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Zhijie Li

Publications and source records attributed to Zhijie Li.

At least 19 recordsLinked to original sources

The number of limit cycles of piecewise linear Liénard systems

For the planar Liénard differential system $\dot{x}=F(x)-y$, $\dot{y}=x$, where $F(x)$ is a piecewise linear function, Tonnelier (SIAM J. Appl. Math., 2002) conjectured that the maximum number of limit cycles of the system is $n$ when $F(x)$ has $n$ fold points and no jump points, and $2n$ when $F(x)$ has $n$ jump points and no fold points. This conjecture was confirmed by Llibre et al. (J. Nonlinear Sci., 2015) (resp. Chen et al. (J. London Math. Soc., 2026a)) when $F(x)$ has one fold point and no jump points (resp. two fold points). More recently, Chen et al. (J. London Math. Soc., 2026b) proved that the conjecture is correct when $F(x)$ has no fold points and one jump point. All other cases remain open. Here we verify that the lower bound for the maximum number of limit cycles of the system can be $n$ when $F(x)$ has only $n$ fold points, and $2n$ when $F(x)$ has only $n$ jump points, thereby confirming the lower bound part of Tonnelier's conjecture. Moreover, when $F(x)$ has $m$ jump points and $n-m$ fold points, $0\le m\le n$, we also show that the system can have $n+m=(n-m)+2m$ limit cycles. In addition, a complete classification of the {dynamics} near infinity for this class of systems is provided.

math.DS

Cyclicity of lips and center-focus problem near infinity

In this work, we discover an intriguing Liénard system which is integrable near infinity, integrable near zero and having Ilyashenko-Kotova lips in between. Moreover, we give a necessary and sufficient condition of polynomial Liénard system with center at infinity. In a different direction we improve a lower bound on Hilbert number for Liénard systems. The improvement is due to development of Brudnyi method of calculation of cyclicity of centers near infinity and near zero.

math.DS

Charge Tunable Optical Nonlinearity of Moiré Exciton-Polaritons

Transition metal dichalcogenides represent a versatile platform to study strong light-matter interactions based on excitons and electrons in ordered lattices. Twist-engineering of moiré structures further enables the manipulation of the polaritonic nonlinearities via engineering the exciton landscape on the nanoscale. In this work, we demonstrate in-situ control of the optical saturation-based nonlinearity of moiré exciton-polaritons by phase space restriction via charge doping. Strong exciton-photon coupling is established in a gate-controllable MoTe$_2$-MoSe$_2$ heterobilayer, embedded in a spectrally-tunable open cavity. A small gate voltage can effectively lower the necessary polariton density by one order of magnitude to achieve a similar nonlinear saturation effect as in the charge-neutral case. Our microscopic description successfully explains the observed phenomena in the framework of Pauli blocking for the moiré superlattices with charge preoccupation.

cond-mat.mes-hall

Electron-beam Writing of Spectrally Uniform Green Single-photon Emitters in Hexagonal Boron Nitride

Scalable quantum photonic technologies require single-photon emitters whose positions and emission energies can be engineered simultaneously. Hexagonal boron nitride (hBN) is an attractive room-temperature host, but deterministic creation of spectrally reproducible emitters remains challenging. Here, we use a standard scanning electron microscope as a direct-writing tool to activate bright green single-photon emitters in hBN at predefined sites, without ion implantation or post-fabrication thermal annealing. The written emitters exhibit reproducible zero-phonon-line emission centered near 536 nm, room-temperature antibunching with g(2)(0) as low as 0.08, high brightness, strong linear polarization, and stable emission. Thickness-dependent activation, stacking experiments, cathodoluminescence spectroscopy, and first-principles calculations support a carbon-related defect complex as the most plausible origin of the emission. As a proof of nanophotonic compatibility, we further activate emitters in a nanoparticle-on-mirror plasmonic nanocavity and observe photoluminescence enhancement accompanied by shortened emission lifetimes. These results establish electron-beam direct writing as a practical route to site-selective, spectrally uniform green quantum emitters in hBN, offering a promising basis for integrated room-temperature quantum photonic architectures.

physics.optics

GLM-5V-Turbo: Toward a Native Foundation Model for Multimodal Agents

We present GLM-5V-Turbo, a step toward native foundation models for multimodal agents. As foundation models are increasingly deployed in real environments, agentic capability depends not only on language reasoning, but also on the ability to perceive, interpret, and act over heterogeneous contexts such as images, videos, webpages, documents, GUIs. GLM-5V-Turbo is built around this objective: multimodal perception is integrated as a core component of reasoning, planning, tool use, and execution, rather than as an auxiliary interface to a language model. This report summarizes the main improvements behind GLM-5V-Turbo across model design, multimodal training, reinforcement learning, toolchain expansion, and integration with agent frameworks. These developments lead to strong performance in multimodal coding, visual tool use, and framework-based agentic tasks, while preserving competitive text-only coding capability. More importantly, our development process offers practical insights for building multimodal agents, highlighting the central role of multimodal perception, hierarchical optimization, and reliable end-to-end verification.

cs.CV

Machine-learning-based simulation of turbulent flows over periodic hills using a hybrid U-Net and Fourier neural operator framework

Simulating massively separated turbulent flows over bodies is one of the major applications for large-eddy simulation (LES). In the current work, we propose a machine-learning-based LES framework for the rapid simulation of turbulent flows over periodic hills using a hybrid U-Net and Fourier neural operator (HUFNO) framework. The newly proposed HUFNO model integrates the strengths of both the convolutional neural network (CNN) and Fourier neural operator (FNO) in a novel way that the FNO is applied in the periodic directions of the flow field while the non-periodicity is handled by the CNN-based U-Net framework. In the numerical tests, compared to the original FNO and the U-Net framework, the HUFNO model shows a higher accuracy in the predictions of the velocity field and Reynolds stresses. Further numerical experiments in the LES show that the HUFNO framework outperforms the traditional Smagorinsky (SMAG) model and the wall-adapted local eddy-viscosity (WALE) model in the predictions of the turbulence statistics, the energy spectrum, the invariant characteristics of velocity gradients, the wall stresses and the flow separation structures, with much lower computational cost. Importantly, the accuracy and efficiency are transferable to unseen initial conditions, Reynolds number and hill shapes, underscoring its great potentials for the fast prediction of strongly separated turbulent flows over curved boundaries.

physics.flu-dyn

Scalable quantum error mitigation for dynamical decoupling

Quantum coherence remains a fundamental challenge for advancing quantum technologies. Although dynamical decoupling can suppress decoherence noise, it frequently misestimates decoherence times due to control errors -- a previously underappreciated issue. Here, we present Hadamard phase cycling, a scalable non-Markovian quantum error mitigation method using group-structured phase configurations to filter spurious dynamics. Validated across molecular electron spins, nitrogen-vacancy centers in diamond, nuclear spins, trapped ions, and superconducting qubits, this technique enables accurate decoherence time characterization and enhanced state fidelity with linear complexity. Our results indicate that many reported ultralong decoherence times stem from artifacts like coherence-population mixing rather than genuine noise suppression. By ensuring dynamical authenticity, Hadamard phase cycling establishes a robust framework for reliable quantum control, paving the way for reassessment and advancement of coherence benchmarks in the NISQ era.

quant-ph

Uncertainty quantification and stability of neural operators for prediction of three-dimensional turbulence

Turbulence poses challenges for numerical simulation due to its chaotic, multiscale nature and high computational cost. Traditional turbulence modeling often struggles with accuracy and long-term stability. Recent scientific machine learning (SciML) models, such as Fourier Neural Operators (FNO), show promise in solving PDEs, but are typically limited to one-step-ahead predictions and often fail over long time horizons, especially in 3D turbulence. This study proposes a framework to assess the reliability of neural operator models in turbulent flows. Using three-dimensional forced homogeneous isotropic turbulence (HIT) as a benchmark, we evaluate models in terms of uncertainty quantification (UQ), error propagation, and sensitivity to initial perturbations. Statistical tools such as error distribution analysis and autocorrelation functions (ACF) are used to assess predictive robustness and temporal coherence. Our proposed model, the factorized-implicit FNO (F-IFNO), improves long-term stability and accuracy by incorporating implicit factorization into the prediction process. It outperforms conventional LES and other FNO-based models in balancing accuracy, stability, and efficiency. The results highlight the importance of prediction constraints, time interval selection, and UQ in developing robust neural operator frameworks for turbulent systems.

physics.flu-dyn

Single-molecule Scale Nuclear Magnetic Resonance Spectroscopy using a Robust Near-Infrared Spin Sensor

Nuclear magnetic resonance (NMR) at the single-molecule level with atomic resolution holds transformative potential for structural biology and surface chemistry. Near-surface solid-state spin sensors with optical readout ability offer a promising pathway toward this goal. However, their extreme proximity to target molecules demands exceptional robustness against surface-induced perturbations. Furthermore, life science applications require these sensors to operate in biocompatible spectral ranges that minimize photodamage. In this work, we demonstrate that the PL6 quantum defect in 4H silicon carbide (4H-SiC) can serve as a robust near-infrared spin sensor. This sensor operates at tissue-transparent wavelengths and exhibits exceptional near-surface stability even at depth of 2 nm. Using shallow PL6 centers, we achieve nanoscale NMR detection of proton ($\mathrm{^{1}H}$) spins in immersion oil and fluorine ($\mathrm{^{19}F}$) spins in Fomblin, attaining a detection volume of $\mathrm{(3~nm)^3}$ and a sensitivity reaching the requirement for single-proton spin detection. This work establishes 4H-SiC quantum sensors as a compelling platform for nanoscale magnetic resonance, with promising applications in probing low-dimensional water phases, protein folding dynamics, and molecular interactions.

quant-ph

An Implicit Adaptive Fourier Neural Operator for Long-term Predictions of Three-dimensional Turbulence

Long-term prediction of three-dimensional (3D) turbulent flows is one of the most challenging problems for machine learning approaches. Although some existing machine learning approaches such as implicit U-net enhanced Fourier neural operator (IUFNO) have been proven to be capable of achieving stable long-term predictions for turbulent flows, their computational costs are usually high. In this paper, we use the adaptive Fourier neural operator (AFNO) as the backbone to construct a model that can predict 3D turbulence. Furthermore, we employ the implicit iteration to our constructed AFNO and propose the implicit adaptive Fourier neural operator (IAFNO). IAFNO is systematically tested in three types of 3D turbulence, including forced homogeneous isotropic turbulence (HIT), temporally evolving turbulent mixing layer and turbulent channel flow. The numerical results demonstrate that IAFNO is more accurate than IUFNO and the traditional large-eddy simulation using dynamic Smagorinsky model (DSM), while exhibiting greater stability compared to IUFNO. Meanwhile, the AFNO model exhibits instability in numerical simulations. Moreover, the training efficiency of IAFNO is 4 times higher than that of IUFNO, and the number of parameters and GPU memory occupation of IAFNO are only 1/80 and 1/3 of IUFNO, respectively in HIT. In other tests, the improvements are slightly lower but still considerable. These improvements mainly come from patching and self-attention in 3D space. Besides, the well-trained IAFNO is significantly faster than the DSM. The code (and datasets) of the current work can be accessed at https://github.com/yuchi-richard-jiang/IAFNO.

physics.flu-dyn

Deterministic and Scalable Quantum Light Generation in DNA Origami-Programmed Organic Molecule-MoS$_2$ Monolayer Hybrids

The functionalization of atomically-thin transition metal dichalcogenides (TMDs) with organic molecules is a promising approach for realizing nanoscale optoelectronic devices with tailored functionalities, such as quantum light generation or p-n junctions. However, achieving precise control over the molecules' positioning on the 2D material remains a significant challenge. Here, we overcome the limitations of solution- and vapor-deposition methods and use a DNA origami placement technique to spatially arrange thiol molecules on a chip surface at the single-molecule level with high assembly yields. We successfully integrated MoS$_2$ monolayers with micron-scale thiol-origami patterns, achieving single-photon emission from thiol-induced localized excitons in MoS$_2$. Our work lays a foundation for the chemical control of quantum emitters in atomically-thin semiconductors and enables the design and production of ultracompact 2D devices for quantum technologies.

cond-mat.mes-hall

Control of atomic reconstruction and quasi-1D excitons in strain-engineered moiré heterostructures

In two-dimensional nearly commensurate heterostructures, strain plays a critical role in shaping electronic behavior. While previous studies have focused on random strain introduced during fabrication, achieving controlled structural design has remained challenging. Here, we demonstrate the deterministic creation of one-dimensional arrays from initially zero-dimensional triangular moiré patterns in MoSe$_2$-WSe$_2$ heterobilayers. This transformation, driven by the interplay of uniaxial strain and atomic reconstruction, results in one-dimensional confinement of interlayer excitons within domain walls, exhibiting near-unity linearly polarized emission related to the confinement-induced symmetry breaking. The width of the domain walls--and consequently the degree of exciton confinement--can be precisely tuned by the interlayer twist angle. By applying out-of-plane electric field, the confined excitons exhibit energy shifts exceeding 100~meV and changes in the fine-structure splitting by up to a factor of two. Our work demonstrates the potential of strain engineering for constructing designer moiré systems with programmable quantum properties, paving the way for future optoelectronic applications.

cond-mat.mes-hall

LESnets (Large-Eddy Simulation nets): Physics-informed neural operator for large-eddy simulation of turbulence

Acquisition of large datasets for three-dimensional (3D) partial differential equations (PDE) is usually very expensive. Physics-informed neural operator (PINO) eliminates the high costs associated with generation of training datasets, and shows great potential in a variety of partial differential equations. In this work, we employ physics-informed neural operator, encoding the large-eddy simulation (LES) equations directly into the neural operator for simulating three-dimensional incompressible turbulent flows. We develop the LESnets (Large-Eddy Simulation nets) by adding large-eddy simulation equations to two different data-driven models, including Fourier neural operator (FNO) and implicit Fourier neural operator (IFNO) without using label data. Notably, by leveraging only PDE constraints to learn the spatio-temporal dynamics, LESnets models retain the computational efficiency of data-driven approaches while obviating the necessity for data. Meanwhile, using LES equations as PDE constraints makes it possible to efficiently predict complex turbulence at coarse grids. We investigate the performance of the LESnets models with two standard three-dimensional turbulent flows: decaying homogeneous isotropic turbulence and temporally evolving turbulent mixing layer. In the numerical experiments, the LESnets models show similar accuracy as compared to traditional large-eddy simulation and data-driven models including FNO and IFNO, and exhibits a robust generalization ability to unseen regime of flow fields. By integrating a single set of flow data, the LESnets models can automatically learn the coefficient of the subgrid scale (SGS) model during the training of the neural operator. Moreover, the well-trained LESnets models are significantly faster than traditional LES, and exhibits comparable computational efficiency to the data-driven FNO and IFNO models.

physics.flu-dyn

Discovery of an anomalous non-evaporating sub-nanometre water layer in open environment

Water exhibits complex behaviors as a result of hydrogen bonding, and low-dimensional confined water plays a key role in material science, geology, and biology science. Conventional techniques like STM, TEM, and AFM enable atomic-scale observations but face limitations under ambient conditions and surface topographies. NV center magnetic resonance technology provides an opportunity to overcome these limitations, offering non-contact atomic-scale measurements with chemical resolution capability. In this study, a nanoscale layer dissection method was developed utilizing NV center technology to analyze water layers with diverse physicochemical properties. It unveiled the presence of a non-evaporating sub-nanometer water layer on a diamond surface under ambient conditions. This layer demonstrated impervious to atmospheric water vapor and exhibited unique electronic transport mediated via hydrogen bonding. These findings provide new perspectives and a platform for studying the structure and behavior of low-dimensional water, as well as the surface properties influenced by adsorbed water under native conditions.

cond-mat.mtrl-sci

Prediction of turbulent channel flow using Fourier neural operator-based machine-learning strategy

Fast and accurate predictions of turbulent flows are of great importance in the science and engineering field. In this paper, we investigate the implicit U-Net enhanced Fourier neural operator (IUFNO) in the stable prediction of long-time dynamics of three-dimensional (3D) turbulent channel flows. The trained IUFNO models are tested in the large-eddy simulations (LES) at coarse grids for three friction Reynolds numbers: $Re_τ\approx180$, $395$ and $590$. The adopted near-wall mesh grids are tangibly coarser than the general requirements for wall-resolved LES. Compared to the original Fourier neural operator (FNO), the implicit FNO (IFNO) and U-Net enhanced FNO (UFNO), the IUFNO model has a much better long-term predictive ability. The numerical experiments show that the IUFNO framework outperforms the traditional dynamic Smagorinsky model (DSM) and the wall-adapted local eddy-viscosity (WALE) model in the predictions of a variety of flow statistics and structures, including the mean and fluctuating velocities, the probability density functions (PDFs) and joint PDF of velocity fluctuations, the Reynolds stress profile, the kinetic energy spectrum, and the Q-criterion (vortex structures). Meanwhile, the trained IUFNO models are computationally much faster than the traditional LES models. Thus, the IUFNO model is a promising approach for the fast prediction of wall-bounded turbulent flow.

physics.flu-dyn

Prediction of three-dimensional chemically reacting compressible turbulence based on implicit U-Net enhanced Fourier neural operator

The accurate and fast prediction of long-term dynamics of turbulence presents a significant challenge for both traditional numerical simulations and machine learning methods. In recent years, the emergence of neural operators has provided a promising approach to address this issue. The implicit U-Net enhanced Fourier neural operator (IU-FNO) has successfully demonstrated long-term stable predictions for three-dimensional incompressible turbulence. In this study, we extend this method to the three-dimensional chemically reacting compressible turbulence. Numerical results show that the IU-FNO model predicts flow dynamics significantly faster than the traditional dynamic Smagorinsky model (DSM) used in large eddy simulation (LES). In terms of prediction accuracy, the IU-FNO framework outperforms the traditional DSM in predicting the energy spectra of velocity, temperature, and density, the probability density functions (PDFs) of vorticity and velocity increments, and instantaneous spatial structures of temperature. Therefore, the IU-FNO represents a highly promising approach for predicting chemically reacting compressible turbulence.

physics.flu-dyn

Fourier neural operator for large eddy simulation of compressible Rayleigh-Taylor turbulence

The Fourier neural operator (FNO) framework is applied to the large eddy simulation (LES) of three-dimensional compressible Rayleigh-Taylor (RT) turbulence with miscible fluids at Atwood number $A_t=0.5$, stratification parameter $Sr=1.0$, and Reynolds numbers $Re=10000$ and 30000. The FNO model is first used for predicting three-dimensional compressible turbulence. The different magnitudes of physical fields are normalized using root mean square values for an easier training of FNO models. In the \emph{a posteriori} tests, the FNO model outperforms the velocity gradient model (VGM), the dynamic Smagorinsky model (DSM), and implicit large eddy simulation (ILES) in predicting various statistical quantities and instantaneous structures, and is particularly superior to traditional LES methods in predicting temperature fields and velocity divergence. Moreover, the computational efficiency of the FNO model is much higher than that of traditional LES methods. FNO models trained with short-time, low Reynolds number data exhibit a good generalization performance on longer-time predictions and higher Reynolds numbers in the \emph{a posteriori} tests.

physics.flu-dyn

A transformer-based neural operator for large-eddy simulation of turbulence

Predicting the large-scale dynamics of three-dimensional (3D) turbulence is challenging for machine learning approaches. This paper introduces a transformer-based neural operator (TNO) to achieve precise and efficient predictions in the large-eddy simulation (LES) of 3D turbulence. The performance of the proposed TNO model is systematically tested and compared with LES using classical sub-grid scale (SGS) models, including the dynamic Smagorinsky model (DSM) and the dynamic mixed model (DMM), as well as the original Fourier neural operator (FNO) model, in homogeneous isotropic turbulence (HIT) and free-shear turbulent mixing layer. The numerical simulations comprehensively evaluate the performance of these models on a variety of flow statistics, including the velocity spectrum, the probability density functions (PDFs) of vorticity, the PDFs of velocity increments, the evolution of turbulent kinetic energy, and the iso-surface of the Q-criterion. The results indicate that the accuracy of the TNO model is comparable to the LES with DSM model, and outperforms the FNO model and LES using DMM in HIT. In the free-shear turbulence, the TNO model exhibits superior accuracy compared to other models. Moreover, the TNO model has fewer parameters than the FNO model and enables long-term stable predictions, which the FNO model cannot achieve. The well-trained TNO model is significantly faster than traditional LES with DSM and DMM models, and can be generalized to higher Taylor-Reynolds number cases, indicating its strong potential for 3D nonlinear engineering applications.

physics.flu-dyn