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Zhenzhong Wang

Publications and source records attributed to Zhenzhong Wang.

At least 19 recordsLinked to original sources
arXiv2026

Geometry-Aware Anisotropic Boundary Correction for Aerodynamic Simulation

Aerodynamic simulation is a key component of engineering shape design, where core quantities such as the surface pressure coefficient strongly depend on flow dynamics near solid boundaries. Neural operators provide an efficient alternative to expensive Computational Fluid Dynamics (CFD) solvers. However, conventional methods treat the boundary region isotropically, failing to account for the distinct physical behaviors along the boundaries. In reality, the aerodynamic process exhibits anisotropy: along the tangential direction, flow propagates along the wall; along the normal direction, physical quantities are constrained by the wall. To explicitly model the distinct physical behaviors, we propose GeoABC, a geometry-conditioned anisotropic boundary correction framework. GeoABC leverages the boundary geometries to introduce direction-aware boundary correction into the intermediate representations of neural operators, transforming boundary geometry from static input features into a structural prior that modulates physical prediction. On 2D airfoil and 3D car tasks, GeoABC consistently adapts to multiple neural operator backbones, reducing near-boundary relative $L_2$ error by $\sim$38\% on average, narrowing the structural near-wall gap shared by mainstream neural operators, and advancing neural operators toward high-fidelity aerodynamic simulation.

physics.flu-dyn↗
arXiv2026

VisionLaw: Inferring Interpretable Intrinsic Dynamics from Visual Observations via Bilevel Optimization

The intrinsic dynamics of an object governs its physical behavior in the real world, playing a critical role in enabling physically plausible interactive simulation with 3D assets. Existing methods have attempted to infer the intrinsic dynamics of objects from visual observations, but generally face two major challenges: one line of work relies on manually defined constitutive priors, making it difficult to align with actual intrinsic dynamics; the other models intrinsic dynamics using neural networks, resulting in limited interpretability and poor generalization. To address these challenges, we propose VisionLaw, a bilevel optimization framework that infers interpretable expressions of intrinsic dynamics from visual observations. At the upper level, we introduce an LLMs-driven decoupled constitutive evolution strategy, where LLMs are prompted to act as physics experts to generate and revise constitutive laws, with a built-in decoupling mechanism that substantially reduces the search complexity of LLMs. At the lower level, we introduce a vision-guided constitutive evaluation mechanism, which utilizes visual simulation to evaluate the consistency between the generated constitutive law and the underlying intrinsic dynamics, thereby guiding the upper-level evolution. Experiments on both synthetic and real-world datasets demonstrate that VisionLaw can effectively infer interpretable intrinsic dynamics from visual observations. It significantly outperforms existing state-of-the-art methods and exhibits strong generalization for interactive simulation in novel scenarios.

cs.CV↗
arXiv2026

Training-Free Diffusion-Driven Modeling of Pareto Set Evolution for Dynamic Multiobjective Optimization

Dynamic multiobjective optimization problems (DMOPs) feature time-varying objectives, which cause the Pareto optimal solution (POS) set to drift over time and make it difficult to maintain both convergence and diversity under limited response time. Many existing prediction-based dynamic multiobjective evolutionary algorithms (DMOEAs) either depend on learned models with nontrivial training cost or employ one-step population mapping, which may overlook the gradual nature of POS evolution. This paper proposes DD-DMOEA, a training-free diffusion-based dynamic response mechanism for DMOPs. The key idea is to treat the POS obtained in the previous environment as a "noisy" sample set and to guide its evolution toward the current POS through an analytically constructed multi-step denoising process. A knee-point-based auxiliary strategy is used to specify the target region in the new environment, and an explicit probability-density formulation is derived to compute the denoising update without neural training. To reduce the risk of misleading guidance caused by knee-point prediction errors, an uncertainty-aware scheme adaptively adjusts the guidance strength according to the historical prediction deviation. Experiments on the CEC2018 dynamic multiobjective benchmarks show that DD-DMOEA achieves competitive or better convergence-diversity performance and provides faster dynamic response than several state-of-the-art DMOEAs.

cs.NE↗
arXiv2026

Neuro-Symbolic Multitasking: A Unified Framework for Discovering Generalizable Solutions to PDE Families

Solving Partial Differential Equations (PDEs) is fundamental to numerous scientific and engineering disciplines. A common challenge arises from solving the PDE families, which are characterized by sharing an identical mathematical structure but varying in specific parameters. Traditional numerical methods, such as the finite element method, need to independently solve each instance within a PDE family, which incurs massive computational cost. On the other hand, while recent advancements in machine learning PDE solvers offer impressive computational speed and accuracy, their inherent ``black-box" nature presents a considerable limitation. These methods primarily yield numerical approximations, thereby lacking the crucial interpretability provided by analytical expressions, which are essential for deeper scientific insight. To address these limitations, we propose a neuro-assisted multitasking symbolic PDE solver framework for PDE family solving, dubbed NMIPS. In particular, we employ multifactorial optimization to simultaneously discover the analytical solutions of PDEs. To enhance computational efficiency, we devise an affine transfer method by transferring learned mathematical structures among PDEs in a family, avoiding solving each PDE from scratch. Experimental results across multiple cases demonstrate promising improvements over existing baselines, achieving up to a $\sim$35.7% increase in accuracy while providing interpretable analytical solutions.

cs.AI↗
arXiv2026

Endowing Molecular Language with Geometry Perception via Modality Compensation for High-Throughput Quantum Hamiltonian Prediction

The quantum Hamiltonian is a fundamental property that governs a molecule's electronic structure and behavior, and its calculation and prediction are paramount in computational chemistry and materials science. Accurate prediction is highly reliant on extensive training data, including precise molecular geometries and the Hamiltonian matrices, which are expensive to acquire via either experimental or computational methods. Towards a fast yet accurate method for Hamiltonian prediction, we first introduce a geometry information-aware molecular language model to bypass the use of expensive molecular geometries by only using the readily available molecular language -- simplified molecular input line entry system (SMILES). Our method employs multimodal alignment to bridge the relationship between SMILES strings and their corresponding molecular geometries. Recognizing that the molecular language inherently lacks explicit geometric information, we propose a geometry modality compensation strategy to imbue molecular language representations with essential geometric features, thereby enabling accurate predictions using SMILES. In addition, given the high cost of acquiring Hamiltonian data, we devise a weakly supervised strategy to fine-tune the molecular language model, thus improving the data efficiency. Theoretically, we prove that the prediction generalization error without explicit molecular geometry can be bounded through our modality compensation scheme. Empirically, our method achieves superior computational efficiency, providing up to 100x speedup over conventional quantum mechanical methods while maintaining comparable prediction accuracy. We further demonstrate the practical case study of our approach in the screening of electrolyte formulations.

cs.CE↗
arXiv2026

Multiphase transport and compositional mixing mechanisms in twin-wire laser directed energy deposition: toward process stability and graded material fabrication

Twin-wire laser directed energy deposition (TW-LDED) provides a promising route for alloying and fabrication of compositionally graded structures. However, inherent multiparameter coupling in twin-wire systems critically exacerbates both process instabilities and compositional inhomogeneity. This unresolved issue escalates into a fundamental technological bottleneck, as the underlying physical mechanisms remain poorly understood. This study developed a high-fidelity multi-physics and multiphase simulation framework coupled with experimental validation to reveal thermal-fluid behavior and heat-mass transfer mechanisms in TW-LDED using Inconel 718 and SS316L fine wires. Three distinct transition modes were identified: twin-wire melt droplet, twin-wire liquid bridge, and droplet-bridge mixed transitions, with the twin-wire liquid bridge regime delivering optimal stability and uniform mixing. Parametric analysis demonstrates that increasing wire feeding speed or decreasing wire initial height promotes stable liquid bridge formation, while small laser spots at low feeding speeds induce excessive volumetric energy density and bridge instability. Simulation and single-track experiments confirm that liquid bridge transitions reduce dimensional fluctuations by 85% while enhancing compositional homogeneity. Conversely, the melt droplet-bridge transition mode creates periodic flow switching and compositional discontinuities along the scan direction. Finally, a 60 mm functionally graded ring was successfully fabricated using optimized parameters, achieving uniform elemental distribution in the transition zone without significant segregation, validating the feasibility of TW-LDED for functionally graded components.

nlin.CD↗
arXiv2026

Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Models

Providing explainable molecular property predictions is critical for many scientific domains, such as drug discovery and material science. Though transformer-based language models have shown great potential in accurate molecular property prediction, they neither provide chemically meaningful explanations nor faithfully reveal the molecular structure-property relationships. In this work, we develop a framework for explainable molecular property prediction based on language models, dubbed as Lamole, which can provide chemical concepts-aligned explanations. We take a string-based molecular representation -- Group SELFIES -- as input tokens to pretrain and fine-tune our Lamole, as it provides chemically meaningful semantics. By disentangling the information flows of Lamole, we propose combining self-attention weights and gradients for better quantification of each chemically meaningful substructure's impact on the model's output. To make the explanations more faithfully respect the structure-property relationship, we then carefully craft a marginal loss to explicitly optimize the explanations to be able to align with the chemists' annotations. We bridge the manifold hypothesis with the elaborated marginal loss to prove that the loss can align the explanations with the tangent space of the data manifold, leading to concept-aligned explanations. Experimental results over six mutagenicity datasets and one hepatotoxicity dataset demonstrate Lamole can achieve comparable classification accuracy and boost the explanation accuracy by up to 14.3%, being the state-of-the-art in explainable molecular property prediction.

cs.LG↗
arXiv2026

Crystalline Material Discovery in the Era of Artificial Intelligence

Crystalline materials, with symmetrical and periodic structures, exhibit a wide spectrum of properties and have been widely used in numerous applications across electronics, energy, and beyond. For crystalline materials discovery, traditional experimental and computational approaches are time-consuming and expensive. In these years, thanks to the explosive amount of crystalline materials data, great interest has been given to data-driven materials discovery. Particularly, recent advancements have exploited the expressive representation ability of deep learning to model the highly complex atomic systems within crystalline materials, opening up new avenues for fast and accurate materials discovery. These works typically focus on four types of tasks, including physicochemical property prediction, crystalline material synthesis, aiding characterization, and accelerating theoretical computations. Despite the remarkable progress, there is still a lack of systematic investigation to summarize their distinctions and limitations. To fill this gap, we systematically investigated the progress made in recent years. We first introduce several data representations of the crystalline materials. Based on the representations, we summarize various fundamental deep learning models and their tailored usages in various material discovery tasks. Finally, we highlight the remaining challenges and propose future directions.

cs.CE↗
arXiv2025

Cross-Field Interface-Aware Neural Operators for Multiphase Flow Simulation

Multiphase flow simulation is critical in science and engineering but incurs high computational costs due to complex field discontinuities and the need for high-resolution numerical meshes. While Neural Operators (NOs) offer an efficient alternative for solving Partial Differential Equations (PDEs), they struggle with two core challenges unique to multiphase systems: spectral bias caused by spatial heterogeneity at phase interfaces, and the persistent scarcity of expensive, high-resolution field data. This work introduces the Interface Information Aware Neural Operator (IANO), a novel architecture that mitigates these issues by leveraging readily obtainable interface data (e.g., topology and position). Interface data inherently contains the high-frequency features not only necessary to complement the physical field data, but also help with spectral bias. IANO incorporates an interface-aware function encoding mechanism to capture dynamic coupling, and a geometry-aware positional encoding method to enhance spatial fidelity for pointwise super-resolution. Empirical results across multiple multiphase flow cases demonstrate that IANO achieves significant accuracy improvements (up to $\sim$10\%) over existing NO baselines. Furthermore, IANO exhibits superior generalization capabilities in low-data and noisy settings, confirming its utility for practical, data-efficient $\text{AI}$-based multiphase flow simulations.

physics.flu-dyn↗
arXiv2025

PEGNet: A Physics-Embedded Graph Network for Long-Term Stable Multiphysics Simulation

Accurate and efficient simulations of physical phenomena governed by partial differential equations (PDEs) are important for scientific and engineering progress. While traditional numerical solvers are powerful, they are often computationally expensive. Recently, data-driven methods have emerged as alternatives, but they frequently suffer from error accumulation and limited physical consistency, especially in multiphysics and complex geometries. To address these challenges, we propose PEGNet, a Physics-Embedded Graph Network that incorporates PDE-guided message passing to redesign the graph neural network architecture. By embedding key PDE dynamics like convection, viscosity, and diffusion into distinct message functions, the model naturally integrates physical constraints into its forward propagation, producing more stable and physically consistent solutions. Additionally, a hierarchical architecture is employed to capture multi-scale features, and physical regularization is integrated into the loss function to further enforce adherence to governing physics. We evaluated PEGNet on benchmarks, including custom datasets for respiratory airflow and drug delivery, showing significant improvements in long-term prediction accuracy and physical consistency over existing methods. Our code is available at https://github.com/Yanghuoshan/PEGNet.

cs.LG↗
arXiv2025

Phys4DGen: Physics-Compliant 4D Generation with Multi-Material Composition Perception

4D content generation aims to create dynamically evolving 3D content that responds to specific input objects such as images or 3D representations. Current approaches typically incorporate physical priors to animate 3D representations, but these methods suffer from significant limitations: they not only require users lacking physics expertise to manually specify material properties but also struggle to effectively handle the generation of multi-material composite objects. To address these challenges, we propose Phys4DGen, a novel 4D generation framework that integrates multi-material composition perception with physical simulation. The framework achieves automated, physically plausible 4D generation through three innovative modules: first, the 3D Material Grouping module partitions heterogeneous material regions on 3D representations' surfaces via semantic segmentation; second, the Internal Physical Structure Discovery module constructs the mechanical structure of object interiors; finally, we distill physical prior knowledge from multimodal large language models to enable rapid and automatic material properties identification for both objects' surfaces and interiors. Experiments on both synthetic and real-world datasets demonstrate that Phys4DGen can generate high-fidelity 4D content with physical realism in open-world scenarios, significantly outperforming state-of-the-art methods.

cs.CV↗
arXiv2025

Fading the Digital Ink: A Universal Black-Box Attack Framework for 3DGS Watermarking Systems

With the rise of 3D Gaussian Splatting (3DGS), a variety of digital watermarking techniques, embedding either 1D bitstreams or 2D images, are used for copyright protection. However, the robustness of these watermarking techniques against potential attacks remains underexplored. This paper introduces the first universal black-box attack framework, the Group-based Multi-objective Evolutionary Attack (GMEA), designed to challenge these watermarking systems. We formulate the attack as a large-scale multi-objective optimization problem, balancing watermark removal with visual quality. In a black-box setting, we introduce an indirect objective function that blinds the watermark detector by minimizing the standard deviation of features extracted by a convolutional network, thus rendering the feature maps uninformative. To manage the vast search space of 3DGS models, we employ a group-based optimization strategy to partition the model into multiple, independent sub-optimization problems. Experiments demonstrate that our framework effectively removes both 1D and 2D watermarks from mainstream 3DGS watermarking methods while maintaining high visual fidelity. This work reveals critical vulnerabilities in existing 3DGS copyright protection schemes and calls for the development of more robust watermarking systems.

cs.CR↗
arXiv2025

Pharmacokinetic characteristics of Jinhong tablets in normal, chronic superficial gastritis and intestinal microbial disorder rats

Jinhong tablet (JHT), a traditional Chinese medicine made from four herbs, effectively treats chronic superficial gastritis (CSG) by soothing the liver, relieving depression, regulating qi, and promoting blood circulation. However, its pharmacokinetics are underexplored. This study investigates JHT's pharmacokinetics in normal rats and its differences in normal, CSG, and intestinal microbial disorder rats. A quantitative method for seven active ingredients in rat plasma was established using UPLC-TQ-MS/MS. After administering various JHT doses, plasma concentrations were measured to assess pharmacokinetics in normal rats. The pharmacokinetics of four main ingredients were compared in normal, CSG, and fecal microbiota transplantation (FMT) rats. Intestinal microbial changes were evaluated by high-throughput sequencing. Spearman correlation analysis linked ingredient exposure to gut microbiota disturbances. The method showed good linearity, precision, accuracy, extraction recovery, and stability. In normal rats, all seven ingredients were rapidly absorbed. Tetrahydropalmatine, corydaline, costunolide, and rhamnosylvitexin had good exposure, while dehydrocorydaline, allocryptopine, and palmatine hydrochloride had low exposure. Tetrahydropalmatine, corydaline, and costunolide followed linear pharmacokinetics (AUC0-t, Cmax) at doses of 0.7-5.6 g/kg, while rhamnosylvitexin and dehydrocorydaline showed linearity at 0.7-2.8 g/kg. In CSG and FMT rats, pharmacokinetic differences were observed. CSG enhanced costunolide exposure and Cmax, and increased rhamnosylvitexin exposure. FMT raised corydaline exposure and rhamnosylvitexin Cmax, linked to 20 bacterial genera.

q-bio.QM↗
arXiv2025

NexusSplats: Efficient 3D Gaussian Splatting in the Wild

Photorealistic 3D reconstruction of unstructured real-world scenes remains challenging due to complex illumination variations and transient occlusions. Existing methods based on Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) struggle with inefficient light decoupling and structure-agnostic occlusion handling. To address these limitations, we propose NexusSplats, an approach tailored for efficient and high-fidelity 3D scene reconstruction under complex lighting and occlusion conditions. In particular, NexusSplats leverages a hierarchical light decoupling strategy that performs centralized appearance learning, efficiently and effectively decoupling varying lighting conditions. Furthermore, a structure-aware occlusion handling mechanism is developed, establishing a nexus between 3D and 2D structures for fine-grained occlusion handling. Experimental results demonstrate that NexusSplats achieves state-of-the-art rendering quality and reduces the number of total parameters by 65.4\%, leading to 2.7$\times$ faster reconstruction.

cs.CV↗
arXiv2024

Cross-Modality Attack Boosted by Gradient-Evolutionary Multiform Optimization

In recent years, despite significant advancements in adversarial attack research, the security challenges in cross-modal scenarios, such as the transferability of adversarial attacks between infrared, thermal, and RGB images, have been overlooked. These heterogeneous image modalities collected by different hardware devices are widely prevalent in practical applications, and the substantial differences between modalities pose significant challenges to attack transferability. In this work, we explore a novel cross-modal adversarial attack strategy, termed multiform attack. We propose a dual-layer optimization framework based on gradient-evolution, facilitating efficient perturbation transfer between modalities. In the first layer of optimization, the framework utilizes image gradients to learn universal perturbations within each modality and employs evolutionary algorithms to search for shared perturbations with transferability across different modalities through secondary optimization. Through extensive testing on multiple heterogeneous datasets, we demonstrate the superiority and robustness of Multiform Attack compared to existing techniques. This work not only enhances the transferability of cross-modal adversarial attacks but also provides a new perspective for understanding security vulnerabilities in cross-modal systems.

cs.CV↗
arXiv2024

Phy124: Fast Physics-Driven 4D Content Generation from a Single Image

4D content generation focuses on creating dynamic 3D objects that change over time. Existing methods primarily rely on pre-trained video diffusion models, utilizing sampling processes or reference videos. However, these approaches face significant challenges. Firstly, the generated 4D content often fails to adhere to real-world physics since video diffusion models do not incorporate physical priors. Secondly, the extensive sampling process and the large number of parameters in diffusion models result in exceedingly time-consuming generation processes. To address these issues, we introduce Phy124, a novel, fast, and physics-driven method for controllable 4D content generation from a single image. Phy124 integrates physical simulation directly into the 4D generation process, ensuring that the resulting 4D content adheres to natural physical laws. Phy124 also eliminates the use of diffusion models during the 4D dynamics generation phase, significantly speeding up the process. Phy124 allows for the control of 4D dynamics, including movement speed and direction, by manipulating external forces. Extensive experiments demonstrate that Phy124 generates high-fidelity 4D content with significantly reduced inference times, achieving stateof-the-art performance. The code and generated 4D content are available at the provided link: https://anonymous.4open.science/r/BBF2/.

cs.CV↗
arXiv2024

Adversarial Learning for Neural PDE Solvers with Sparse Data

Neural network solvers for partial differential equations (PDEs) have made significant progress, yet they continue to face challenges related to data scarcity and model robustness. Traditional data augmentation methods, which leverage symmetry or invariance, impose strong assumptions on physical systems that often do not hold in dynamic and complex real-world applications. To address this research gap, this study introduces a universal learning strategy for neural network PDEs, named Systematic Model Augmentation for Robust Training (SMART). By focusing on challenging and improving the model's weaknesses, SMART reduces generalization error during training under data-scarce conditions, leading to significant improvements in prediction accuracy across various PDE scenarios. The effectiveness of the proposed method is demonstrated through both theoretical analysis and extensive experimentation. The code will be available.

cs.LG↗
arXiv2024

Ask, Attend, Attack: A Effective Decision-Based Black-Box Targeted Attack for Image-to-Text Models

While image-to-text models have demonstrated significant advancements in various vision-language tasks, they remain susceptible to adversarial attacks. Existing white-box attacks on image-to-text models require access to the architecture, gradients, and parameters of the target model, resulting in low practicality. Although the recently proposed gray-box attacks have improved practicality, they suffer from semantic loss during the training process, which limits their targeted attack performance. To advance adversarial attacks of image-to-text models, this paper focuses on a challenging scenario: decision-based black-box targeted attacks where the attackers only have access to the final output text and aim to perform targeted attacks. Specifically, we formulate the decision-based black-box targeted attack as a large-scale optimization problem. To efficiently solve the optimization problem, a three-stage process \textit{Ask, Attend, Attack}, called \textit{AAA}, is proposed to coordinate with the solver. \textit{Ask} guides attackers to create target texts that satisfy the specific semantics. \textit{Attend} identifies the crucial regions of the image for attacking, thus reducing the search space for the subsequent \textit{Attack}. \textit{Attack} uses an evolutionary algorithm to attack the crucial regions, where the attacks are semantically related to the target texts of \textit{Ask}, thus achieving targeted attacks without semantic loss. Experimental results on transformer-based and CNN+RNN-based image-to-text models confirmed the effectiveness of our proposed \textit{AAA}.

cs.AI↗