arXiv subjects

Willie J. Padilla

Publications and source records attributed to Willie J. Padilla.

At least 19 recordsLinked to original sources

arXiv2026

Active Electronic Terahertz Imaging for Industrial Applications: From Hardware to the Paradigm Shift by Artificial Intelligence

Imaging with terahertz (THz) radiation (0.3-10 THz) benefits from a unique combination of attributes: penetration through dry, non-polar packaging materials; variations of dielectric functions to provide contrast; the existence of spectral fingerprint resonances for some classes of materials; non-ionizing photon energies that are safe for use around humans; and - viewed from the low-frequency side - an extension of the capabilities of microwave radar to higher frequencies and thus to substantially better spatial resolution, at wavelengths which still permit direct measurement of the complex-valued radiation field. This review concentrates on active THz imaging with electronic sources combined with power detectors or coherent receivers - the system class most likely to deliver fast (ideally real-time), cost-effective and deployable solutions for a wide range of industrial applications such as quality control, non-destructive testing, security screening and ranging for situational awareness. Such systems should be deployable on robotic and emerging autonomous platforms. We review the state of the art of compact semiconductor detector arrays, of imaging modalities ranging from focused-beam raster and frequency-modulated continuous-wave architectures to coherent Fourier-plane acquisition, and of augmentation techniques such as compressive sensing. Particular attention is paid to the growing role of artificial intelligence: from convolutional neural networks and physics-informed deep learning for phase retrieval and image reconstruction, to agentic frameworks for autonomous system design. The bottlenecks of THz imaging - acquisition speed, resolution, contrast and cost - are re-examined in the light of these innovations, and a reference-anchored technology roadmap is derived which projects an order-of-magnitude reduction in the measurement requirements of THz imaging.

physics.optics↗

arXiv2025

An Agentic Framework for Autonomous Metamaterial Modeling and Inverse Design

Recent significant advances in integrating multiple Large Language Model (LLM) systems have enabled Agentic Frameworks capable of performing complex tasks autonomously, including novel scientific research. We develop and demonstrate such a framework specifically for the inverse design of photonic metamaterials. When queried with a desired optical spectrum, the Agent autonomously proposes and develops a forward deep learning model, accesses external tools via APIs for tasks like simulation and optimization, utilizes memory, and generates a final design via a deep inverse method. The framework's effectiveness is demonstrated in its ability to automate, reason, plan, and adapt. Notably, the Agentic Framework possesses internal reflection and decision flexibility, permitting highly varied and potentially novel outputs.

cs.AI↗

arXiv2025

Learning Electromagnetic Metamaterial Physics With ChatGPT

Large language models (LLMs) such as ChatGPT, Gemini, LlaMa, and Claude are trained on massive quantities of text parsed from the internet and have shown a remarkable ability to respond to complex prompts in a manner often indistinguishable from humans. For all-dielectric metamaterials consisting of unit cells with four elliptical resonators, we present a LLM fine-tuned on up to 40,000 data that can predict the absorptivity spectrum given a text prompt that only specifies the metasurface geometry. Results are compared to conventional machine learning approaches including feed-forward neural networks, random forest, linear regression, and K-nearest neighbor (KNN). Remarkably, the fine-tuned LLM (FT-LLM) achieves a comparable performance across large dataset sizes with a deep neural network. We also explore inverse problems by asking the LLM to predict the geometry necessary to achieve a desired spectrum. LLMs possess several advantages over humans that may give them benefits for research, including the ability to process enormous amounts of data, find hidden patterns in data, and operate in higher-dimensional spaces. This suggests they may be able to leverage their general knowledge of the world to learn faster from training data than traditional models, making them valuable tools for research and analysis.

physics.optics↗

arXiv2024

Does Deep Active Learning Work in the Wild?

Deep active learning (DAL) methods have shown significant improvements in sample efficiency compared to simple random sampling. While these studies are valuable, they nearly always assume that optimal DAL hyperparameter (HP) settings are known in advance, or optimize the HPs through repeating DAL several times with different HP settings. Here, we argue that in real-world settings, or in the wild, there is significant uncertainty regarding good HPs, and their optimization contradicts the premise of using DAL (i.e., we require labeling efficiency). In this study, we evaluate the performance of eleven modern DAL methods on eight benchmark problems as we vary a key HP shared by all methods: the pool ratio. Despite adjusting only one HP, our results indicate that eight of the eleven DAL methods sometimes underperform relative to simple random sampling and some frequently perform worse. Only three methods always outperform random sampling (albeit narrowly), and we find that these methods all utilize diversity to select samples - a relatively simple criterion. Our findings reveal the limitations of existing DAL methods when deployed in the wild, and present this as an important new open problem in the field.

cs.LG↗

arXiv2024

Physics-informed Inverse Design of Multi-bit Programmable Metasurfaces

Emerging reconfigurable metasurfaces offer various possibilities in 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 270o, due to the design constraints with time-intensive forward design methodologies. Here, we demonstrate a multi-bit programmable metasurface 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, our 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, our method achieves the enhanced phase tuning as large as 300o. Additionally, we experimentally validate the inverse designed programmable beam steering metasurface, which is adaptable across 1-bit, 2-bit, and tri-state coding schemes, yielding a deflection angle up to 68o and broadened steering coverage. Our demonstration provides a promising pathway for rapidly exploring advanced metasurface devices, with potentially great impact on communication and imaging technologies.

physics.optics↗

arXiv2023

Fundamental absorption bandwidth to thickness limit for transparent homogeneous layers

Past work has considered the analytic properties of the reflection coefficient for a metal-backed slab. The primary result established a fundamental relationship for the minimal layer thickness to bandwidth ratio achievable for an absorber. There has yet to be establishment of a similar relationship for non metal-backed layers, and here we present the universal result based on the Kramers-Kronig relations. Our theory is validated with transfer matrix calculations of homogeneous materials, and full-wave numerical simulations of electromagnetic metamaterials. Our results place more general fundamental limits on absorbers and thus will be important for both fundamental and applied studies.

physics.optics↗

arXiv2023

Extremely thin perfect absorber by generalized multipole bianisotropic effect

Symmetry breaking plays a crucial role in understanding the fundamental physics underlying numerous physical phenomena, including the electromagnetic response in resonators, giving rise to intriguing effects such as directional light scattering, supercavity lasing, and topologically protected states. In this work, we demonstrate that adding a small fraction of lossy metal (as low as $1\times10^{-6}$ in volume), to a lossless dielectric resonator breaks inversion symmetry thereby lifting its degeneracy, leading to a strong bianisotropic response. In the case of the metasurface composed of such resonators, this effect leads to unidirectional perfect absorption while maintaining nearly perfect reflection from the opposite direction. We have developed more general Onsager-Casimir relations for the polarizabilities of particle arrays, taking into account the contributions of quadrupoles, which shows that bianisotropy is not solely due to dipoles, but also involves high-order multipoles. Our experimental validation demonstrates an extremely thin terahertz-perfect absorber with a wavelength-to-thickness ratio of up to 25,000, where the material thickness is only 2% of the theoretical minimum thickness dictated by the fundamental limit. Our findings have significant implications for a variety of applications, including energy harvesting, thermal management, single-photon detection, and low-power directional emission.

physics.optics↗

arXiv2023

Deep Generalized Green's Functions

In this study, we address the challenge of obtaining a Green's function operator for linear partial differential equations (PDEs). The Green's function is well-sought after due to its ability to directly map inputs to solutions, bypassing the need for common numerical methods such as finite difference and finite elements methods. However, obtaining an explicit form of the Green's function kernel for most PDEs has been a challenge due to the Dirac delta function singularity present. To address this issue, we propose the Deep Generalized Green's Function (DGGF) as an alternative, which can be solved for in an efficient and accurate manner using neural network models. The DGGF provides a more efficient and precise approach to solving linear PDEs while inheriting the reusability of the Green's function, and possessing additional desirable properties such as mesh-free operation and a small memory footprint. The DGGF is compared against a variety of state-of-the-art (SOTA) PDE solvers, including direct methods, namely physics-informed neural networks (PINNs), Green's function approaches such as networks for Gaussian approximation of the Dirac delta functions (GADD), and numerical Green's functions (NGFs). The performance of all methods is compared on four representative PDE categories, each with different combinations of dimensionality and domain shape. The results confirm the advantages of DGGFs, and benefits of Generalized Greens Functions as an novel alternative approach to solve PDEs without suffering from singularities.

cs.CE↗

arXiv2023

Machine Learning for Mie-Tronics

Electromagnetic multipole expansion theory underpins nanoscale light-matter interactions, particularly within subwavelength meta-atoms, paving the way for diverse and captivating optical phenomena. While conventionally brute force optimization methods, relying on the iterative exploration of various geometries and materials, are employed to obtain the desired multipolar moments, these approaches are computationally demanding and less effective for intricate designs. In this study, we unveil the potential of machine learning for designing dielectric meta-atoms with desired multipolar moments up to the octupole terms. Specifically, we develop forward prediction models to unravel the intricate relationship between the scattering response and the topological attributes of individual meta-atoms, and an inverse design model to reconstruct scatterers with the targeted multipolar moments. Utilizing a tandem network trained to tailor dielectric meta-atoms for generating intended multipolar moments across a broad spectral range, we further demonstrate the generation of uniquely shaped meta-atoms for exciting exclusive higher order magnetic response and establishing super-scattering regime of light-matter interaction. We also illustrate the accurate prediction of electric field distributions within the given scatterer. Our versatile methodology can be readily applied to existing datasets and seamlessly integrated with various network architectures and problem domains, making it a valuable tool for the design of different platforms at nanoscale.

physics.optics↗

arXiv2022

Towards Robust Deep Active Learning for Scientific Computing

Deep learning (DL) is revolutionizing the scientific computing community. To reduce the data gap, active learning has been identified as a promising solution for DL in the scientific computing community. However, the deep active learning (DAL) literature is dominated by image classification problems and pool-based methods. Here we investigate the robustness of pool-based DAL methods for scientific computing problems (dominated by regression) where DNNs are increasingly used. We show that modern pool-based DAL methods all share an untunable hyperparameter, termed the pool ratio, denoted $γ$, which is often assumed to be known apriori in the literature. We evaluate the performance of five state-of-the-art DAL methods on six benchmark problems if we assume $γ$ is \textit{not} known - a more realistic assumption for scientific computing problems. Our results indicate that this reduces the performance of modern DAL methods and that they sometimes can even perform worse than random sampling, creating significant uncertainty when used in real-world settings. To overcome this limitation we propose, to our knowledge, the first query synthesis DAL method for regression, termed NA-QBC. NA-QBC removes the sensitive $γ$ hyperparameter and we find that, on average, it outperforms the other DAL methods on our benchmark problems. Crucially, NA-QBC always outperforms random sampling, providing more robust performance benefits.

cs.LG↗

arXiv2021

Inverse deep learning methods and benchmarks for artificial electromagnetic material design

Deep learning (DL) inverse techniques have increased the speed of artificial electromagnetic material (AEM) design and improved the quality of resulting devices. Many DL inverse techniques have succeeded on a number of AEM design tasks, but to compare, contrast, and evaluate assorted techniques it is critical to clarify the underlying ill-posedness of inverse problems. Here we review state-of-the-art approaches and present a comprehensive survey of deep learning inverse methods and invertible and conditional invertible neural networks to AEM design. We produce easily accessible and rapidly implementable AEM design benchmarks, which offers a methodology to efficiently determine the DL technique best suited to solving different design challenges. Our methodology is guided by constraints on repeated simulation and an easily integrated metric, which we propose expresses the relative ill-posedness of any AEM design problem. We show that as the problem becomes increasingly ill-posed, the neural adjoint with boundary loss (NA) generates better solutions faster, regardless of simulation constraints. On simpler AEM design tasks, direct neural networks (NN) fare better when simulations are limited, while geometries predicted by mixture density networks (MDN) and conditional variational auto-encoders (VAE) can improve with continued sampling and re-simulation.

cs.LG↗

arXiv2021

A triple-mode mid-infrared modulator for all-surface radiative thermal management

Thermal management is ubiquitous in the modern world and indispensable for a sustainable future. Radiative heat management provides unique advantages because the heat transfer can be controlled by the surface. However, different surface emissivities require different tuning strategies. Here, we demonstrate a triple-mode mid-infrared modulator that can switch between passive heating and cooling suitable for all types of object surface emissivities. The device is composed of a surface-textured infrared-semi-transparent elastomer coated with a metallic back reflector, which is biaxially strained to sequentially achieve three fundamental modes: emission, reflection, and transmission. By analyzing and optimizing the coupling between optical and mechanical properties, we achieve a performance of emittance contrast = 0.58, transmittance contrast = 0.49, and reflectance contrast = 0.39. The device can provide a new design paradigm of radiation heat regulation to develop the next generation wearable devices, robotics, and camouflage technology.

physics.app-ph↗

arXiv2020

Neural-adjoint method for the inverse design of all-dielectric metasurfaces

All-dielectric metasurfaces exhibit exotic electromagnetic responses, similar to those obtained with metal-based metamaterials. Research in all-dielectric metasurfaces currently uses relatively simple unit-cell designs, but increased geometrical complexity may yield even greater scattering states. Although machine learning has recently been applied to the design of metasurfaces with impressive results, the much more challenging task of finding a geometry that yields the desired spectra remains largely unsolved. We explore and adapt a recent deep learning approach -- termed neural-adjoint -- and find it is capable of accurately and efficiently estimating complex geometry needed to yield a targeted frequency-dependent scattering. We also show how the neural-adjoint method can intelligently grow the design search space to include designs that increasingly and accurately approximate the desired scattering response. The neural-adjoint method is not restricted to the case demonstrated and may be applied to plasmonics, photonic bandgap, and other structured material systems.

physics.optics↗

arXiv2019

Extreme Nonreciprocity with Spatio-Temporally Modulated Metasurfaces

Emerging photonic functionalities are mostly governed by the fundamental principle of Lorentz reciprocity. Lifting the constraints imposed by this principle could circumvent deleterious effects that limit the performance of photonic systems. A variety of approaches have recently been explored to break reciprocity, yet most efforts have been limited to confined photonic systems. Here, we propose and experimentally demonstrate a spatio-temporally modulated metasurface capable of extreme breakdown of Lorentz reciprocity. Through tailoring the momentum and frequency harmonic contents of the scattered waves, we achieve dynamical beam steering, reconfigurable focusing, and giant free-space optical isolation exemplifying the flexibility of our platform. We develop a generalized Bloch-Floquet theory which offers physical insights into the demonstrated extreme nonreciprocity, and its predictions are in excellent agreement with experiments. Our work opens exciting opportunities in applications where free-space nonreciprocal wave propagation is desired, including wireless communications and radiative energy transfer.

physics.app-ph↗

arXiv2012

Characterization of Surface Electromagnetic Waves and Scattering on Infrared Metamaterial Absorbers

We report, for the first time, a full experimental and computational investigation of all possible light matter interactions on the surface of an infrared metamaterial absorber (MMA). Two channels of energy dissipation - diffuse scattering and generation of surface electromagnetic waves - are quantified in terms of their impact on specular absorption. The diffuse scattering is found to play a negligible roll in the absorption process, at least for wavelengths greater than the periodicity of unit cell. In contrast, surface electromagnetic waves are found to be generated for transverse magnetic (TM) polarized light at the operational wavelength of the MMA, i.e. λ_0, and shorter wavelengths. Our computational results indicate that the highly lossy surface electromagnetic wave generated at λ_0 is responsible for the good angular dependence of absorption in TM polarization. Experimental results are supported by full wave three dimensional electromagnetic and eigenmode simulations.

physics.optics↗

arXiv2012

Extremely Sub-wavelength Planar Magnetic Metamaterials

We present highly sub-wavelength magnetic metamaterials designed for operation at radio frequencies (RFs). A dual layer design consisting of independent planar spiral elements enables experimental demonstration of a unit cell size (a) that is ~ 700 times smaller than the resonant wavelength (λ0). Simulations indicate that utilization of a conductive via to connect spiral layers permits further optimization and we achieve a unit cell that is λ0/a ~ 2000. Magnetic metamaterials are characterized by a novel time domain method which permits determination of the complex magnetic response. Numerical simulations are performed to support experimental data and we find excellent agreement. These new designs make metamaterial low frequency experimental investigations practical and suggest their use for study of magneto-inductive waves, levitation, and further enable potential RF applications.

physics.optics↗

arXiv2012

Liquid Crystal Tunable Metamaterial Perfect Absorber

We present experimental demonstration of electronically tunable metamaterial perfect absorbers in the terahertz regime. By incorporation of active liquid crystal into strategic locations within the metamaterial unit cell we are able to modify the absorption by 30 percent at 2.62 THz, as well as tune the resonant absorption over 4 percent in bandwidth. Numerical full-wave simulations match well to experiments and clarify the underlying mechanism, i.e. a simultaneous tuning of both the electric and magnetic response that allows for the preservation of the resonant absorption. These results show that the fundamental light interactions of surfaces can be dynamically controlled by all-electronic means and provide a path forward for realization of novel applications.

physics.optics↗

arXiv2012

Loss compensation in Metamaterials through embedding of active transistor based negative differential resistance circuits

This paper presents an all-electronic approach for loss compensation in metamaterials. This is achieved by embedding active-transistors based negative differential resistance (NDR) circuits in each unit cell of the metamaterial lattice. NDR circuits provide tunable loss compensation over a broad frequency range limited only by the maximum operating frequency of transistors that is reaching terahertz values in newer semiconductor processes. Design, simulation and experimental results of metamaterials composed of split ring resonators (SRR) with and without loss compensation circuits are presented.

physics.optics↗