arXiv subjects

Tianyue Li

Publications and source records attributed to Tianyue Li.

8 recordsLinked to original sources

arXiv2026

Encoding Propagation Invariance into Light

Diffraction governs the axial evolution of optical fields, whereas conventional holographic synthesis primarily controls their transverse structure. Here we add axial diffraction management as an additional design freedom to the transverse_field programmability of holography, enabling the transverse optical function and its diffraction-driven axial evolution to be co_designed. By incorporating established propagation_invariant dynamics into computer_generated hologram and meta_hologram synthesis, we realize task_selectable axial responses in user_defined monochromatic, full-colour and vectorial fields. On a spatial light modulator, the same scalar user_defined field is configured either for a rapidly evolving 1.2_cm depth of field or for an approximately 75_cm propagation_invariant range. Millimetre_scale metasurfaces further enable metre_scale refocusing_free full_colour projection and vectorial colour fields with polarization textures preserved over more than 30 cm. This transverse_axial co_design framework extends holographic field synthesis beyond transverse programmability, providing a broadly compatible route towards task_configurable optical systems and compact multidimensional photonics.

physics.optics↗

arXiv2026

Engineering Photoluminescence with Mie Voids

Spontaneous emission, as a fundamental radiative process and a versatile information carrier, plays a vital role in light-emitting devices, optical information modulation and encryption, super-resolution fluorescence imaging. Engineering the photonic environment surrounding photon emitters enables control over their emission properties. However, simultaneously achieving precise engineering of both excitation enhancement and quantum-yield modulation at the nanoscale remains elusive, highlighting substantial room for advancing the precise orchestrating of photoluminescence. Here, we introduce silicon Mie voids - air-defined cavities that invert the conventional solid-particle geometry - to achieve independent tuning of photoluminescence within a single subwavelength unit, while minimizing optical losses. Full-wave simulations and experiments on both gradient and uniform Mie-void arrays jointly validate this quantitative framework for spontaneous emission tuning, which disentangles excitation enhancement arising from local field confinement in air and quantum-yield enhancement resulting from strengthened emitter-resonator coupling, while confirming the accelerated radiative decay enabled by the modified optical LDOS. Leveraging this flexible mechanism, we realize a multimodal nanophotonic pattern with near-diffraction-limited pixels that encode the EPFL logo in the bright field and the SJTU logo in both dark field and photoluminescence maps. These results establish Mie voids as a powerful platform for high-density multimodal encrypted displays and open new avenues for advancing state-of-the-art nanophotonic devices.

physics.optics↗

arXiv2026

A scalable Ewald-free BIE framework for periodic Stokes flow via hierarchical proxy sums

Particulate Stokes flow in confined, periodic geometries underlies a broad class of problems in biophysics, microfluidics, and the rheology of complex fluids. Boundary integral equation (BIE) methods are a natural tool for such problems, but existing periodization schemes rely either on periodic Green's functions, which are restrictive for complex confining geometries, or on free-space schemes that solve auxiliary proxy strengths alongside the surface densities in an extended linear system whose cost scales unfavorably in three dimensions. We present a BIE framework for three-dimensional particulate Stokes flow in periodic pipes with circular cross-sections, wall-bounded doubly-periodic, and triply-periodic geometries that uses only the free-space Green's function and avoids both Ewald summation and the extended linear system. Proxy sources placed on equivalent surfaces of the kernel-independent FMM (KIFMM) form the auxiliary basis, and contributions from far image boxes are captured by a hierarchical proxy sum made absolutely convergent by a net-force-zero compatibility condition. The resulting periodization precomputation depends only on the periodic-box geometry, independent of the kernel and of the surfaces inside the box, and is reused verbatim across the Stokeslet, stresslet, and rotlet. Combined with high-order adaptive surface discretizations, the method achieves high-order accuracy at $\mathcal{O}(N)$ cost with a single layer of image boxes in the near field. Numerical examples on dense polydisperse suspensions with thousands of particles and on flow through complex periodic channels, together with strong and weak scaling studies, demonstrate efficient performance on systems with millions of degrees of freedom on distributed-memory architectures.

physics.flu-dyn↗

arXiv2026

Lightweight Time Series Data Valuation on Time Series Foundation Models via In-Context Finetuning

Time series foundation models (TSFMs) have demonstrated increasing capabilities due to their extensive pretraining on large volumes of diverse time series data. Consequently, the quality of time series data is crucial to TSFM performance, rendering an accurate and efficient data valuation of time series for TSFMs indispensable. However, traditional data valuation methods, such as influence functions, face severe computational bottlenecks due to their poor scalability with growing TSFM model sizes and often fail to preserve temporal dependencies. In this paper, we propose LTSV, a Lightweight Time Series Valuation on TSFMS via in-context finetuning. Grounded in the theoretical evidence that in-context finetuning approximates the influence function, LTSV estimates a sample's contribution by measuring the change in context loss after in-context finetuning, leveraging the strong generalization capabilities of TSFMs to produce robust and transferable data valuations. To capture temporal dependencies, we introduce temporal block aggregation, which integrates per-block influence scores across overlapping time windows. Experiments across multiple time series datasets and models demonstrate that LTSV consistently provides reliable and strong valuation performance, while maintaining manageable computational requirements. Our results suggest that in-context finetuning on time series foundation models provides a practical and effective bridge between data attribution and model generalization in time series learning.

cs.LG↗

arXiv2025

Scalable Generalized Meta-Spanners Enabling Parallel Multitasking Optical Manipulation

Optical manipulation techniques offer exceptional contactless control but are fundamentally limited in their ability to perform parallel multitasking. To achieve high-density, versatile manipulation with subwavelength photonic devices, it is essential to sculpt light fields in multiple dimensions. Here, we overcome this challenge by introducing generalized optical meta-spanners (GOMSs) based on metasurfaces. Relying on complex-amplitude modulation, this platform generates lens-free, customizable optical fields that suppress diffractive losses. As a result, several advanced functionalities are simultaneously achieved, including longitudinally varying manipulation and in-plane spanner arrays, which outperforms the same operations realized by conventional donut-shaped orbital flows. Furthermore, the particle dynamics is reconfigurable simply by switching the input and output polarizations, facilitating robust multi-channel control. We experimentally validate the proposed approach by demonstrating single-particle dynamics and the parallel manipulation of particle ensembles, revealing exceptional stability for multitasking operations. These results demonstrate an ultracompact platform scalable to a much larger number of optical spanners, advancing metadevices from wavefront sculptors to particle manipulators. We envision that the GOMS will catalyze innovations in cross-disciplinary fields such as targeted drug delivery and cell-level biomechanics.

physics.optics↗

arXiv2025

Boundary integral equation analysis for spheroidal suspensions

In this work, we provide a fast, spectrally accurate method for the evaluation of boundary integral operators (BIOs) on a suspension of prolate and oblate spheroids. We first derive formulas for the standard layer potential operators for the Laplace equation applied to an expansion of the integral densities in the appropriate spheroidal harmonic basis. These then lead to analytical expressions in solid harmonics that allow spectrally accurate evaluation of near-field particle interactions. Finally, a standard quadrature scheme is used to evaluate smooth, far-field interactions; these are then accelerated using the fast multipole method. Through a number of numerical test cases, we verify the accuracy and efficiency of our BIO evaluation framework for dense, polydisperse suspensions of spheroids. Through the use of standard formulas linking Stokes and Laplace potentials, we show our scheme can be readily applied to problems involving particulate suspension flows. For both Laplace and Stokes, our method allows us to evaluate BIOs for suspensions up to hundreds of particles on a single processor.

math.NA↗

arXiv2024

On-demand Quick Metasurface Design with Neighborhood Attention Transformer

Metasurfaces are reshaping traditional optical paradigms and are increasingly required in complex applications that demand substantial computational resources to numerically solve Maxwell's equations-particularly for large-scale systems, inhomogeneous media, and densely packed metadevices. Conventional forward design using electromagnetic solvers is based on specific approximations, which may not effectively address complex problems. In contrast, existing inverse design methods are a stepwise process that is often time-consuming and involves repetitive computations. Here, we present an inverse design approach utilizing a surrogate Neighborhood Attention Transformer, MetaE-former, to predict the performance of metasurfaces with ultrafast speed and high accuracy. This method achieves global solutions for hundreds of nanostructures simultaneously, providing up to a 250,000-fold speedup compared with solving for individual meta-atoms based on the FDTD method. As examples, we demonstrate a binarized high-numerical-aperture (about 1.31) metalens and several optimized structured-light meta-generators. Our method significantly improves the beam shaping adaptability with metasurfaces and paves the way for fast designing of large-scale metadevices for shaping extreme light fields with high accuracy.

physics.optics↗

arXiv2022

Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarks

Establishing open and general benchmarks has been a critical driving force behind the success of modern machine learning techniques. As machine learning is being applied to broader domains and tasks, there is a need to establish richer and more diverse benchmarks to better reflect the reality of the application scenarios. Graph learning is an emerging field of machine learning that urgently needs more and better benchmarks. To accommodate the need, we introduce Graph Learning Indexer (GLI), a benchmark curation platform for graph learning. In comparison to existing graph learning benchmark libraries, GLI highlights two novel design objectives. First, GLI is designed to incentivize \emph{dataset contributors}. In particular, we incorporate various measures to minimize the effort of contributing and maintaining a dataset, increase the usability of the contributed dataset, as well as encourage attributions to different contributors of the dataset. Second, GLI is designed to curate a knowledge base, instead of a plain collection, of benchmark datasets. We use multiple sources of meta information to augment the benchmark datasets with \emph{rich characteristics}, so that they can be easily selected and used in downstream research or development. The source code of GLI is available at \url{https://github.com/Graph-Learning-Benchmarks/gli}.

cs.LG↗