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Emerging reconfigurable metasurfaces offer various possibilities for programmatically manipulating electromagnetic waves across spatial, spectral, and temporal domains, showcasing great potential for enhancing terahertz applications. However, they are hindered by limited tunability, particularly evident in relatively small phase tuning over 270°, due to the design constraints with time-intensive forward design methodologies. Here, a multi-bit programmable metasurface is demonstrated capable of terahertz beam steering facilitated by a developed physics-informed inverse design (PIID) approach. Through integrating a modified coupled mode theory (MCMT) into residual neural networks, the PIID algorithm not only significantly increases the design accuracy compared to conventional neural networks but also elucidates the intricate physical relations between the geometry and the modes. Without decreasing the reflection intensity, the method achieves the enhanced phase tuning as large as 300°. Additionally, the inverse-designed programmable beam steering metasurface is experimentally validated, which is adaptable across 1-bit, 2-bit, and tri-state coding schemes, yielding a deflection angle up to 68° and broadened steering coverage. The demonstration provides a promising pathway for rapidly exploring advanced metasurface devices, with potentially great impact on communication and imaging technologies.
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http://dx.doi.org/10.1002/advs.202406878 | DOI Listing |
ISA Trans
September 2025
Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, 430081
The autoloader is a key subsystem in modern main battle tanks, mainly responsible for ammunition transfer, loading, and resupply. However, it often suffers from uncertainties induced by base oscillations, leading to potential instability. While various control strategies have been proposed, most rely on prior knowledge of such oscillations.
View Article and Find Full Text PDFProc IEEE Int Symp Appl Ferroelectr
September 2024
Department of Biomedical Engineering, New York City, USA.
Arterial stiffness is a key predictor of cardiovascular mortality. This study utilizes ultrasound-based Pulse Wave Imaging (PWI) and Vector Flow Imaging (VFI) to track vessel wall displacement caused by arterial pulse wave propagation and blood flow velocity at a high frame rate (3.3 kHz) to estimate localized arterial wall stiffness through an Inverse problem setting.
View Article and Find Full Text PDFCommun Eng
September 2025
Department of Disaster Mitigation for Structures, College of Civil Engineering, Tongji University, Shanghai, China.
Standard physics informed deep learning and their enhanced variants encounter challenges in addressing inverse problems characterized by extreme discontinuities and high-order parameterized differential equations due to the use of globally smooth activation functions, especially when the unknown parameters exhibit spatially distributed characteristics. Phenomena such as discontinuous loads, boundary truncations, and abrupt changes in material properties introduce singularities in the derivatives, which in turn lead to ill-conditioned information in the gradient flow. To address these limitations, here we propose an information-distilled physics-informed deep-learning framework that combines reduced-order modeling, multi-level domain decomposition, and an ill-conditioning-suppression mechanism.
View Article and Find Full Text PDFSci Rep
August 2025
School of Data Science, University of Virginia, Charlottesville, 22903, USA.
Physics-informed neural networks (PINNs) have emerged as a powerful framework for modeling complex physical systems by embedding governing equations into the learning process. For example, PINNs offer a promising approach to solving the inverse electrocardiographic imaging (ECGI) problem, which aims to reconstruct heart-surface electrical activity from body-surface potential measurements. However, existing PINN-based ECGI models face several challenges, including overfitting to sparsely sampled collocation points, unstable training dynamics, and limited network scalability-particularly when applied to high-dimensional spatiotemporal data.
View Article and Find Full Text PDFMicromachines (Basel)
July 2025
Shanghai Precision Measurement Semiconductor Technology, Inc., Shanghai 210700, China.
As semiconductor manufacturing advances into the angstrom-scale era characterized by three-dimensional integration, conventional metrology technologies face fundamental limitations regarding accuracy, speed, and non-destructiveness. Although optical spectroscopy has emerged as a prominent research focus, its application in complex manufacturing scenarios continues to confront significant technical barriers. This review establishes three concrete objectives: To categorize AI-optical spectroscopy integration paradigms spanning forward surrogate modeling, inverse prediction, physics-informed neural networks (PINNs), and multi-level architectures; to benchmark their efficacy against critical industrial metrology challenges including tool-to-tool (T2T) matching and high-aspect-ratio (HAR) structure characterization; and to identify unresolved bottlenecks for guiding next-generation intelligent semiconductor metrology.
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