Engineering Functions

Examples using constrained engineering design problems.

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Welded Beam Design

from surfaces.test_functions.algebraic import WeldedBeamFunction

func = WeldedBeamFunction()

# Design parameters
params = {
    "h": 0.2,    # Weld thickness
    "l": 3.5,    # Weld length
    "t": 9.0,    # Beam thickness
    "b": 0.2     # Beam width
}

# Penalized objective (includes constraint violations)
cost = func(params)
print(f"Penalized cost: {cost:.4f}")

# Raw objective without penalty
raw_cost = func.raw_objective(params)
print(f"Raw cost: {raw_cost:.4f}")

# Check feasibility
print(f"Feasible: {func.is_feasible(params)}")

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Constraint Handling

from surfaces.test_functions.algebraic import WeldedBeamFunction

func = WeldedBeamFunction()
params = {"h": 0.2, "l": 3.5, "t": 9.0, "b": 0.2}

# Get constraint values (g <= 0 is feasible)
constraints = func.constraints(params)
print("Constraints (g <= 0 is OK):")
for i, g in enumerate(constraints):
    status = "OK" if g <= 0 else "VIOLATED"
    print(f"  g{i+1}: {g:.4f} [{status}]")

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Exploring the Design Space

import random
from surfaces.test_functions.algebraic import WeldedBeamFunction

func = WeldedBeamFunction()
space = func.search_space

print("Design space bounds:")
for name, values in space.items():
    print(f"  {name}: [{min(values):.3f}, {max(values):.3f}]")

# Random search for feasible designs
print("\nSearching for feasible designs...")
feasible_count = 0
for _ in range(100):
    sample = {k: random.choice(v) for k, v in space.items()}
    if func.is_feasible(sample):
        feasible_count += 1
        cost = func.raw_objective(sample)
        print(f"  Feasible design found, cost: {cost:.4f}")
        break

print(f"Feasible designs in 100 samples: {feasible_count}")