome! Please feel free to submit a Pull Request.This project is licensed under the MIT License - see the LICENSE file for details.