I-Corps: Translation Potential of a Design Tool for Optical Fiber Devices

This I-Corps project is focused on the development of a new optical fiber modeling software. Optical fibers are useful in many industries and applications including telecommunications, medicine and defense. This software is built on a foundation of numerical methods and mathematical models, ensuring accuracy and efficiency in optical simulations.

The software provides a tool that researchers can use to get a more accurate understanding of their optical experiments. The tool will empower scientists and engineers to tackle complex optical modeling and simulation challenges. Users can run the software in a graphic user interface to predict the results before running expensive experiments.

The software's advanced technology empowers customers to tackle various challenges in communication, medical, and defense research.

This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a new optical fiber modeling software. The software incorporates advanced models to simulate nonlinear optical effects such as self-phase modulation, four-wave mixing, stimulated Raman scattering, stimulated Brillouin scattering, and transverse mode instability.

These capabilities are essential for designing high-performance optical amplifiers and other nonlinear photonic devices. The software includes an extensive library of material models, accurately representing the refractive index, dispersion, and loss characteristics of various optical materials. Users can also define custom materials to match specific experimental data, ensuring simulations are tailored to real-world conditions.

The software features robust optimization algorithms that help users customize their designs for maximum efficiency and performance. Users can quickly explore a vast design space and identify optimal configurations by leveraging gradient-based optimization, particle swarm optimization, and machine learning.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.