The convergence of gradient descent, GPUs, and Python has revolutionized the field of artificial intelligence, and now, these same technologies are opening new frontiers in photonics. GPUs boost electromagnetic simulation speed by a factor of 100, adjoint-based gradient descent enables efficient exploration of the design space, and Python can automate complex workflows. When combined, they unlock unprecedented potential for accelerating photonic innovation.
In this video, Shanhui and Prash introduced the development of Flexcompute and its electromagnetic products, and Tyler talked about the Tidy3D inverse design tool - TidyGrad in depth.
