论文检索

Yongfu Wei, Yan Lin 0006, Hongfan Gao, Ronghui Xu 0001, Sean Bin Yang, Jilin Hu

The advancement of intelligent transportation systems has led to a growing demand for accurate path representations, which are essential for tasks such as travel time estimation, path ranking, and trajectory analysis. However, traditional path representation learning (PRL) methods often focus solely on single-modal road network data, overlooking important physical and regional factors that influence real-world traffic dynamics. To overcome this limitation, we introduce Path-LLM, a multi-modal path representation learning model that integrates large language models (LLMs) into PRL. Our approach leverages LLMs to interpret both topological and textual data, enabling robust multi-modal path representations. To effectively align and merge these modalities, we propose TPalign, a contrastive learning-based pretraining strategy that ensures alignment within the embedding space. We then present TPfusion, a multimodal fusion module that dynamically adjusts the weight of each modality before integration. To further optimize LLM training, we introduce a Two-stage Overlapping Curriculum Learning (TOCL) approach, which progressively increases the complexity of the training data. Finally, we evaluate Path-LLM on three real-world datasets across traditional PRL downstream tasks, achieving up to a 61.84% improvement in path ranking performance on the Xi'an dataset. Additionally, Path-LLM demonstrates superior performance in both few-shot and zero-shot learning scenarios. Our code is available at: https://github.com/decisionintelligence/Path-LLM.

Leon Trampert, Lorenz Hetterich, Lukas Gerlach 0001, Mona Schappert, Christian Rossow, Michael Schwarz 0001

Browser APIs such as WebHID, WebUSB, Web Serial, and Web MIDI enable web applications to interact directly with external devices. The support of such APIs in Chromium-based browsers, such as Chrome and Edge, radically changes the threat model for peripherals and increases the attack surface. In the past, devices could assume a trusted host, i.e., the operating system. Now, the host is a potentially malicious website and cannot be trusted. We show how this changed threat model leads to security and privacy problems, up to a complete compromise of the operating system. While the API specifications list initial security considerations, they shift the responsibility to (unprepared) device vendors. We systematically analyze the security implications of external devices exposed by such new APIs. By reverse-engineering peripheral devices of several popular widespread vendors, we show that many vendors allow controlling devices via Web APIs up to reprogramming or even fully replacing the firmware. Consequently, web attackers can reprogram devices with malicious payloads and custom firmware without requiring any physical interaction. To demonstrate the security implications, we build several full-chain exploits, leading to arbitrary code execution on the victim system, circumventing the browser sandbox. Our research shows that browser security should not rely on the secure implementation of third-party hardware.

Zewen Liu 0005, Xiaoda Wang, Bohan Wang, Zijie Huang 0002, Carl Yang 0001, Wei Jin 0009

Graph Neural Networks (GNNs) and differential equations (DEs) are two rapidly advancing areas of research that have shown remarkable synergy in recent years. GNNs have emerged as powerful tools for learning on graph-structured data, while differential equations provide a principled framework for modeling continuous dynamics across time and space. The intersection of these fields has led to innovative approaches that leverage the strengths of both, enabling applications in physics-informed learning, spatiotemporal modeling, and scientific computing. This survey aims to provide a comprehensive overview of the burgeoning research at the intersection of GNNs and DEs. We will categorize existing methods, discuss their underlying principles, and highlight their applications across domains such as molecular modeling, traffic prediction, and epidemic spreading. Furthermore, we identify open challenges and outline future research directions to advance this interdisciplinary field. A comprehensive paper list is provided at https://github.com/Emory-Melody/Awesome-Graph-NDEs.

Chuan Qin 0002, Xin Chen, Chengrui Wang, Pengmin Wu, Xi Chen 0073, Yihang Cheng 0001, Jingyi Zhao, Meng Xiao 0001, Xiangchao Dong, Qingqing Long 等

In recent years, the rapid advancement of Artificial Intelligence (AI) technologies, particularly Large Language Models (LLMs), has revolutionized the paradigm of scientific discovery, establishing AI-for-Science (AI4Science) as a dynamic and evolving field. However, there is still a lack of an effective framework for the overall assessment of AI4Science, particularly from a holistic perspective on data quality and model capability. Therefore, in this study, we propose SciHorizon, a comprehensive assessment framework designed to benchmark the readiness of AI4Science from both scientific data and LLM perspectives. First, we introduce a generalizable framework for assessing AI-ready scientific data, encompassing four key dimensions-Quality, FAIRness, Explainability, and Compliance-which are subdivided into 15 sub-dimensions. Drawing on data resource papers published between 2018 and 2023 in peer-reviewed journals, we present recommendation lists of AI-ready datasets for Earth, Life, and Materials Sciences, making a novel and original contribution to the field. Concurrently, to assess the capabilities of LLMs across multiple scientific disciplines, we establish 16 assessment dimensions based on five core indicators-Knowledge, Understanding, Reasoning, Multimodality, and Values-spanning Mathematics, Physics, Chemistry, Life Sciences, and Earth and Space Sciences. Using the developed benchmark datasets, we have conducted a comprehensive evaluation of over 50 representative open-source and closed-source LLMs. All the results are publicly available and can be accessed online at www.scihorizon.cn/en.

Shilong Tao, Zhe Feng, Haonan Sun, Zhanxing Zhu, Yunhuai Liu

Scientific computing for large deformation of elastic-plastic solids is critical for numerous real-world applications. Classical numerical solvers rely primarily on local discrete linear approximation and are constrained by an inherent trade-off between accuracy and efficiency. Recently, deep learning models have achieved impressive progress in solving the continuum mechanism. While previous models have explored various architectures and constructed coefficient-solution mappings, they are designed for general instances without considering specific problem properties and hard to accurately handle with complex elastic-plastic solids involving contact, loading and unloading. In this work, we take stretch bending, a popular metal fabrication technique, as our case study and introduce LaDEEP, a deep learning-based surrogate model for La rge De formation of Elastic-Plastic Solids. We encode the partitioned regions of the involved slender solids into a token sequence to maintain their essential order property. To characterize the physical process of the solid deformation, a two-stage Transformer-based module is designed to predict the deformation with the sequence of tokens as input. Empirically, LaDEEP achieves five magnitudes faster speed than finite element methods with a comparable accuracy, and gains 20.47% relative improvement on average compared to other deep learning baselines. We have also deployed our model into a real-world industrial production system, and it has shown remarkable performance in both accuracy and efficiency. Code is available at https://github.com/therontau0054/LaDEEP.

Jingyuan Zheng, Xin Zhang 0079, Zhilin Qi, Ruiang Qiu, Dongjing Wang, Haiping Zhang 0001, Dongjin Yu

Extreme precipitation, as a core causative factor of meteorological disasters, poses significant challenges for accurate short-term forecasting due to the chaotic nature of precipitation systems and their multi-scale spatio-temporal evolution. Traditional numerical models are notably affected by error accumulation, while existing deep learning models still face dual limitations in physical fidelity and multi-scale feature extraction. To Address these issues, we propose an innovative Multi-scale Physics-informed Transformer with spatio-temporal feature adapter for extreme precipitation nowcasting, termed MPFormer. Our framework comprises two core components: the deterministic Evolution Network and the stochastic Generative Network. The Evolution Network integrates a novel Scale-Aware Temporal Residual Modulation Transformer (STRMT) encoder that captures multi-scale storm dynamics through residual temporal attention. The Generative Network introduces spatio-temporal adapters as lightweight transfer modules for probabilistic modeling. We develop a Multi-scale Physics-informed Loss with three innovations: 1) dynamic weight scheduling for feature fusion, 2) physical constraints preserving storm evolution patterns, and 3) entropy-based uncertainty calibration. Experiments based on MRMS radar data from North America demonstrate that the model can generate high-resolution forecasts (2km grid) with a 3-hour lead time over an area of 2048×2048 square kilometers. Compared to the state-of-the-art technologies, the proposed framework shows significant effectiveness and superiority in metrics such as CSIN, offering a new paradigm that combines physical interpretability with engineering practicality for extreme weather warnings and disaster prevention in smart cities.

Mengtao Yan, Qi Wang 0123, Haining Wang, Ruizhi Chengze, Yi Zhang 0164, Hongsheng Liu 0002, Zidong Wang 0010, Fan Yu 0004, Qi Qi 0003, Hao Sun 0002

Simulation of fluid flows is crucial for modeling physical phenomena like meteorology, aerodynamics, and biomedicine. Classical numerical solvers often require fine spatiotemporal grids to satisfy stability, consistency, and convergence conditions, leading to substantial computational costs. Although machine learning has demonstrated better efficiency, they typically suffer from issues of interpretability, generalizability, and data dependency. Hence, we propose a learnable and differentiable finite volume solver, called LDSolver, designed for efficient and accurate simulation of fluid flows on spatiotemporal coarse grids. LDSolver comprises two key components: (1) a differentiable finite volume solver, and (2) an learnable module providing equivalent approximation for fluxes (derivatives and interpolations), and temporal error correction on coarse grids. Even with limited training data (e.g., only a few trajectories), our model could accelerate the simulation while maintaining a high accuracy with superior generalizability. Experiments on different flow systems (e.g., Burgers, decaying, forced and shear flows) show that LDSolver achieves state-of-the-art performance, surpassing baseline models with notable margins.

Jianwen Sun, Qirong Chen, Zhenya Huang, Zhihai Hu, Ruxia Liang, Xiaoxuan Shen

Memory behavior modeling is a key topic in cognitive psychology and education. Traditional approaches use experimental data to build memory equations, but these models often lack precision and are debated in form. Recently, data-driven methods have improved predictive accuracy but struggle with interpretability, limiting cognitive insights. Although knowledge-informed neural networks have succeeded in fields like physics, their use in behavior modeling is still limited. This paper proposes a Self-evolving Psychology-informed Neural Network (SPsyINN), which leverages classical memory equations as knowledge modules to constrain neural network training. To address challenges such as the difficulty in quantifying descriptors and the limited interpretability of classical memory equations, a genetic symbolic regression algorithm is introduced to conduct evolutionary searches for more optimal expressions based on classical memory equations, enabling the mutual progress of the knowledge module and the neural network module. Specifically, the proposed approach combines genetic symbolic regression and neural networks in a parallel training framework, with a dynamic joint optimization loss function ensuring effective knowledge alignment between the two modules. Then, for addressing the training efficiency differences arising from the distinct optimization methods and computational hardware requirements of genetic algorithms and neural networks, an asynchronous interaction mechanism mediated by proxy data is developed to facilitate effective communication between modules and improve optimization efficiency. Finally, a denoising module is integrated into the neural network to enhance robustness against data noise and improve generalization performance. Experimental results on five large-scale real-world memory behavior demonstrate that SPsyINN outperforms state-of-the-art methods in predictive accuracy. Ablation studies confirm the model's co-evolution capability, improving accuracy while discovering more interpretable memory equations, showing its potential for psychological research. Our code is released at: https://github.com/JiaqiDijon/SPsyINN

Patrick Soga, Zhenyu Lei 0004, Yinhan He, Camille L. Bilodeau, Jundong Li

Predicting changes in binding free energy (ΔΔ G) is a vital task in protein engineering and protein-protein interaction (PPI) engineering for drug discovery. Previous works have observed a high correlation between ΔΔ G and entropy, using probabilities of biologically important objects such as side chain angles and residue identities to estimate ΔΔ G. However, estimating the full conformational distribution of a protein complex is generally considered intractable. In this work, we propose a new approach to ΔΔ G prediction that avoids this issue by instead leveraging energy-based models for estimating the probability of a complex's conformation. Specifically, we novelly decompose ΔΔ G into a sequence-based component estimated by an inverse folding model and a structure-based component estimated by an energy model. This decomposition is made tractable by assuming equilibrium between the bound and unbound states, allowing us to simplify the estimation of degeneracies associated with each state. Unlike previous deep learning-based methods, our method incorporates an energy-based physical inductive bias by connecting the often-used sequence log-odds ratio-based approach to ΔΔ G prediction with a new ΔΔ E term grounded in statistical mechanics. We demonstrate superiority over existing state-of-the-art structure and sequence-based deep learning methods in ΔΔ G prediction and antibody optimization against SARS-CoV-2.

Minbo Ma, Kai Tang, Huan Li 0003, Fei Teng 0001, Dalin Zhang 0001, Tianrui Li 0001

Multivariate Time Series Forecasting (MTSF) has long been a key research focus. Traditionally, these studies assume a fixed number of variables, but in real-world applications, Cyber-Physical Systems often expand as new sensors are deployed, increasing variables in MTSF. In light of this, we introduce a novel task, Expanding-variate Time Series Forecasting (EVTSF). This task presents unique challenges, specifically (1) handling inconsistent data shapes caused by adding new variables, and (2) addressing imbalanced spatio-temporal learning, where expanding variables have limited observed data due to the necessity for timely operation. To address these challenges, we propose STEV, a flexible spatio-temporal forecasting framework. STEV includes a new Flat Scheme to tackle the inconsistent data shape issue, which extends the graph-based spatio-temporal modeling architecture into 1D space by flattening the 2D samples along the variable dimension, making the model variable-scale-agnostic while still preserving dynamic spatial correlations through a holistic graph. Additionally, we introduce a novel Spatio-temporal Focal Learning strategy that incorporates a negative filter to resolve potential conflicts between contrastive learning and graph representation, and a focal contrastive loss as its core to guide the framework to focus on optimizing the expanding variables. To evaluate the effectiveness of STEV, we benchmark EVTSF performance on three real-world datasets from various domains and compare it against three potential solutions employing state-of-the-art (SOTA) MTSF models tailored for EVSTF. Experimental results show that STEV significantly outperforms its competitors, especially in handling expanding variables. Notably, STEV, with only 5% of observations during the expanding period, is on par with SOTA MTSF models trained with complete data. Further exploration of various expanding scenarios underscores the generalizability of STEV in real-world applications.

Jie Lv, Shuyuan Yang 0001, Zhixi Feng

This paper studies grid-free point process modeling under varying fluid parameters. Existing methods rely on grid-based approaches or fixed parameters, making it challenging to handle complex nonlinear dynamics and out-of-distribution (OOD) scenarios. To address this, we propose Adaptive Perturbation Graph ODE (AGODE), a novel framework that integrates three key innovations: (1) an adaptive conditioning mechanism for physical parameter adaptation(2) a continuous graph neural ODE for spatiotemporal evolution modeling, and (3) a perturbation module with mutual information maximization for uncertainty quantification. AGODE employs graph neural networks to encode unstructured point cloud data into latent dynamics governed by neural ODEs, where physical parameters are injected through context-aware conditioning vectors. The perturbation module generates diverse trajectory samples by introducing stochastic noise during ODE integration, while contrastive learning aligns predictions with physical contexts to filter implausible outcomes. Extensive experiments across five fluid dynamics benchmarks (Prometheus, Navier-Stokes, Spherical-SWE, 3D Reaction-Diffusion, ERA5) demonstrate AGODE's state-of-the-art performance. Specifically, AGODE achieves MSE of 0.0302/0.0312 (in-domain/OOD) on Prometheus, outperforming PURE by 6.5%/5.0%, and reduces Navier-Stokes errors by 28.1% compared to physics-informed NMO. Notably, AGODE maintains superior OOD generalization with only 2.9% average error increase versus 7.8% for baselines, while its uncertainty quantification improves prediction reliability by 41% (95% confidence interval coverage). These results validate AGODE's capabilities in continuous spatiotemporal modeling, multi-parameter adaptation, and robust uncertainty estimation for complex fluid systems.

Yingtao Luo, Shikai Fang, Binqing Wu, Qingsong Wen, Liang Sun 0001

Weather forecasting is essential but remains computationally intensive and physically incomplete in traditional numerical weather prediction (NWP) methods. Deep learning (DL) models offer efficiency and accuracy but often ignore physical laws, limiting interpretability and generalization. We propose PhyDL-NWP, a physics-guided deep learning framework that integrates physical equations with latent force parameterization into data-driven models. It predicts weather variables from arbitrary spatiotemporal coordinates, computes physical terms via automatic differentiation, and uses a physics-informed loss to align predictions with governing dynamics. PhyDL-NWP enables resolution-free downscaling by modeling weather as a continuous function and fine-tunes pre-trained models with minimal overhead, achieving up to 170× faster inference with only 55K parameters. Experiments show that PhyDL-NWP improves both forecasting performance and physical consistency.

Huafeng Liu 0001, Yiran Fu, Jingyue Shi, Liping Jing, Jian Yu 0001

Existing work in physical-informed machine learning (PIML) has shown that data-driven learning of solution operators can provide a fast approximate alternative to classical numerical ordinary/partial differential equations (ODEs/PDEs) solvers. Of these, Neural Operators (NOs) have emerged as particularly promising. However, a key challenge in the field of NOs lies in developing methods that can effectively handle out-of-distribution (OOD) forecasting problems. Such problems involve the ability to adaptively learn from observations of the same dynamical system governed by ODEs/PDEs, where the underlying parameters are unknown and vary across instances. These tasks further require precise predictions even when faced with initial conditions and PDEs/ODEs parameters outside the training distribution. In this study, we consider the problem of training models in a risk-reverse manner. We introduce a risk-aware framework aimed at enhancing the OOD robustness of NOs by stochastically optimizing the conditional value-at-risk (CVAR) of a loss distribution. Through experiments on different distinct OOD tasks, our approach demonstrates a significant performance improvement over existing advanced NOs.

Juren Li, Yang Yang 0009, Hanchen Su, Jiayu Liu, Youmin Chen, Jianfeng Zhang, Lujia Pan

Current deep learning approaches for lithium-ion battery analysis are often specialized and limited to specific battery types or individual tasks. While recent advances in large language models (LLMs) highlight the potential of pretraining paradigms, existing time-series pretraining models inadequately address the physicochemical complexity and temporal irregularity inherent to battery operational data. We propose LiPM, a pretrained foundation model that unifies multi-dataset learning through physics-aware objectives and irregularity-tolerant temporal modeling. LiPM introduces three key innovations: (1) A Mix-Masked Autoencoder (MMAE) enforcing electrochemical consistency via joint reconstruction of temporally masked patches and cross-channel masked variables(2) A Coulombic Integration Regression (CIR) task explicitly encoding charge conservation laws, and (3) A dual-scale temporal encoder combining irregular intra-patch processing (preserving raw timestamps) with regular inter-patch attention (capturing macroscopic dynamics). Trained on eight heterogeneous battery datasets without cycle-label annotations, LiPM demonstrates universal applicability across partial charge-discharge segments and irregular sampling protocols. Extensive experiments show remarkable improvements over 9 state-of-the-art baselines in critical downstream tasks.

Jian Li 0064, Han Wan, Ning Lin, Yu-Liang Zhan, Ruizhi Chengze, Haining Wang, Yi Zhang 0164, Hongsheng Liu 0002, Zidong Wang 0010, Fan Yu 0004 等

Understanding and reasoning about dynamics governed by physical laws through visual observation, akin to human capabilities in the real world, poses significant challenges. Currently, object-centric dynamic simulation methods, which emulate human behavior, have achieved notable progress but overlook two critical aspects: 1) the integration of physical knowledge into models. Humans gain physical insights by observing the world and apply this knowledge to accurately reason about various dynamic scenarios; 2) the validation of model adaptability across diverse scenarios. Real-world dynamics, especially those involving fluids and objects, demand models that not only capture object interactions but also simulate fluid flow characteristics. To address these gaps, we introduce SlotPi, a slot-based physics-informed object-centric reasoning model. SlotPi integrates a physical module based on Hamiltonian principles with a spatio-temporal prediction module for dynamic forecasting. Our experiments highlight the model's strengths in tasks such as prediction and Visual Question Answering (VQA) on benchmark and fluid datasets. Furthermore, we have created a real-world dataset encompassing object interactions, fluid dynamics, and fluid-object interactions, on which we validated our model's capabilities. The model's robust performance across all datasets underscores its strong adaptability, laying a foundation for developing more advanced world models.

Qinchen Yang 0001, Zhiqing Hong, Dongjiang Cao, Haotian Wang 0008, Zejun Xie, Tian He 0001, Yunhuai Liu, Yu Yang 0010, Desheng Zhang 0002

Textual description of a physical location, commonly known as an address, plays an important role in location-based services(LBS) such as on-demand delivery and navigation. However, the prevalence of abnormal addresses, those containing inaccuracies that fail to pinpoint a location, have led to significant costs. Address rewriting has emerged as a solution to rectify these abnormal addresses. Despite the critical need, existing address rewriting methods are limited, typically tailored to correct specific error types, or frequently require retraining to process new address data effectively. In this study, we introduce AddrLLM, an innovative framework for address rewriting that is built upon a retrieval augmented large language model. AddrLLM overcomes aforementioned limitations through a meticulously designed Supervised Fine-Tuning module, an Address-centric Retrieval Augmented Generation module and a Bias-free Objective Alignment module. To the best of our knowledge, this study pioneers the application of LLM-based address rewriting approach to solve the issue of abnormal addresses. Through comprehensive offline testing with real-world data on a national scale and subsequent online deployment, AddrLLM has demonstrated superior performance in integration with existing logistics system. It has significantly decreased the rate of parcel re-routing by approximately 43%, underscoring its exceptional efficacy in real-world applications.

Yuan Mi, Pu Ren, Hongteng Xu, Hongsheng Liu 0002, Zidong Wang 0010, Yike Guo, Ji-Rong Wen, Hao Sun 0002, Yang Liu 0005

Data-centric methods have shown great potential in understanding and predicting spatiotemporal dynamics, enabling better design and control of the object system. However, deep learning models often lack interpretability, fail to obey intrinsic physics, and struggle to cope with the various domains. While geometry-based methods, e.g., graph neural networks (GNNs), have been proposed to further tackle these challenges, they still need to find the implicit physical laws from large datasets and rely excessively on rich labeled data. In this paper, we herein introduce the conservation-informed GNN (CiGNN), an end-to-end explainable learning framework, to learn spatiotemporal dynamics based on limited training data. The network is designed to conform to the general conservation law via symmetry, where conservative and non-conservative information passes over a multiscale space enhanced by a latent temporal marching strategy. The efficacy of our model has been verified in various spatiotemporal systems based on synthetic and real-world datasets, showing superiority over baseline models. Results demonstrate that CiGNN exhibits remarkable accuracy and generalizability, and is readily applicable to learning for prediction of various spatiotemporal dynamics in a spatial domain with complex geometry.

Xiao Luo 0001, Junyu Luo 0002, Huiyu Jiang, Hang Zhou 0008, Zhiping Xiao 0001, Wei Ju 0001, Carl Ji Yang, Ming Zhang 0004, Yizhou Sun

This paper investigates the problem of learning mesh-based physical simulations, which is a crucial task with applications in fluid mechanics and aerodynamics. Recent works typically utilize graph neural networks (GNNs) to produce next-time states on irregular meshes by modeling interacting dynamics, and then adopt iterative rollouts for the whole trajectories. However, these methods cannot achieve satisfactory performance in long-term predictions due to the failure of capturing long-term dependency and potential error accumulations. To tackle this, we introduce a new future-to-present learning perspective, and further develop a simple yet effective approach named Foresight And Interpolation (FAIR) for long-term mesh-based simulations. The main idea of our FAIR is to first learn a graph ODE model for coarse long-term predictions and then refine short-term predictions via interpolation. Specifically, FAIR employs a continuous graph ODE model that incorporates past states into the evolution of interacting node representations, which is capable of learning coarse long-term trajectories under a multi-task learning framework. Then, we leverage a channel aggregation strategy to summarize the trajectories for refined short-term predictions, which can be illustrated using an interpolation process. Through pyramid-like alternative propagation between the foresight step and refinement step, our proposed framework FAIR can generate accurate long-term trajectories, achieving a significant error reduction compared with the best baseline on four benchmark datasets. Extensive ablation studies and visualization further validate the superiority of our proposed FAIR.

Shuo Liu 0017, Zihan Zhou, Yuanhao Liu, Jing Zhang 0148, Hong Qian

Cognitive diagnosis aims to infer students' mastery levels based on their historical response logs. However, existing cognitive diagnosis models (CDMs), which rely on ID embeddings, often have to train specific models on specific domains. This limitation may hinder their directly practical application in various target domains, such as different subjects (e.g., Math, English and Physics) or different education platforms (e.g., ASSISTments, Junyi Academy and Khan Academy). To address this issue, this paper proposes the language representation favored zero-shot cross-domain cognitive diagnosis (LRCD). Specifically, LRCD first analyzes the behavior patterns of students, exercises and concepts in different domains, and then describes the profiles of students, exercises and concepts using textual descriptions. Via recent advanced text-embedding modules, these profiles can be transformed to vectors in the unified language space. Moreover, to address the discrepancy between the language space and the cognitive diagnosis space, we propose language-cognitive mappers in LRCD to learn the mapping from the former to the latter. Then, these profiles can be easily and efficiently integrated and trained with existing CDMs. Extensive experiments show that training LRCD on real-world datasets can achieve commendable zero-shot performance across different target domains, and in some cases, it can even achieve competitive performance with some classic CDMs trained on the full response data on target domains. Notably, we surprisingly find that LRCD can also provide interesting insights into the differences between various subjects (such as humanities and sciences) and sources (such as primary and secondary education).

Zhihao Li 0004, Haoze Song, Di Xiao, Zhilu Lai, Wei Wang 0011

Partial Differential Equations (PDEs) underpin many scientific phenomena, yet traditional computational approaches often struggle with complex, nonlinear systems and irregular geometries. This paper introduces the AMG method, a Multi-Graph neural operator approach designed for efficiently solving PDEs on Arbitrary geometries. AMG leverages advanced graph-based techniques and dynamic attention mechanisms within a novel GraphFormer architecture, enabling precise management of diverse spatial domains and complex data interdependencies. By constructing multi-scale graphs to handle variable feature frequencies and a physics graph to encapsulate inherent physical properties, AMG significantly outperforms previous methods, which are typically limited to uniform grids. We present a comprehensive evaluation of AMG across six benchmarks, demonstrating its consistent superiority over existing state-of-the-art models. Our findings highlight the transformative potential of tailored graph neural operators in surmounting the challenges faced by conventional PDE solvers. Our code and datasets are available on https://github.com/lizhihao2022/AMG.