arXiv subjects

Hua Tong

Publications and source records attributed to Hua Tong.

At least 19 recordsLinked to original sources

arXiv2026

Universal Jamming Criticality and Self-Organizing Principles from Disorder to the Limit of Perfect Crystalline Order

While crystals are defined by periodic order, the nature of amorphous solids remains elusive due to their disordered, diverse, and nonequilibrium structures. Here, we focus on jammed elastic packings and systematically tune structure from crystalline to fully disordered to unveil the universal underlying characteristics. We demonstrate that their mechanical properties are universally governed by jamming criticality, featuring characteristic scaling behaviors near the jamming transition, excepting the singular close-packed point. This is facilitated by random nonaffine elasticity arising from contact-level disorder. Consequently, the jamming density can approach close packing, suggesting a fundamental decoupling between jamming criticality and the glass transition physics. Moreover, we uncover a universal coordination-number distribution and contact hyperuniformity in marginally jammed states, independent of particle-level structure. These findings suggest a general organizing mechanism for emergent rigidity in disordered solids, underscore the broad relevance of jamming physics, and complement principles of mechanical self-organization.

cond-mat.soft↗

arXiv2026

Gravitational waves from graviton bremsstrahlung in scalar leptoquark decays

We study the stochastic gravitational wave background originated from graviton bremsstrahlung in decays of scalar leptoquarks, which are colored scalar bosons simultaneously coupling to a quark and a lepton. We take the scalar leptoquarks in the $\mathrm{SU}(5)$ grand unified theory as a concrete example. Stringent experimental bounds on proton decay force these particles to be superheavy, which in turn renders their graviton bremsstrahlung, induced by quantum gravity effects, less suppressed. By solving the relevant Boltzmann equation, we trace the evolution of the scalar leptoquark number density in the early universe and use it to compute the resulting gravitational wave spectrum. We find that high-frequency gravitational wave detectors employing resonant cavity techniques offer a promising means to probe such signals.

hep-ph↗

arXiv2026

Scalable DDPM-Polycube: An Extended Diffusion-Based Method for Hexahedral Mesh and Volumetric Spline Construction

Polycube structures provide parametric domains for all-hexahedral (all-hex) mesh generation and analysis-suitable volumetric spline construction in isogeometric analysis (IGA). Recent learning-based polycube pipelines have improved automation, yet several challenges remain when handling complex CAD geometries. These challenges include the limited diversity of primitive geometries, restricted grid configurations, and the increasing cost of genus-guided context search during inference as both the primitive set and the grid size grow. In this paper, we present {Scalable DDPM-Polycube}, an extended diffusion-based polycube construction method that addresses these limitations. First, we expand the primitive set from two primitive geometries to three by introducing a blind-hole cube primitive, thereby improving the representation of local hole-like features that do not change the global genus. Second, we extend the grid configuration from the previous $2\times 1$ setting to an enlarged three-dimensional grid configuration, which increases representational capacity and reduces mapping distortion for complex geometries. Third, we develop a genus-guided context generation strategy together with a hierarchical verification procedure, enabling robust context generation in both user-guided and automated modes. Once a valid polycube structure is generated, it is used for parametric mapping, all-hex control mesh generation, and volumetric spline construction. Experimental results demonstrate that scalable DDPM-Polycube improves the generality, scalability, and automation of diffusion-based polycube generation, and supports hex mesh generation and volumetric spline construction for IGA applications on complex geometries.

cs.CE↗

arXiv2025

Element-Saving Hexahedral 3-Refinement Templates

Conforming hex meshes are widely regarded as an effective computational domain for simulation because of their nice numerical properties, yet automatically decomposing a general 3D volume into a conforming hex mesh remains a formidable challenge. Among existing approaches, methods that construct an adaptive Cartesian grid and subsequently convert it into a conforming mesh stand out for their robustness. However, topological conversion schemes require strict compatibility conditions that inevitably increase element count. State-of-the-art 2-refinement octree methods employ weakly-balanced and generalized pairing conditions to yield low element counts, but suffer from critical limitations: primal cell information is lost after dualization, and resulting dual cells often exhibit non-planar quad faces. Alternatively, 3-refinement 27-tree methods directly generate conforming hex meshes through template-based replacement, producing higher-quality elements with planar faces, but previous techniques impose far stricter conditions, severely over-refining grids by factors of ten to one hundred. This article introduces a novel 3-refinement approach using a moderately-balanced condition, slightly stronger than weakly-balanced but substantially more relaxed than prior 3-refinement requirements. The key insight is that recursively applying local refinements can isolate and reduce complex configurations to simpler cases covered by a fundamental template set. Two open-sourced variants are provided: one optimized for speed, and another trading some computational cost for marginally reduced element counts. Compared to previous 3-refinement methods, they significantly reduce final hex element counts while preserving min SJ values and guaranteeing convex polyhedral cells; relative to 2-refinement state-of-the-art, they also achieve a lower Hausdorff ratio using slightly fewer elements.

cs.CG↗

arXiv2025

MCHex: Marching Cubes Based Adaptive Hexahedral Mesh Generation with Guaranteed Positive Jacobian

Grid-based methods are the most robust approach for automatic hexahedral (hex) meshing, but they struggle to achieve high boundary fidelity and element quality. Conventional pipelines remove outside elements. This yields axis-aligned surfaces that converge to the input geometry at first order. The subsequent padding and projection steps are heuristic, offering no guarantees on final boundary fidelity or mesh quality. This paper introduces MCHex, a fundamental reformulation of boundary and mesh quality handling in grid-based hex meshing. MCHex directly applies Marching Cubes (MC) inside each grid cell, ensuring that the mesh boundary is an MC surface. The key insight is that by constraining cut-cell configurations to a 3-regular polyhedron, a midpoint subdivision of these configurations produces an all-hex mesh with a guaranteed positive Jacobian for every element. MCHex provides three advantages: (1) a theoretical guarantee of positive Jacobian for all hex elements; (2) boundary convergence that matches the approximation rate of MC, together with a non-heuristic algorithm that has well-bounded time complexity and achieves the fastest wall-clock time among all existing methods; and (3) generation of manifold surfaces for arbitrary geometries and a natural padded layer. Extensive evaluation on a benchmark of 202 geometries compares MCHex against several previous state-of-the-art hex meshing methods under a slightly smaller element budget, demonstrating that MCHex consistently produces positive Jacobian meshes with similar boundary fidelity while running significantly faster. MCHex can integrate seamlessly with post-processing steps such as mesh smoothing, mesh simplification, and is suitable for simulation.

cs.CG↗

arXiv2025

Orientational phase transitions induced by two-patch interactions

For two-patch particles in two dimensions, we find that the coupling of anisotropic patchy interactions and the triangular lattice leads to novel phase behaviors. For asymmetric patch-patch (PP) and nonpatch-nonpatch (NN) interactions, the system has dual orientationally ordered phases of the same symmetry, intermediated by a nematic phase. Both phase transitions from the nematic phase to dual ordered phases are continuous and belong to the same universality class, and they lead to highly nonmonotonic variations of the nematic order parameter. The system becomes disordered at high temperature through another continuous transition. When the PP and NN interactions become symmetric, the system has subextensive ground-state entropy, and with increasing temperature it undergoes two Berezinskii-Kosterlitz-Thouless phase transitions, with a critical phase connecting a nematic phase and a disordered phase. These results open up new opportunities for designing patchy interactions to study orientational phase transitions and critical phenomena.

cond-mat.soft↗

arXiv2025

Disentangling hierarchical relaxations in glass formers via dynamic eigenmodes

Hierarchical dynamics in glass-forming systems span multiple timescales, from fast vibrations to slow structural rearrangements, appearing in both supercooled fluids and glassy states. Understanding how these diverse processes interact across timescales remains a central challenge. Here, by combining direct particle-level observations with a dynamic eigenmode approach that decomposes intermediate-timescale responses into distinct modes, we reveal the microscopic organisation of relaxation dynamics in two-dimensional colloidal systems. We identify five classes of modes characterizing hierarchical dynamics: (i) quasi-elastic modes, (ii) slow-reversible string modes contributing to dynamic heterogeneity, (iii) slow-irreversible string modes leading to flow, (iv) fast-$β$ modes with fast-reversible strings, and (v) random noise modes. The emergence of quasi-elastic modes marks the onset of glassy dynamics, while reversible string modes dominate dynamic heterogeneity throughout both supercooled and glassy regimes. Our findings offer a unified microscopic framework for understanding how distinct relaxation processes interconnect across timescales, illuminating the mechanisms driving glass formation.

cond-mat.soft↗

arXiv2025

A mechanical approach to facilitate the formation of dodecagonal quasicrystals and their approximants

The conditions for forming quasicrystals and their approximants are stringent, normally requiring multiple length scales to stabilize the quasicrystalline order. Here we report an unexpected finding that the approximants and motifs of dodecagonal quasicrystals can be spontaneously formed in the simplest system of identical hard disks, utilizing the unstable feature of the initial square packing subject to mechanical perturbations. Because there is only one length scale involved, this finding challenges existing theories of quasicrystals and their approximants. By applying the same approach to a system known to form a dodecagonal quasicrystal, we develop decent quasicrystalline order in a purely mechanical manner. With the aid of thermal treatment, we achieve a significantly better quasicrystalline order than that from the direct self-assembly of the liquid state within the same period of time. In sufficiently low temperatures where the self-assembly of a liquid is significantly hindered, our approach still promotes the formation of quasicrystals. Our study thus opens a venue for high-efficiency search and formation of quasicrystals, and may have broader implications for the design and synthesis of quasicrystalline materials.

cond-mat.soft↗

arXiv2025

DDPM-Polycube: A Denoising Diffusion Probabilistic Model for Polycube-Based Hexahedral Mesh Generation and Volumetric Spline Construction

In this paper, we propose DDPM-Polycube, a generative polycube creation approach based on denoising diffusion probabilistic models (DDPM) for generating high-quality hexahedral (hex) meshes and constructing volumetric splines. Unlike DL-Polycube methods that rely on predefined polycube structure templates, DDPM-Polycube models the deformation from input geometry to its corresponding polycube structures as a denoising task. By learning the deformation characteristics of simple geometric primitives (a cube and a cube with a hole), the DDPM-Polycube model progressively reconstructs polycube structures from input geometry by removing non-standard Gaussian noise. Once valid polycube structures are generated, they are used for surface segmentation and parametric mapping to generate high-quality hex meshes. Truncated hierarchical B-splines are then applied to construct volumetric splines that satisfy the requirements of isogeometric analysis (IGA). Experimental results demonstrate that DDPM-Polycube model can directly generate polycube structures from input geometries, even when the topology of these geometries falls outside its trained range. This provides greater generalization and adaptability for diverse engineering geometries. Overall, this research shows the potential of diffusion models in advancing mesh generation and IGA applications.

cs.GR↗

arXiv2024

Fast and Robust Hexahedral Mesh Optimization via Augmented Lagrangian, L-BFGS, and Line Search

We present a new software package, ``HexOpt,'' for improving the quality of all-hexahedral (all-hex) meshes by maximizing the minimum mixed scaled Jacobian-Jacobian energy functional, and projecting the surface points of the all-hex meshes onto the input triangular mesh. The proposed HexOpt method takes as input a surface triangular mesh and a volumetric all-hex mesh. A constrained optimization problem is formulated to improve mesh quality using a novel function that combines Jacobian and scaled Jacobian metrics which are rectified and scaled to quadratic measures, while preserving the surface geometry. This optimization problem is solved using the augmented Lagrangian (AL) method, where the Lagrangian terms enforce the constraint that surface points must remain on the triangular mesh. Specifically, corner points stay exactly at the corner, edge points are confined to the edges, and face points are free to move across the surface. To take the advantage of the Quasi-Newton method while tackling the high-dimensional variable problem, the Limited-Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm is employed. The step size for each iteration is determined by the Armijo line search. Coupled with smart Laplacian smoothing, HexOpt has demonstrated robustness and efficiency, successfully applying to 3D models and hex meshes generated by different methods without requiring any manual intervention or parameter adjustment.

cs.CG↗

arXiv2024

DL-Polycube: Deep learning enhanced polycube method for high-quality hexahedral mesh generation and volumetric spline construction

In this paper, we present a novel algorithm that integrates deep learning with the polycube method (DL-Polycube) to generate high-quality hexahedral (hex) meshes, which are then used to construct volumetric splines for isogeometric analysis. Our DL-Polycube algorithm begins by establishing a connection between surface triangular meshes and polycube structures. We employ deep neural network to classify surface triangular meshes into their corresponding polycube structures. Following this, we combine the acquired polycube structural information with unsupervised learning to perform surface segmentation of triangular meshes. This step addresses the issue of segmentation not corresponding to a polycube while reducing manual intervention. Quality hex meshes are then generated from the polycube structures, with employing octree subdivision, parametric mapping and quality improvement techniques. The incorporation of deep learning for creating polycube structures, combined with unsupervised learning for segmentation of surface triangular meshes, substantially accelerates hex mesh generation. Finally, truncated hierarchical B-splines are constructed on the generated hex meshes. We extract trivariate Bézier elements from these splines and apply them directly in isogeometric analysis. We offer several examples to demonstrate the robustness of our DL-Polycube algorithm.

cs.CG↗

arXiv2024

Ideal noncrystals: A possible new class of ordered matter without apparent broken symmetry

Order and disorder constitute two fundamental and opposite themes in condensed matter physics and materials science. Crystals are considered the epitome of order, characterised by long-range translational order. The discovery of quasicrystals, which exhibit rotational symmetries forbidden in crystals and lack periodicity, led to a paradigm shift in solid-state physics. Moving one step forward, it is intriguing to ask whether ordered matter can exist without apparent symmetry breaking. The same question arises considering how ordered amorphous (noncrystalline) solids can be structured. Here, we present the discovery of ideal noncrystals in two dimensions, which are disordered in the conventional sense, lacking Bragg peaks, but exhibit high orderliness based on the steric order, i.e., they are ideally packed. A path-integral-like scheme reveals the underlying long-range structural correlation. We find that these ideal noncrystals are characterised by phononic vibrational modes following the Debye law, fully affine elastic responses, and suppressed density fluctuations at longer wavelengths, reminiscent of hyperuniformity -- all characteristics typically associated with crystals. Therefore, ideal noncrystals represent a peculiar form of matter with a mixed nature -- noncrystalline yet possessing crystal-like properties. Notably, these states are found to be thermodynamically favourable, indicating them as a possible new class of ordered matter without apparent symmetry breaking. Our findings significantly broaden the conceptualization of ordered states of matter and may contribute to a deeper understanding of entropy-driven ordering, particularly in generic amorphous materials.

cond-mat.soft↗

arXiv2024

HybridOctree_Hex: Hybrid Octree-Based Adaptive All-Hexahedral Mesh Generation with Jacobian Control

We present a new software package, "HybridOctree_Hex," for adaptive all-hexahedral mesh generation based on hybrid octree and quality improvement with Jacobian control. The proposed HybridOctree_Hex begins by detecting curvatures and narrow regions of the input boundary to identify key surface features and initialize an octree structure. Subsequently, a strongly balanced octree is constructed using the balancing and pairing rules. Inspired by our earlier preliminary hybrid octree-based work, templates are designed to guarantee an all-hexahedral dual mesh generation directly from the strongly balanced octree. With these pre-defined templates, the sophisticated hybrid octree construction step is skipped to achieve an efficient implementation. After that, elements outside and around the boundary are removed to create a core mesh. The boundary points of the core mesh are connected to their corresponding closest points on the surface to fill the buffer zone and build the final mesh. Coupled with smart Laplacian smoothing, HybridOctree_Hex takes advantage of a delicate optimization-based quality improvement method considering geometric fitting, Jacobian and scaled Jacobian, to achieve a minimum scaled Jacobian that is higher than $0.5$. We empirically verify the robustness and efficiency of our method by running the HybridOctree_Hex software on dozens of complex 3D models without any manual intervention or parameter adjustment. We provide the HybridOctree_Hex source code, along with comprehensive results encompassing the input and output files and statistical data in the following repository: https://github.com/CMU-CBML/HybridOctree_Hex.

cs.CG↗

arXiv2023

Generate plane quad mesh with neural networks and tree search

The quality of mesh generation has long been considered a vital aspect in providing engineers with reliable simulation results throughout the history of the Finite Element Method (FEM). The element extraction method, which is currently the most robust method, is used in business software. However, in order to speed up extraction, the approach is done by finding the next element that optimizes a target function, which can result in local mesh of bad quality after many time steps. We provide TreeMesh, a method that uses this method in conjunction with reinforcement learning (also possible with supervised learning) and a novel Monte-Carlo tree search (MCTS) (Coulom(2006), Kocsis and Szepesvári(2006), Browne et~al.(2012)). The algorithm is based on a previously proposed approach (Pan et~al.(2021)). After making many improvements on DRL (algorithm, state-action-reward setting) and adding a MCTS, it outperforms the former work on the same boundary. Furthermore, using tree search, our program reveals much preponderance on seed-density-changing boundaries, which is common on thin-film materials.

cs.LG↗

arXiv2023

SRL-Assisted AFM: Generating Planar Unstructured Quadrilateral Meshes with Supervised and Reinforcement Learning-Assisted Advancing Front Method

High-quality mesh generation is the foundation of accurate finite element analysis. Due to the vast interior vertices search space and complex initial boundaries, mesh generation for complicated domains requires substantial manual processing and has long been considered the most challenging and time-consuming bottleneck of the entire modeling and analysis process. In this paper, we present a novel computational framework named ``SRL-assisted AFM" for meshing planar geometries by combining the advancing front method with neural networks that select reference vertices and update the front boundary using ``policy networks." These deep neural networks are trained using a unique pipeline that combines supervised learning with reinforcement learning to iteratively improve mesh quality. First, we generate different initial boundaries by randomly sampling points in a square domain and connecting them sequentially. These boundaries are used for obtaining input meshes and extracting training datasets in the supervised learning module. We then iteratively improve the reinforcement learning model performance with reward functions designed for special requirements, such as improving the mesh quality and controlling the number and distribution of extraordinary points. Our proposed supervised learning neural networks achieve an accuracy higher than 98% on predicting commercial software. The final reinforcement learning neural networks automatically generate high-quality quadrilateral meshes for complex planar domains with sharp features and boundary layers.

math.NA↗

arXiv2021

Achieving adjustable elasticity with non-affine to affine transition

For various engineering and industrial applications it is desirable to realize mechanical systems with broadly adjustable elasticity to respond flexibly to the external environment. Here we discover a topology-correlated transition between affine and non-affine regimes in elasticity in both two- and three-dimensional packing-derived networks. Based on this transition, we numerically design and experimentally realize multifunctional systems with adjustable elasticity. Within one system, we achieve solid-like affine response, liquid-like non-affine response and a continuous tunability in between. Moreover, the system also exhibits a broadly tunable Poisson's ratio from positive to negative values, which is of practical interest for energy absorption and for fracture-resistant materials. Our study reveals a fundamental connection between elasticity and network topology, and demonstrates its practical potential for designing mechanical systems and metamaterials.

cond-mat.mtrl-sci↗

arXiv2021

Influence of Roughness on Granular Avalanches

Combining X-ray tomography with simultaneous shear force measurement, we investigate shear-induced granular avalanches using spherical particles with different surface roughness. We find that systems consisting of particles with large surface roughness display quasi-periodic avalanches interrupted by crackling-like small ones. In contrast, systems consisting of particles with small roughness display no detectable avalanches. The stress drop of quasi-periodic avalanche shows a linear relation with the correlation length of particle non-affine displacement, suggesting that roughness enhances inter-particle locking and hence particle-level dynamic correlation length. However, the nonaffine displacement is two orders of magnitude smaller than particle size, indicating that stress is mainly released on the length scale of roughness. The correlation length of non-affine displacements abruptly increases when a quasi-periodic avalanche occurs, suggesting that quasi-periodic avalanches can be interpreted as a spinodal nucleation event in a first-order phase transition.

cond-mat.soft↗

arXiv2021

Experimental Test on Edwards Volume Ensemble of Tapped Granular Packings

Using X-ray tomography, we experimentally investigate granular packings subject to mechanical tapping for three types of beads with different friction coefficients. We validate Edwards volume ensemble in these three-dimensional granular systems and establish a granular version of thermodynamic zeroth law. Within Edwards framework, we also explicitly clarify how friction influences granular statistical mechanics as modifying the density of states, which allows us to determine the entropy as a function of packing fraction and friction subsequently. Additionally, we obtain a granular jamming phase diagram based on geometric coordination number and packing fraction.

cond-mat.soft↗