Zobrazeno 1 - 10
of 187
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 1, Gomez, Sebastian 1, Hu, Guanzhong 2, Abaalkhail, Albatool 1, Beasley, Jazmyn E. 2, Zhang, Peter 3, Xu, Yuan 1, Chen, Xiaohan 1, Tian, Steven 2, Mandal, Jyotirmoy 4, 5, ∗, Raman, Aaswath P. 4, ∗∗, Yu, Nanfang 1, ∗∗∗, Yang, Yuan 1, 6, ∗∗∗∗
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