Physics-AI Symbiosis

TL;DR

This paper explores how integrating physics principles with AI, especially in photonics, can address challenges like interpretability and efficiency, potentially revolutionizing physical science and AI development.

Contribution

It proposes a symbiotic approach combining physics and AI to overcome current limitations and advance both fields, particularly in photonic applications.

Findings

Physics-AI symbiosis enhances interpretability and efficiency.

Photonic applications benefit from physics-informed AI models.

The approach has potential to transform physical science and AI development.

Abstract

The phenomenal success of physics in explaining nature and designing hardware is predicated on efficient computational models. A universal codebook of physical laws defines the computational rules and a physical system is an interacting ensemble governed by these rules. Led by deep neural networks, artificial intelligence (AI) has introduced an alternate end-to-end data-driven computational framework, with astonishing performance gains in image classification and speech recognition and fueling hopes for a novel approach to discovering physics itself. These gains, however, come at the expense of interpretability and also computational efficiency; a trend that is on a collision course with the expected end of semiconductor scaling known as the Moore's Law. With focus on photonic applications, this paper argues how an emerging symbiosis of physics and artificial intelligence can overcome such formidable challenges, thereby not only extending the latter's spectacular rise but also transforming the direction of physical science.