Zobrazeno 1 - 10
of 182
pro vyhledávání: '"Raman, Aaswath"'
Tuning the spatial extent of directional thermal emission across an arbitrary, and fixed spectral bandwidth is a fundamentally enabling capability for a range of emerging applications such as thermophotovoltaics, thermal imaging, and radiative coolin
Externí odkaz:
http://arxiv.org/abs/2412.17659
Autor:
Cui, Daniel, Raman, Aaswath P.
We show that purely lossy defects in one- and two-dimensional non-Hermitian photonic crystals can enable a dramatic gain enhancement not accessible with lossless defects. We further show that the underlying mechanism behind the loss-induced enhanceme
Externí odkaz:
http://arxiv.org/abs/2411.00016
Autor:
Santhanam, Parthiban, Cui, Daniel, Hwang, Jae Seung, Abraham, David, He, Isabella, Watanabe, Enzo, Raman, Aaswath Pattabhi
Voltage conversion is a fundamental electronic process critical to engineered systems across a wide spectrum of applications and spanning many orders of magnitude in scale. Conventional approaches like transformers and charge pumps perform well in sp
Externí odkaz:
http://arxiv.org/abs/2410.13220
A fundamental capability for any transmissive optical component is anti-reflection, yet this capability is challenging to achieve in a cost-efficient manner over longer infrared wavelengths. We demonstrate that Mie resonant nanophotonic structures en
Externí odkaz:
http://arxiv.org/abs/2306.05405
Controlling both the spectral bandwidth and directional range of emitted thermal radiation is a fundamental challenge in modern photonics and materials research. Recent work has shown that materials with a spatial gradient in their epsilon near zero
Externí odkaz:
http://arxiv.org/abs/2212.14112
In recent years, hybrid design strategies combining machine learning (ML) with electromagnetic optimization algorithms have emerged as a new paradigm for the inverse design of photonic structures and devices. While a trained, data-driven neural netwo
Externí odkaz:
http://arxiv.org/abs/2209.15434
From higher computational efficiency to enabling the discovery of novel and complex structures, deep learning has emerged as a powerful framework for the design and optimization of nanophotonic circuits and components. However, both data-driven and e
Externí odkaz:
http://arxiv.org/abs/2209.04447
Controlling the spectral response of thermal emitters has become increasingly important for a range of energy and sensing applications. Conventional approaches to achieving arbitrary spectrum selectivity in photonic systems have entailed combining mu
Externí odkaz:
http://arxiv.org/abs/2209.03951
Autor:
Cheng, Qilong, Gomez, Sebastian, Hu, Guanzhong, Abaalkhail, Albatool, Beasley, Jazmyn E., Zhang, Peter, Xu, Yuan, Chen, Xiaohan, Tian, Steven, Mandal, Jyotirmoy, Raman, Aaswath P., Yu, Nanfang, Yang, Yuan
Publikováno v:
In Nexus 17 September 2024 1(3)
A fundamental challenge in the design of photonic devices, and electromagnetic structures more generally, is the optimization of their overall architecture to achieve a desired response. To this end, topology or shape optimizers based on the adjoint
Externí odkaz:
http://arxiv.org/abs/2109.14886