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One of NASA’s primary science goals is to directly image and characterize the atmospheres of Earth-like planets orbiting nearby stars. The observations are extremely challenging because the planets appear adjacent to their parent stars, near telescope diffraction limits, and their reflected light is 10 billion times fainter than the star. An elegant solution to this problem, first proposed by Lyman Spitzer in 1962, is to employ a starshade, a flower-shaped diffraction screen positioned far in front of a telescope so that it shadows the starlight without blocking the planet light.

Topics: Directed Energy, DOD SMART Scholar program, recruiting and hiring.

Recent strides in machine learning have shown that computation can be performed by practically any controllable physical system that responds to physical stimuli encoding data [1]. This perspective opens new frontiers for computational approaches using Physical Neural Networks (PNNs) [2, 3, 4] and provides a framework to deepen our understanding of their biological counterparts—neural circuits in living organisms.