SmartUQ's Dynamic Optimization and Dynamic Contour Finding tools apply Bayesian optimization to solving hard engineering design challenges.
Both tools operate by:
Using an emulator (e.g. a Gaussian Process model) to accurately approximate expensive simulations.
Targeting optimization (maximizing/minimizing outputs) or contour mapping (improving accuracy around a specified level set) by guiding the sampling process with information from the emulator's predictions and uncertainties, akin to using an acquisition function to balance exploration and exploitation.
Using an iterative sampling strategy to intelligently select new simulation points to increase emulator accuracy and improve optimal point estimates.
This allows efficient optimization and characterization of complex engineering simulations with significantly fewer simulation runs.
Bayesian Optimization Fundamentals
Bayesian optimization is an efficient strategy for optimizing systems via expensive physical tests or computationally heavy simulations.
Core Components of Bayesian Optimization
Probabilistic Surrogate Model (e.g., Gaussian Process):
Approximates the expensive objective function.
Provides a predicted mean and uncertainty (variance) for any given point.
Continuously refines the model as more data is gathered.
Acquisition Function (e.g., Expected Improvement):
Determines the next best point to evaluate by using the surrogate model's predictions and uncertainties.
Trades off between exploitation and exploration for both optimization and improving the surrogate model.
Bayesian Optimization Workflow
The Bayesian optimization process involves these sequential steps:
Begin with initial observations.
Fit the surrogate model to the observed data.
Optimize the acquisition function to identify the next evaluation point.
Evaluate the true objective function at the identified point.
Incorporate the new observation and repeat the cycle.
Engineering Applications of Bayesian Optimization
Bayesian optimization is very useful in engineering domains where experiments or simulations are resource-intensive:
Aerospace:Minimize sampling requirements for optimization and simulation calibration when simulation or physical data are expensive to collect or there are limitted samples available.
Robotics: Optimizing control policies or physical design parameters for robotic systems, significantly reducing costly test runs.
Chemical and Materials Engineering: Designing new materials or optimizing chemical processes such as catalyst composition or process temperatures, thus reducing expensive laboratory experimentation.
Design Optimization: Tuning parameters in complex engineering simulations (e.g., fluid dynamics, structural analysis) to achieve optimal performance characteristics using fewer simulation runs.
Advantages of SmartUQ's Dynamic Optimization and Contour Finding
Reducing the number of expensive simulations or experiments required to identify optimal designs or understand system behaviors.
Accelerating development cycles and substantially lowering computational costs.
Enabling the exploration of a broader design space than traditional approaches.
Using an intelligent emulator to guide the optimization, allowing engineers to attain high-quality solutions with fewer resources, a critical advantage when dealing with computationally intensive simulations or costly physical prototypes.
This Webinar will demonstrate why surrogate model accuracy is so important for indirect optimization success when conducting machine learning based or Bayesian Optimization.
As simulation, monitoring, and testing techniques become more sophisticated and produce increasing quantities of data, the need for a powerful engineering analytics tool to accurately and efficiently extract information and insights from the data has never been greater.
