An adjoint method was used to investigate the sensitivity of peak ozone at selected sites in Southern California to nearly 900 model inputs including surface emissions, reaction rate coefficients, dry deposition velocities, boundary conditions, and initial conditions. Simulations showed large changes in ozone and ozone sensitivities at three sites investigated between summers 1987 and 1997 due to emission reductions. However, only small changes in ozone and ozone sensitivities were predicted between 1997 and 2010. Sensitivities of the differences in ozone between simulations with different emission scenarios were calculated and compared to sensitivities of ozone in each simulation. In some cases, the sensitivities of ozone differences were smaller than those of ozone itself, but in other cases, such as when the sensitivityto NOx emissions changed sign, sensitivities of differences were larger. The adjoint method was most useful for determining when and where model inputs affect, or have the potential to affect, an ozone response. For example, the method was used to plot the spatial distribution of important emission source regions to 1-hour versus 8-hour peak ozone. Changes in the distribution and sign of the adjoint function for emitted species revealed changes in the area of influence of pollutant emissions on peak ozone due to emission controls. The adjoint method provides useful information complementary to that obtained from forward sensitivity analysis methods.
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http://dx.doi.org/10.1021/es051026z | DOI Listing |
iScience
January 2025
Department of Artificial Intelligence, Hanyang University, Seoul 04763, South Korea.
We present a Fourier neural operator (FNO)-based surrogate solver for the efficient optimization of wavefronts in tunable metasurface controls. Existing methods, including the Gerchberg-Saxton algorithm and the adjoint optimization, are often computationally demanding due to their iterative processes, which require numerical simulations at each step. Our surrogate solver overcomes this limitation by providing highly accurate gradient estimations with respect to changes in tunable meta-atoms without the need for direct simulations.
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December 2024
Department of Biomedical Engineering, Tufts University, 4 Colby Street, Medford, MA, 02155, USA.
We propose an overview of the Rytov approximation in diffuse optics of biological tissues, for the inverse and forward problems. First, we show a physical interpretation of the Rytov approximation as a type of partial pathlength (named fluence rate partial pathlength) which is distinct from the usual partial pathlength for reflectance measurements. Second, we study the accuracy of the Rytov approximation for the calculation of Jacobians considering absorption perturbations and reflectance measurements.
View Article and Find Full Text PDFNanophotonics
April 2024
School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.
Finding an optimal device structure in the vast combinatorial design space of freeform nanophotonic design has been an enormous challenge. In this study, we propose physics-informed reinforcement learning (PIRL) that combines the adjoint-based method with reinforcement learning to improve the sample efficiency by an order of magnitude compared to conventional reinforcement learning and overcome the issue of local minima. To illustrate these advantages of PIRL over other conventional optimization algorithms, we design a family of one-dimensional metasurface beam deflectors using PIRL, exceeding most reported records.
View Article and Find Full Text PDFNanophotonics
April 2024
KAIST, Daejeon, Republic of Korea.
Proximity-field nanopatterning (PnP) have been used recently as a rapid, cost-effective, and large-scale fabrication method utilizing volumetric interference patterns generated by conformal phase masks. Despite the effectiveness of PnP processes, their design diversity has not been thoroughly explored. Here, we demonstrate that the possibility of generating any two-dimensional lattice with diverse motifs.
View Article and Find Full Text PDFNanophotonics
September 2024
Ningbo University, Ningbo, China.
Mode converters (MCs) play an essential role in mode-division multiplexing (MDM) systems. Numerous schemes have been developed on the silicon-on-insulator (SOI) platform, yet most of them focus solely on the conversion of fundamental mode to one or two specific higher-order modes. In this study, we introduce a hybrid shape optimization (HSO) method that combines particle swarm optimization (PSO) with adjoint methods to optimize the shape of the S-bend waveguide, facilitating the design of arbitrary-order MCs featuring compactness and high performance.
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