Leveraging Python to Drive Advanced Optimizations of Photonic Devices in OptiFDTD
Within a rapidly evolving photonics industry, the ability to iterate on complex designs is a significant competitive advantage. OptiFDTD has long provided researchers with a robust, high-performance simulation environment, complete with a versatile VBScripting engine that allows for extensive internal automation and customization.
This webinar focuses on how to further extend these capabilities by integrating OptiFDTD into a Python-driven ecosystem. We will demonstrate how users can combine OptiFDTD with custom Python control code or to access the diverse library of high-level optimization frameworks openly available. By establishing a bridge between external Python scripts and OptiFDTD’s internal VBScripting, designers can orchestrate sophisticated, automated workflows that are both flexible and highly efficient.
Key Discussion Points:
- The Best of Both Worlds: Use of OptiFDTD’s numerical engines while utilizing Python as a high-level controller for complex design sweeps.
- Interfacing Mechanics: Technical walkthrough of the communication layer between Python and VBScript, ensuring seamless parameter passing and Figure of Merit (FoM) extraction.
- Intelligent Optimization: Implementing advanced strategies like Bayesian Optimization to navigate the design space effectively. We will discuss using Gaussian Processes (GP) to model the objective function, where the mean and uncertainty guide the search.
- Strategic Sampling: Utilizing Acquisition Functions (AF) to balance the exploration of new design regions with the exploitation of known high-performance areas.
By the end of this session, you will understand how to treat OptiFDTD as a powerful “simulation node” within your broader computational toolset, allowing you to find optimal solutions with fewer evaluations and greater architectural flexibility.