Machine and Deep Learning for Smart Electromagnetism and Photonics

Researchers: Haim Suchowski (Physics) & Nadav Cohen (Computer Science)


Artificial Intelligence has emerged as a powerful approach that has already led to many breakthroughs in multiple scientific and technological fields in the past decade.


Deep Learning, inspired by the layered and hierarchical deep architecture of the human brain, has emerged as an efficient means to design photonic structures.


Our current proposal stems from the complementary expertise of the two groups and from the realization that Deep learning strength can be further leveraged to address inverse design challenges in physics and, in particular, in electromagnetism’s sub-wavelength inverse design spanning the large spectrum of electromagnetic and photonics applications.


More specifically, using recently introduced DL methods and our recent achievements, we plan to address challenging inverse design tasks in electromagnetism such as sensing with nano-photonics, novel 5G and Wifi antennas as well as integrated photonics for quantum information and LIDARs.

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