arXiv subjects

Zhi-Qiang Zhang

Publications and source records attributed to Zhi-Qiang Zhang.

17 recordsLinked to original sources

arXiv2026

Three-dimensional quantum anomalous Hall effect in Weyl semimetals

The quantum anomalous Hall effect (QAHE) is a quantum phenomenon in which a two-dimensional system exhibits a quantized Hall resistance $h/e^2$ in the absence of magnetic field, where $h$ is the Planck constant and $e$ is the electron charge. In this work, we extend this novel phase to three dimensions and thus propose a three-dimensional QAHE exhibiting richer and more versatile transport behaviors. We first confirm this three-dimensional QAHE through the quantized Chern number, then establish its bulk-boundary correspondence, and finally reaffirm it via the distinctive transport properties. Remarkably, we find that the three-dimensional QAHE hosts two chiral surface states along one spatial direction while a pair of chiral hinge states along another direction, and the location of the hinge states depends sensitively on the Fermi energy. These two types of boundary states are further connected through a perpendicular chiral surface states, whose chirality is also Fermi energy dependent. Consequently, depending on the transport direction, its Hall resistance can quantize to $0$, $h/e^2$, or $\pm h/e^2$ when the Fermi energy is tuned across the charge neutral point. This three-dimensional QAHE not only fill the gap in the Hall effect family but also holds significant potentials in device applications such as in-memory computing.

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arXiv2025

Multi-Joint Physics-Informed Deep Learning Framework for Time-Efficient Inverse Dynamics

Time-efficient estimation of muscle activations and forces across multi-joint systems is critical for clinical assessment and assistive device control. However, conventional approaches are computationally expensive and lack a high-quality labeled dataset for multi-joint applications. To address these challenges, we propose a physics-informed deep learning framework that estimates muscle activations and forces directly from kinematics. The framework employs a novel Multi-Joint Cross-Attention (MJCA) module with Bidirectional Gated Recurrent Unit (BiGRU) layers to capture inter-joint coordination, enabling each joint to adaptively integrate motion information from others. By embedding multi-joint dynamics, inter-joint coupling, and external force interactions into the loss function, our Physics-Informed MJCA-BiGRU (PI-MJCA-BiGRU) delivers physiologically consistent predictions without labeled data while enabling time-efficient inference. Experimental validation on two datasets demonstrates that PI-MJCA-BiGRU achieves performance comparable to conventional supervised methods without requiring ground-truth labels, while the MJCA module significantly enhances inter-joint coordination modeling compared to other baseline architectures.

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arXiv2025

Chern Vector Protected Three-dimensional Quantized Hall Effect

Recently, Chern vector with arbitrary formula $\textbf{C}\!=\!(\mathcal{C}_{yz},\mathcal{C}_{xz},\mathcal{C}_{xy})$ in three-dimensional systems has been experimentally realized [\B{Nature 609, 925 (2022)}]. Motivated by these progresses, we propose the Chern vector $\textbf{C}\!=\!(0,m,n)$-protected quantized Hall effect in three-dimensional systems. By examining samples with Chern vector $\textbf{C}\!=\!(0,m,n)$ and dimensions $L_y$ and $L_z$ along the $y$- and $z$-directions, we demonstrate a topologically protected two-terminal response. This response can be reformulated as the sum of the transmission coefficients along the $x$- and $y$-directions, given by $(mL_y\!+\!nL_z)$. When applied to Hall bar setups, this topological mechanism gives rise to quantized Hall conductances, such as $G_{xy}$ and $G_{xz}$, which are expressed by $\pm(mL_y\!+\!nL_z)$. These Hall conductances exhibit a clear dependency on sample dimensions, illuminating the intrinsic three-dimensional nature. Finally, we propse potential candidates for experimental realization. Our findings not only deepen the understanding of the topological nature of Chern vectors but also enlighten the exploration of their transport properties.

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arXiv2024

Knowledge-Based Deep Learning for Time-Efficient Inverse Dynamics

Accurate understanding of muscle activation and muscle forces plays an essential role in neuro-rehabilitation and musculoskeletal disorder treatments. Computational musculoskeletal modeling has been widely used as a powerful non-invasive tool to estimate them through inverse dynamics using static optimization, but the inherent computational complexity results in time-consuming analysis. In this paper, we propose a knowledge-based deep learning framework for time-efficient inverse dynamic analysis, which can predict muscle activation and muscle forces from joint kinematic data directly while not requiring any label information during model training. The Bidirectional Gated Recurrent Unit (BiGRU) neural network is selected as the backbone of our model due to its proficient handling of time-series data. Prior physical knowledge from forward dynamics and pre-selected inverse dynamics based physiological criteria are integrated into the loss function to guide the training of neural networks. Experimental validations on two datasets, including one benchmark upper limb movement dataset and one self-collected lower limb movement dataset from six healthy subjects, are performed. The experimental results have shown that the selected BiGRU architecture outperforms other neural network models when trained using our specifically designed loss function, which illustrates the effectiveness and robustness of the proposed framework.

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arXiv2024

Physics-informed Deep Learning for Muscle Force Prediction with Unlabeled sEMG Signals

Computational biomechanical analysis plays a pivotal role in understanding and improving human movements and physical functions. Although physics-based modeling methods can interpret the dynamic interaction between the neural drive to muscle dynamics and joint kinematics, they suffer from high computational latency. In recent years, data-driven methods have emerged as a promising alternative due to their fast execution speed, but label information is still required during training, which is not easy to acquire in practice. To tackle these issues, this paper presents a novel physics-informed deep learning method to predict muscle forces without any label information during model training. In addition, the proposed method could also identify personalized muscle-tendon parameters. To achieve this, the Hill muscle model-based forward dynamics is embedded into the deep neural network as the additional loss to further regulate the behavior of the deep neural network. Experimental validations on the wrist joint from six healthy subjects are performed, and a fully connected neural network (FNN) is selected to implement the proposed method. The predicted results of muscle forces show comparable or even lower root mean square error (RMSE) and higher coefficient of determination compared with baseline methods, which have to use the labeled surface electromyography (sEMG) signals, and it can also identify muscle-tendon parameters accurately, demonstrating the effectiveness of the proposed physics-informed deep learning method.

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arXiv2024

Measurement of the Chern Number for Non-Hermitian Chern Insulators

The identification of the topological invariant of a topological system is crucial in experiments. However, due to the inherent non-Hermitian features, such determination is notably challenging in non-Hermitian systems. Here, we propose that the magnetic effect can be utilized to measure the Chern number of the non-Hermitian Chern insulator. We find that the splitting of non-Hermitian bands under the magnetic field is Chern number dependent. Consequently, one can easily identify the Chern number by analyzing these splitting sub-bands. From the experimental perspective, the measurement of non-Hermitian bands is demonstrated in LC electric circuits. Furthermore, we find that the non-Hermiticity can drive open (closed) orbits of sub-bands in the Hermitian limit closed (open), which can also be identified by our proposal. These phenomena highlight the distinctive capabilities of non-Hermitian systems. Our results facilitate the detection of Chern numbers for non-Hermitian systems and may motivate further studies of their topological properties.

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arXiv2023

Dissipation Enhanced Unidirectional Transport in Topological Systems

Dissipation is a common occurrence in real-world systems and is generally considered to be detrimental to transport. In this study, we examine the transport properties of a narrow quantum anomalous Hall system with dissipation applied on one edge. When the Fermi level resides within the hybridization gap, we find that while transport is suppressed on one edge, it is significantly enhanced on the other. We reveal that this enhancement arises from dissipation-induced gap closure, which is deeply rooted in the point gap topology of the system, resulting in a reduction of the decaying coefficient. When the dissipation is very large, we find that the low-energy physics is nearly indistinguishable from a narrower system, whose dissipation amplitude is inversely proportional to that of the original one. To get more physical intuition, we demonstrate that the low-energy physics can be well captured by a pair of coupled counter-propagating chiral edge states, one of which has a modified group velocity and an effective dissipation. We also briefly discuss the possible experimental realizations of this enhanced unidirectional transport.

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arXiv2022

Boosting Personalised Musculoskeletal Modelling with Physics-informed Knowledge Transfer

Data-driven methods have become increasingly more prominent for musculoskeletal modelling due to their conceptually intuitive simple and fast implementation. However, the performance of a pre-trained data-driven model using the data from specific subject(s) may be seriously degraded when validated using the data from a new subject, hindering the utility of the personalised musculoskeletal model in clinical applications. This paper develops an active physics-informed deep transfer learning framework to enhance the dynamic tracking capability of the musculoskeletal model on the unseen data. The salient advantages of the proposed framework are twofold: 1) For the generic model, physics-based domain knowledge is embedded into the loss function of the data-driven model as soft constraints to penalise/regularise the data-driven model. 2) For the personalised model, the parameters relating to the feature extraction will be directly inherited from the generic model, and only the parameters relating to the subject-specific inference will be finetuned by jointly minimising the conventional data prediction loss and the modified physics-based loss. In this paper, we use the synchronous muscle forces and joint kinematics prediction from surface electromyogram (sEMG) as the exemplar to illustrate the proposed framework. Moreover, convolutional neural network (CNN) is employed as the deep neural network to implement the proposed framework, and the physics law between muscle forces and joint kinematics is utilised as the soft constraints. Results of comprehensive experiments on a self-collected dataset from eight healthy subjects indicate the effectiveness and great generalization of the proposed framework.

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arXiv2022

Modified Generalized-Brillouin-Zone Theory with On-site Disorders

We study the characterization of the non-Hermitian skin effect (NHSE) in non-Hermitian systems with on-site disorder. We extend the application of generalized-Brillouin-zone (GBZ) theory to these systems. By proposing a modified GBZ theory, we give a faithfully description of the NHSE. For applications, we obtain a unified $β$ for system with long-range hopping, and explain the conventional-GBZ irrelevance of the magnetic suppression of the NHSE in the previous study.

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arXiv2022

Bulk-Bulk Correspondence in Disordered Non-Hermitian Systems

The consistency between eigenvalues calculated under open and periodic boundary conditions, named as {\it bulk-bulk correspondence} ($\mathcal{BBC}$), can be destroyed in systems with non-Hermitian skin effect (NHSE). In spite of the great success of the generalized Brillouin zone (GBZ) theory in clean non-Hermitian systems, the applicability of GBZ theory is questionable when the translational symmetry is broken. Thus, it is of great value to rebuild the $\mathcal{BBC}$ for disorder samples, which extends the application of GBZ theory in non-Hermitian systems. Here, we propose a scheme reconstructing $\mathcal{BBC}$, which can be regarded as the solution of an optimization problem. By solving this optimization problem analytically, we reconstruct the $\mathcal{BBC}$ and obtain the modified GBZ theory in several prototypical disordered non-Hermitian models. The modified GBZ theory gives a precise description of NHSE, which predicts the intriguing disorder-enhanced and disorder-irrelevant NHSEs.

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arXiv2022

Quantized charge-pumping in higher-order topological insulators

We study the quantized charge pumping of higher-order topological insulators (HOTIs) with edge-corner correspondences based on the combination of the rotation of in-plane magnetic field and the quantum spin Hall effect. A picture of a specific charge pumping process is uncovered with the help of the non-equilibrium Green's function method. Significantly, we demonstrate that the quantized charge pumping current is achieved without the participation of bulk states, and the charges move along the boundary of the sample. Furthermore, the effects of external parameters on the pumping current is also studied. We find that the magnitude and direction of the pumping current can be manipulated by adjusting the coupling strength between the leads and sample. Our work deepens the understanding of the charge pumping in HOTIs and extends the study of their transport properties.

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arXiv2021

Building programable integrated circuits through disordered Chern insulators

We study the construction of programable integrated circuits with the help of disordered Chern insulators (CIs) in this letter. Specifically, the schemes for low dissipation logic devices and connecting wires are proposed. We use the external-gate-induced step voltage to construct spatially adjustable channels, where these channels take the place of the conventional wires. Our numerical calculation manifests that the external gates can be adopted to program the arbitrary number of wires ($n$-to-$m$ connections). We find that their electron transport is dissipationless and robust against gate voltage fluctuation and disorder strength. Furthermore, seven basic logic gates distinct from the conventional structures are proposed. Our proposal has potential applications in low power integrated circuits and enlightens the building of integrated circuits in topological materials.

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arXiv2021

Bulk-boundary correspondence in disordered higher-order topological insulators

In this work, we study the disorder effects on the bulk-boundary correspondence of two-dimensional higher-order topological insulators (HOTIs). We concentrate on two cases: (i) bulk-corner correspondence, (ii) edge-corner correspondence. For the bulk-corner correspondence case, we demonstrate the existence of the mobility gaps and clarify the related topological invariant that characterizes the mobility gap. Furthermore, we find that, while the system preserves the bulk-corner correspondence in the presence of disorder, the corner states are protected by the mobility gap instead of the bulk gap. For the edge-corner correspondence case, we show that the bulk mobility gap and edge band gaps of HOTIs are no longer closed simultaneously. Therefore, a rich phase diagram is obtained, including various disorder-induced phase transition processes. Notably, a disorder-induced transition from the non-trivial to trivial phase is realized, distinguishing the HOTIs from the other topological states. Our results deepen the understanding of bulk-boundary correspondence and enrich the topological phase transitions of disordered HOTIs.

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arXiv2021

Real space topological invariant and higher-order topological Anderson insulator in two-dimensional non-Hermitian systems

We study the characterization and realization of higher-order topological Anderson insulator (HOTAI) in non-Hermitian systems, where the non-Hermitian mechanism ensures extra symmetries as well as gain and loss disorder.We illuminate that the quadrupole moment $Q_{xy}$ can be used as the real space topological invariant of non-Hermitian higher-order topological insulator (HOTI). Based on the biorthogonal bases and non-Hermitian symmetries, we prove that $Q_{xy}$ can be quantized to $0$ or $0.5$. Considering the disorder effect, we find the disorder-induced phase transition from normal insulator to non-Hermitian HOTAI. Furthermore, we elucidate that the real space topological invariant $Q_{xy}$ is also applicable for systems with the non-Hermitian skin effect. Our work enlightens the study of the combination of disorder and non-Hermitian HOTI.

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arXiv2021

Chiral Interface States and Related Quantized Transport in Disordered Chern Insulators

In this Letter, we study an Anderson-localization-induced quantized transport in disordered Chern insulators (CIs). By investigating the disordered CIs with a step potential, we find that the chiral interface states emerge along the interfaces of the step potential, and the energy range for such quantized transport can be manipulated through the potential strength. Furthermore, numerical simulations on cases with a multi-step potential demonstrate that such chiral state can be spatially shifted by varying the Fermi energy, and the energy window for quantized transport is greatly enlarged. Experimentally, such chiral interface states can be realized by imposing transverse electric field, in which the energy window for quantized transport is much broader than the intrinsic band gap of the corresponding CI. These phenomena are quite universal for disordered CIs due to the direct phase transition between the CI and the normal insulator.

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arXiv2021

The Global Phase Diagram of disordered Higher-order Weyl Semimetals

We study the disorder-induced phase transition of higher-order Weyl semimetals (HOWSMs) and the fate of the topological features of disordered HOWSMs. We obtain a global phase diagram of HOWSMs according to the scaling theory of Anderson localization. Specifically, a phase transition from the Weyl semimetal (WSM) to the HOWSM is uncovered, distinguishing the disordered HOWSMs from the traditional WSMs. Further, we confirm the robustness of Weyl-nodes for HOWSMs. Interestingly, the unique topological properties of HOWSMs show different behaviors: (i) the quantized quadrupole moment and the corresponding quantized charge of hinge states are fragile to weak disorder; (ii) the hinge states show moderate stability which enables the feasibility in experimental observation. Our study deepens the understanding of the topological nature of HOWSMs and paves a possible way to the characterization of such a phase in experiments.

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arXiv2019

Topological Anderson Insulator in electric circuits

Although topological Anderson insulator has been predicted in 2009, the lasting investigations of this disorder established nontrivial state results in only two experimental observations in cold atoms [Science, {\bf 362 },929 (2018)] and in photonic crystals [Nature, {\bf 560}, 461 (2018)] recently. In this paper, we study the topological Anderson transition in electric circuits. By arranging capacitor and inductor network, we construct a disordered Haldane model. Specially, the disorder is introduced by the grounding inductors with random inductance. Based on non-commutative geometry method and transport calculation, we confirm that the disorder in circuits can drive a transition from normal insulator to topological Anderson insulator. We also find the random inductance induced disorder possessing unique characters rather than Anderson disorder, therefore it leads to distinguishable features of topological Anderson transition in circuits. Different from other systems, the topological Anderson insulator in circuits can be detected by measuring the corresponding quantized transmission coefficient and edge state wavefunction due to mature microelectronic technology.

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