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Researcher profile

Yuntian Chen

Yuntian Chen contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Machine Learningphysics.opticsApplicationsArtificial IntelligenceComputation and Languagemath-phmath.MPphysics.data-anCryptography and Securityeess.ASeess.IVeess.SPNeural and Evolutionary Computingphysics.app-phSound

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Published work

23 published item(s)

preprint2026arXiv

Advanced Long-term Earth System Forecasting

Reliable long-term forecasting of Earth system dynamics is fundamentally limited by instabilities in current artificial intelligence (AI) models during extended autoregressive simulations. These failures often originate from inherent spectral bias, leading to inadequate representation of critical high-frequency, small-scale processes and subsequent uncontrolled error amplification. Inspired by the nested grids in numerical models used to resolve small scales, we present TritonCast. At the core of its design is a dedicated latent dynamical core, which ensures the long-term stability of the macro-evolution at a coarse scale. An outer structure then fuses this stable trend with fine-grained local details. This design effectively mitigates the spectral bias caused by cross-scale interactions. In atmospheric science, it achieves state-of-the-art accuracy on the WeatherBench 2 benchmark while demonstrating exceptional long-term stability: executing year-long autoregressive global forecasts and completing multi-year climate simulations that span the entire available $2500$-day test period without drift. In oceanography, it extends skillful eddy forecast to $120$ days and exhibits unprecedented zero-shot cross-resolution generalization. Ablation studies reveal that this performance stems from the synergistic interplay of the architecture's core components. TritonCast thus offers a promising pathway towards a new generation of trustworthy, AI-driven simulations. This significant advance has the potential to accelerate discovery in climate and Earth system science, enabling more reliable long-term forecasting and deeper insights into complex geophysical dynamics.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Context-Alignment: Activating and Enhancing LLM Capabilities in Time Series

Recently, leveraging pre-trained Large Language Models (LLMs) for time series (TS) tasks has gained increasing attention, which involves activating and enhancing LLMs' capabilities. Many methods aim to activate LLMs' capabilities based on token-level alignment, but overlook LLMs' inherent strength in natural language processing -- \textit{their deep understanding of linguistic logic and structure rather than superficial embedding processing.} We propose Context-Alignment (CA), a new paradigm that aligns TS with a linguistic component in the language environments familiar to LLMs to enable LLMs to contextualize and comprehend TS data, thereby activating their capabilities. Specifically, such context-level alignment comprises structural alignment and logical alignment, which is achieved by Dual-Scale Context-Alignment GNNs (DSCA-GNNs) applied to TS-language multimodal inputs. Structural alignment utilizes dual-scale nodes to describe hierarchical structure in TS-language, enabling LLMs to treat long TS data as a whole linguistic component while preserving intrinsic token features. Logical alignment uses directed edges to guide logical relationships, ensuring coherence in the contextual semantics. Following the DSCA-GNNs framework, we propose an instantiation method of CA, termed Few-Shot prompting Context-Alignment (FSCA), to enhance the capabilities of pre-trained LLMs in handling TS tasks. FSCA can be flexibly and repeatedly integrated into various layers of pre-trained LLMs to improve awareness of logic and structure, thereby enhancing performance. Extensive experiments show the effectiveness of FSCA and the importance of Context-Alignment across tasks, particularly in few-shot and zero-shot forecasting, confirming that Context-Alignment provides powerful prior knowledge on context. The code is open-sourced at https://github.com/tokaka22/ICLR25-FSCA.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

RETO: A Rotary-Enhanced Transformer Operator for High-Fidelity Prediction of Automotive Aerodynamics

Rapid aerodynamic evaluation is crucial for modern vehicle design, yet existing neural operators struggle to capture intricate spatial correlations. We propose the rotary-enhanced transformer operator (RETO), a novel neural solver featuring a dual-stage spatial awareness mechanism: sinusoidal-cosine encodings for global referencing and rotary positional encodings (RoPE) for relative displacements. RoPE encodes spatial relations via unitary rotations, enforcing translation invariance and enhancing local gradient resolution. RETO is validated on ShapeNet and the high-fidelity DrivAerML benchmark. On ShapeNet, RETO achieves a relative $L_2$ error of 0.063, outperforming RegDGCNN at 0.125 and representing a 16\% improvement over the Transolver baseline, which yields an error of 0.075. These performance gains are further amplified on the DrivAerML dataset, where RETO achieves relative $L_2$ errors of 0.089 for surface pressure and 0.097 for velocity. In comparison, Transolver results in errors of 0.116 and 0.121 for the same metrics, indicating that RETO achieves precision enhancements of 23\% and 19\%, respectively. For comprehensive comparison, the surface pressure and velocity errors for AB-UBT are 0.102 and 0.124, while RegDGCNN yields 0.235 and 0.312, respectively. Information-theoretical analysis shows that the entropy peak of RETO at 0.35 is significantly lower than that of Transolver at 0.75 under $10^4$ resolution, indicating a focused attentional mechanism capable of preserving localized gradients against global diffusion.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

SuperEar: Eavesdropping on Mobile Voice Calls via Stealthy Acoustic Metamaterials

Acoustic eavesdropping is a privacy risk, but existing attacks rarely work in real outdoor situations where people make phone calls on the move. We present SuperEar, the first portable system that uses acoustic metamaterials to reliably capture conversations in these scenarios. We show that the threat is real as a practical prototype can be implemented to enhance faint signals, cover the full range of speech with a compact design, and reduce noise and distortion to produce clear audio. We show that SuperEar can be implemented from low-cost 3D-printed parts and off-the-shelf hardware. Experimental results show that SuperEar can recover phone call audio with a success rate of over 80% at distances of up to 4.6 m - more than twice the range of previous approaches. Our findings highlight a new class of privacy threats enabled by metamaterial technology that requires attention.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Efficient and accurate numerical-projection of electromagnetic multipoles for scattering objects

In this paper, we develop an efficient and accurate procedure of electromagnetic multipole decomposition by using the Lebedev and Gaussian quadrature methods to perform the numerical integration. Firstly, we briefly review the principles of multipole decomposition, highlighting two numerical projection methods including surface and volume integration. Secondly, we discuss the Lebedev and Gaussian quadrature methods, provide a detailed recipe to select the quadrature points and the corresponding weighting factor, and illustrate the integration accuracy and numerical efficiency (that is, with very few sampling points) using a unit sphere surface and regular tetrahedron. In the demonstrations of an isotropic dielectric nanosphere, a symmetric scatterer, and an anisotropic nanosphere, we perform multipole decomposition and validate our numerical projection procedure. The obtained results from our procedure are all consistent with those from Mie theory, symmetry constraints, and finite element simulations.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Multi-spatial Multi-temporal Air Quality Forecasting with Integrated Monitoring and Reanalysis Data

Accurate air quality forecasting is crucial for public health, environmental monitoring and protection, and urban planning. However, existing methods fail to effectively utilize multi-scale information, both spatially and temporally. Spatially, there is a lack of integration between individual monitoring stations and city-wide scales. Temporally, the periodic nature of air quality variations is often overlooked or inadequately considered. To address these limitations, we present a novel Multi-spatial Multi-temporal air quality forecasting method based on Graph Convolutional Networks and Gated Recurrent Units (M2G2), bridging the gap in air quality forecasting across spatial and temporal scales. The proposed framework consists of two modules: Multi-scale Spatial GCN (MS-GCN) for spatial information fusion and Multi-scale Temporal GRU(MT-GRU) for temporal information integration. In the spatial dimension, the MS-GCN module employs a bidirectional learnable structure and a residual structure, enabling comprehensive information exchange between individual monitoring stations and the city-scale graph. Regarding the temporal dimension, the MT-GRU module adaptively combines information from different temporal scales through parallel hidden states. Leveraging meteorological indicators and four air quality indicators, we present comprehensive comparative analyses and ablation experiments, showcasing the higher accuracy of M2G2 in comparison to nine currently available advanced approaches across all aspects. The improvements of M2G2 over the second-best method on RMSE of the 24h/48h/72h are as follows: PM2.5: (7.72%, 6.67%, 10.45%); PM10: (6.43%, 5.68%, 7.73%); NO2: (5.07%, 7.76%, 16.60%); O3: (6.46%, 6.86%, 9.79%). Furthermore, we demonstrate the effectiveness of each module of M2G2 by ablation study.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

AutoKE: An automatic knowledge embedding framework for scientific machine learning

Imposing physical constraints on neural networks as a method of knowledge embedding has achieved great progress in solving physical problems described by governing equations. However, for many engineering problems, governing equations often have complex forms, including complex partial derivatives or stochastic physical fields, which results in significant inconveniences from the perspective of implementation. In this paper, a scientific machine learning framework, called AutoKE, is proposed, and a reservoir flow problem is taken as an instance to demonstrate that this framework can effectively automate the process of embedding physical knowledge. In AutoKE, an emulator comprised of deep neural networks (DNNs) is built for predicting the physical variables of interest. An arbitrarily complex equation can be parsed and automatically converted into a computational graph through the equation parser module, and the fitness of the emulator to the governing equation is evaluated via automatic differentiation. Furthermore, the fixed weights in the loss function are substituted with adaptive weights by incorporating the Lagrangian dual method. Neural architecture search (NAS) is also introduced into the AutoKE to select an optimal network architecture of the emulator according to the specific problem. Finally, we apply transfer learning to enhance the scalability of the emulator. In experiments, the framework is verified by a series of physical problems in which it can automatically embed physical knowledge into an emulator without heavy hand-coding. The results demonstrate that the emulator can not only make accurate predictions, but also be applied to similar problems with high efficiency via transfer learning.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Design optimization of band-pass filter based on parity-time symmetry coupled-resonant

Integrated optical filter based on microring resonators plays a critical role in many applications, ranging from wavelength division multiplexing and switching to channel routing. Bandwidth tunable filters are capable of meeting the on-demand flexible operations in complex situations, due to their advantages of scalability, multi-function, and energy-saving. It has been investigated recently that parity-time (PT) symmetry coupled-resonant systems can be applied to the bandwidth-tunable filters. However, due to the trade-off between the bandwidth-tunable contrast ratio and insertion loss of system, the bandwidth-tunable contrast ratio of this method is severely limited. Here, the bandwidth-tunable contrast ratio is defined as the maximum bandwidth divided by the minimum bandwidth. In this work, we show that high bandwidth-tunable contrast ratio and low insertion loss of system can be achieved simultaneously by increasing the coupling strength between the input port and the resonant. System characterizations under different coupling states reveal that the low insertion loss can be obtained when the system initially operates at the over-coupling condition. A high bandwidth-tunable contrast ratio PT-symmetry band-pass filter with moderate insertion loss is shown on the Silicon platform. Our scheme provides an effective method to reduce the insertion loss of on-chip tunable filters, which is also applicable to the high-order cascaded microring systems.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Integration of knowledge and data in machine learning

Scientific research's mandate is to comprehend and explore the world, as well as to improve it based on experience and knowledge. Knowledge embedding and knowledge discovery are two significant methods of integrating knowledge and data. Through knowledge embedding, the barriers between knowledge and data can be eliminated, and machine learning models with physical common sense can be established. Meanwhile, humans' understanding of the world is always limited, and knowledge discovery takes advantage of machine learning to extract new knowledge from observations. Knowledge discovery can not only assist researchers to better grasp the nature of physics, but it can also support them in conducting knowledge embedding research. A closed loop of knowledge generation and usage are formed by combining knowledge embedding with knowledge discovery, which can improve the robustness and accuracy of models and uncover previously unknown scientific principles. This study summarizes and analyzes extant literature, as well as identifies research gaps and future opportunities.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Interpretable machine learning optimization (InterOpt) for operational parameters: a case study of highly-efficient shale gas development

An algorithm named InterOpt for optimizing operational parameters is proposed based on interpretable machine learning, and is demonstrated via optimization of shale gas development. InterOpt consists of three parts: a neural network is used to construct an emulator of the actual drilling and hydraulic fracturing process in the vector space (i.e., virtual environment); the Sharpley value method in interpretable machine learning is applied to analyzing the impact of geological and operational parameters in each well (i.e., single well feature impact analysis); and ensemble randomized maximum likelihood (EnRML) is conducted to optimize the operational parameters to comprehensively improve the efficiency of shale gas development and reduce the average cost. In the experiment, InterOpt provides different drilling and fracturing plans for each well according to its specific geological conditions, and finally achieved an average cost reduction of 9.7% for a case study with 104 wells.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Nonreciprocal light propagation induced by a subwavelength spinning cylinder

Nonreciprocal optical devices have broad applications in light manipulations for communications and sensing. Non-magnetic mechanisms of optical nonreciprocity are highly desired for high-frequency on-chip applications. Here, we investigate the nonreciprocal properties of light propagation in a dielectric waveguide induced by a subwavelength spinning cylinder. We find that the chiral modes of the cylinder can give rise to unidirectional coupling with the waveguide via the transverse spin-orbit interaction, leading to different transmissions for guided wave propagating in opposite directions and thus optical isolation. We reveal the dependence of the nonreciprocal properties on various system parameters including mode order, spinning speed, and coupling distance. The results show that higher-order chiral modes and larger spinning speed generally give rise to stronger nonreciprocity, and there exists an optimal cylinder-waveguide coupling distance where the optical isolation reaches the maximum. Our work contributes to the understanding of nonreciprocity in subwavelength moving structures and can find applications in integrated photonic circuits, topological photonics, and novel metasurfaces.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Any equation is a forest: Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE)

Partial differential equations (PDEs) are concise and understandable representations of domain knowledge, which are essential for deepening our understanding of physical processes and predicting future responses. However, the PDEs of many real-world problems are uncertain, which calls for PDE discovery. We propose the symbolic genetic algorithm (SGA-PDE) to discover open-form PDEs directly from data without prior knowledge about the equation structure. SGA-PDE focuses on the representation and optimization of PDE. Firstly, SGA-PDE uses symbolic mathematics to realize the flexible representation of any given PDE, transforms a PDE into a forest, and converts each function term into a binary tree. Secondly, SGA-PDE adopts a specially designed genetic algorithm to efficiently optimize the binary trees by iteratively updating the tree topology and node attributes. The SGA-PDE is gradient-free, which is a desirable characteristic in PDE discovery since it is difficult to obtain the gradient between the PDE loss and the PDE structure. In the experiment, SGA-PDE not only successfully discovered nonlinear Burgers' equation, Korteweg-de Vries (KdV) equation, and Chafee-Infante equation, but also handled PDEs with fractional structure and compound functions that cannot be solved by conventional PDE discovery methods.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

DNN2LR: Automatic Feature Crossing for Credit Scoring

Credit scoring is a major application of machine learning for financial institutions to decide whether to approve or reject a credit loan. For sake of reliability, it is necessary for credit scoring models to be both accurate and globally interpretable. Simple classifiers, e.g., Logistic Regression (LR), are white-box models, but not powerful enough to model complex nonlinear interactions among features. Fortunately, automatic feature crossing is a promising way to find cross features to make simple classifiers to be more accurate without heavy handcrafted feature engineering. However, credit scoring is usually based on different aspects of users, and the data usually contains hundreds of feature fields. This makes existing automatic feature crossing methods not efficient for credit scoring. In this work, we find local piece-wise interpretations in Deep Neural Networks (DNNs) of a specific feature are usually inconsistent in different samples, which is caused by feature interactions in the hidden layers. Accordingly, we can design an automatic feature crossing method to find feature interactions in DNN, and use them as cross features in LR. We give definition of the interpretation inconsistency in DNN, based on which a novel feature crossing method for credit scoring prediction called DNN2LR is proposed. Apparently, the final model, i.e., a LR model empowered with cross features, generated by DNN2LR is a white-box model. Extensive experiments have been conducted on both public and business datasets from real-world credit scoring applications. Experimental shows that, DNN2LR can outperform the DNN model, as well as several feature crossing methods. Moreover, comparing with the state-of-the-art feature crossing methods, i.e., AutoCross, DNN2LR can accelerate the speed for feature crossing by about 10 to 40 times on datasets with large numbers of feature fields.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

DNN2LR: Interpretation-inspired Feature Crossing for Real-world Tabular Data

For sake of reliability, it is necessary for models in real-world applications to be both powerful and globally interpretable. Simple classifiers, e.g., Logistic Regression (LR), are globally interpretable, but not powerful enough to model complex nonlinear interactions among features in tabular data. Meanwhile, Deep Neural Networks (DNNs) have shown great effectiveness for modeling tabular data, but is not globally interpretable. In this work, we find local piece-wise interpretations in DNN of a specific feature are usually inconsistent in different samples, which is caused by feature interactions in the hidden layers. Accordingly, we can design an automatic feature crossing method to find feature interactions in DNN, and use them as cross features in LR. We give definition of the interpretation inconsistency in DNN, based on which a novel feature crossing method called DNN2LR is proposed. Extensive experiments have been conducted on four public datasets and two real-world datasets. The final model, i.e., a LR model empowered with cross features, generated by DNN2LR can outperform the complex DNN model, as well as several state-of-the-art feature crossing methods. The experimental results strongly verify the effectiveness and efficiency of DNN2LR, especially on real-world datasets with large numbers of feature fields.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Arbitrary Polarization-Independent Backscattering or Reflection by Rotationally-Symmetric Reciprocal Structures

We study the backward scatterings of plane waves by reciprocal scatterers and reveal that $n$-fold ($n\geq3$) rotation symmetry is sufficient to secure invariant backscattering for arbitrarily-polarized incident plane waves. It is further demonstrated that the same principle is also applicable for infinite periodic structures in terms of reflection, which simultaneously guarantees the transmission invariance if there are neither Ohmic losses nor extra diffraction channels. At the presence of losses, extra reflection symmetries (with reflection planes either parallel or perpendicular to the incident direction) can be incorporated to ensure simultaneously the invariance of transmission and absorption. The principles we have revealed are protected by fundamental laws of reciprocity and parity conservation, which are fully independent of the optical or geometric parameters of the photonic structures. The optical invariance obtained is intrinsically robust against perturbations that preserve reciprocity and the geometric symmetries, which could be widely employed for photonic applications that require stable backscatterings or reflections.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Electromagnetic Duality Protected Scattering Properties of Nonmagnetic Particles

Optical properties of nonmagnetic structures that support artificial optically-induced magnetic responses have recently attracted surging interest. Here we conduct symmetry-dictated investigations into scattering properties of nonmagnetic particles from perspectives of electromagnetic duality with discrete geometric rotations. For arbitrary scattering configurations, we reveal that far-field scattering patterns are invariant under duality transformations, which in particular means that scattering patterns of self-dual clusters with random particle distributions are polarization independent. Based on this revelation, it is further discovered that scattering bodies of combined duality-(n-fold) rotation symmetry, for any polarizations of incident waves, exhibit also n-fold rotationally symmetric scattering patterns with zero backward components, satisfying the first Kerker condition automatically. We employ both coupled dipole theory and full numerical simulations to demonstrate those scattering properties, solely based upon nonmagnetic core-shell particles that support optically-induced dipolar resonances. Those substantiated scattering properties are fully induced by fundamental symmetry principles, and thus can survive any non-symmetry-breaking perturbations, which may find applications in a wide range of optical devices that require intrinsically robust functionalities.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Global Mie Scattering

In various subdisciplines of optics and photonics, Mie theory has been serving as a fundamental language and play indispensable roles widely. Conventional studies related to Mie scattering largely focus on local properties such as differential cross sections and angular polarization distributions. Though spatially integrated features of total cross sections in terms of both scattering and absorption are routine for investigations, they are intrinsically dependent on the specific morphologies of both the scattering bodies and the incident waves, consequently manifesting no sign of global invariance. Here we propose global Mie scattering theory to explore topological invariants for characterizations of scatterings by any obstacles of arbitrarily structured or polarized coherent light. It is revealed that, independent of distributions and interactions among the scattering bodies of arbitrary geometric and optical parameters, in the far field inevitably there are directions where the scatterings are either zero or circularly polarized. Furthermore, for each such singular direction we can assign a half-integer index and the index sum of all those directions are bounded to be a global topological invariant of $2$. The global Mie theory we propose, which is mathematically simple but conceptually penetrating, can render new perspectives for light scattering and topological photonics in both linear and nonlinear regimes, and would potentially shed new light on the scattering of acoustic and matter waves of various forms.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Ideal Kerker scattering by homogeneous spheres: the role of gain or loss

We reexamine a recent work [Phys. Rev. Lett. \textbf{125}, 073205 (2020)] that investigates how the optical gain or loss (characterized by isotropic complex refractive indexes) influences the ideal Kerker scattering of exactly zero backward scattering. There it has been rigourously proved that, for non-magnetic homogeneous spheres with incident plane waves, either gain or loss prohibits such ideal Kerker scattering, provided that only electric and magnetic multipoles of a specific order are present and contributions from other multipoles can all be made precisely zero. Here we reveal that, when two multipoles of a fixed order are perfectly matched in terms of both phase and magnitude, multipoles of at least the next two orders cannot possibly be tuned to be all precisely zero or even perfectly matched, and consequently cannot directly produce ideal Kerker scattering. Moreover, we further demonstrate that, when multipoles of different orders are simultaneously taken into consideration, the loss or gain can serve as a helpful rather than harmful contributing factor, for the eliminations of backward scattering.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Physics-constrained indirect supervised learning

This study proposes a supervised learning method that does not rely on labels. We use variables associated with the label as indirect labels, and construct an indirect physics-constrained loss based on the physical mechanism to train the model. In the training process, the model prediction is mapped to the space of value that conforms to the physical mechanism through the projection matrix, and then the model is trained based on the indirect labels. The final prediction result of the model conforms to the physical mechanism between indirect label and label, and also meets the constraints of the indirect label. The present study also develops projection matrix normalization and prediction covariance analysis to ensure that the model can be fully trained. Finally, the effect of the physics-constrained indirect supervised learning is verified based on a well log generation problem.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Scattering and absorption invariance of nonmagnetic particles under duality transformations

We revisit the total scatterings (in terms of extinction, scattering and absorption cross sections) by arbitrary clusters of nonmagnetic particles that support optically-induced magnetic responses. Our reexamination is conducted from the perspective of the electromagnetic duality symmetry, and it is revealed that all total scattering properties are invariant under duality transformations. This secures that for self-dual particle clusters, the total scattering properties are polarization independent for any fixed incident direction; while for non-self-dual particle clusters, two scattering configurations that are connected to each other through a duality transformation would exhibit identical scattering properties. This electromagnetic duality induced invariance is irrelevant to specific particle distributions or wave incident directions, which is illustrated for both random and periodic clusters.

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preprint2020arXiv

Symmetry Protected Invariant Scattering Properties for Arbitrary Polarizations

Polarization independent Mie scattering of building blocks is foundational for constructions of optical systems with robust functionalities. Conventional studies for such polarization independence are generally restricted to special states of either linear or circular polarizations, widely neglecting elliptically-polarized states that are generically present in realistic applications. Here we present a comprehensive recipe to achieve invariant scattering properties (including extinction, scattering and absorption) for arbitrary polarizations, requiring only rotation symmetry and absence of optical activities. It is discovered that sole rotation symmetries can effectively decouple the two scattering channels that originate from the incident circularly polarized waves of opposite handedness, leading to invariance of all scattering properties for any polarizations on the same latitude circle of the Poincaré sphere. Further incorporations of extra inversion or mirror symmetries would eliminate the optical activities and thus ensure scattering property invariance for arbitrary polarizations. In sharp contrast to previous investigations that rely heavily on complicated algebraic formulas, our arguments are fully intuitive and geometric, bringing to surface the essential physical principles rather than obscuring them. The all-polarization invariance we reveal is induced by discrete spatial symmetries of the scattering configurations, underlying which there are functioning laws of reciprocity and conservation of parity and helicity. This symmetry-protected intrinsic invariance is robust against any symmetry-preserving perturbations, which may render extra flexibilities for designing optical devices with stable functionalities.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Line Singularities and Hopf Indices of Electromagnetic Multipoles

Electromagnetic multipoles can be continuously mapped to tangent vectors on the momentum sphere, the topology of which guarantees the existence of isolated singularities. For pure (real or imaginary) vectors, those singularities correspond to zeros of tangent fields, which can be classified by integer Poincaré indices. Nevertheless, electromagnetic fields are generally complex vectors, a comprehensive characterization of which requires the introduction of line fields and line singularities categorized by half-integer Hopf indices. Here we explore complex vectorial electromagnetic multipoles from the perspective of line singularities, focusing on the special case of polarization line field. Similar to the case of pure vectors, the Poincaré-Hopf theorem forces the index sum of all line singularities to be $2$, irrespective of the specific multipolar compositions. With this multipolar insight, we further unveil the underlying structures of radiative circularly-polarized Bloch modes of photonic crystal slabs, revealing their topological origins with line singularities of Hopf indices. Our work has established subtle connections between three seemingly unrelated but sweeping physical entities (line singularities of Hopf indices, electromagnetic multipoles, and Bloch modes), which can nourish new frames of visions and applications fertilizing many related fields.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Scattering Activities Bounded by Reciprocity and Parity Conservation

Scattering activities are generally manifest through different optical responses of scattering bodies to circularly polarized light of opposite handedness. Similar to the ubiquitous roles played by scattering theory across different branches of photonics, scattering activities can serve as a fundamental concept to clarify underlying mechanisms of various chiroptical effects, both within and beyond scattering systems. In this work we investigate scattering activities for reciprocal systems that exhibit various geometric symmetries but are intrinsically achiral. We reveal how scattering activities are generally bounded by reciprocity and parity conservation, demonstrating that though extinction activity is usually eliminated by symmetry, scattering activities in forms of distinct absorptions, scatterings or angular scattering patterns can more widely emerge. Since our analyses are solely based on fundamental laws of reciprocity and parity conservation, regardless of geometric and optical parameters of the scattering systems studied, the principles revealed are generically applicable. The intuitive and pictorial framework we have established is beyond any specific coupling models, able to reveal hidden connections between seemingly unrelated chiral manifestations, and thus more accessible for a unified understanding of various chiroptical effects.

0 citations0 reviewsNo code linkedOpen access