Systems built to replace trial-and-error with predictive, simulation-driven decision-making. Each example reflects a real engineering workflow where conventional approaches break down.
Uses SciPy's differential_evolution to find the global minimum of any 1D or 2D mathematical expression across user-defined bounds.
Configure dimensionality, function, and search bounds.
Optimization summary, status, and minimum location.
Interactive function landscape with identified global minimum.
Select a preset or enter a custom function, then run the optimizer to visualize the search landscape.
This demo uses stochastic global optimization rather than local gradient descent, allowing it to escape local minima and explore broader design spaces more reliably.
Evaluate operating conditions and geometry constraints to identify viable heat exchanger configurations from a precomputed design space.
Predict pressure drop across custom heat exchanger geometries using a surrogate model trained on a large OpenFOAM simulation set.
Automates null surface generation for optical inspection, replacing a high-effort manual process with optimization-driven computation.