Copertina

Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Physical Dynamics Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zheng, Zinan, Liu, Yang, Li, Jia, Yao, Jianhua, Rong, Yu
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases
di: Liu, Yang, et al.
Pubblicazione: (2023)

Equivariant Spatio-Temporal Attentive Graph Networks to Simulate Physical Dynamics
di: Wu, Liming, et al.
Pubblicazione: (2024)

Graph Neural Networks for Learning Equivariant Representations of Neural Networks
di: Kofinas, Miltiadis, et al.
Pubblicazione: (2024)

Fast and Distributed Equivariant Graph Neural Networks by Virtual Node Learning
di: Zhang, Yuelin, et al.
Pubblicazione: (2025)

Magnitude-Modulated Equivariant Adapter for Parameter-Efficient Fine-Tuning of Equivariant Graph Neural Networks
di: Jin, Dian, et al.
Pubblicazione: (2025)

Multivector Neurons: Better and Faster O(n)-Equivariant Clifford Graph Neural Networks
di: Liu, Cong, et al.
Pubblicazione: (2024)

SIG: Efficient Self-Interpretable Graph Neural Network for Continuous-time Dynamic Graphs
di: Fang, Lanting, et al.
Pubblicazione: (2024)

Metric Learning for Clifford Group Equivariant Neural Networks
di: Ali, Riccardo, et al.
Pubblicazione: (2024)

From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
di: Wang, Zheng, et al.
Pubblicazione: (2023)

How the Optimizer Shapes Learned Solutions in Equivariant Neural Networks
di: Stupariu, Teodor-Mihai, et al.
Pubblicazione: (2026)

Coupling Graph Neural Networks with Fractional Order Continuous Dynamics: A Robustness Study
di: Kang, Qiyu, et al.
Pubblicazione: (2024)

Separation Power of Equivariant Neural Networks
di: Pacini, Marco, et al.
Pubblicazione: (2024)

Unifying O(3) Equivariant Neural Networks Design with Tensor-Network Formalism
di: Li, Zimu, et al.
Pubblicazione: (2022)

SiGNN: A Spike-induced Graph Neural Network for Dynamic Graph Representation Learning
di: Chen, Dong, et al.
Pubblicazione: (2024)

Continuous Product Graph Neural Networks
di: Einizade, Aref, et al.
Pubblicazione: (2024)

Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics
di: Minartz, Koen, et al.
Pubblicazione: (2023)

Graph Neural Backdoor: Fundamentals, Methodologies, Applications, and Future Directions
di: Yang, Xiao, et al.
Pubblicazione: (2024)

PTCL: Pseudo-Label Temporal Curriculum Learning for Label-Limited Dynamic Graph
di: Zhang, Shengtao, et al.
Pubblicazione: (2025)

Similarity Equivariant Graph Neural Networks for Homogenization of Metamaterials
di: Hendriks, Fleur, et al.
Pubblicazione: (2024)

An Operator-Consistent Graph Neural Network for Learning Diffusion Dynamics on Irregular Meshes
di: Li, Yuelian, et al.
Pubblicazione: (2025)

HeGMN: Heterogeneous Graph Matching Network for Learning Graph Similarity
di: Sang, Shilong, et al.
Pubblicazione: (2025)

Boosting Graph Neural Network Expressivity with Learnable Lanczos Constraints
di: Azizi, Niloofar, et al.
Pubblicazione: (2024)

Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs
di: Yu, He, et al.
Pubblicazione: (2025)

Transfer learning in Scalable Graph Neural Network for Improved Physical Simulation
di: Shen, Siqi, et al.
Pubblicazione: (2025)

Equivariant Graph Network Approximations of High-Degree Polynomials for Force Field Prediction
di: Xu, Zhao, et al.
Pubblicazione: (2024)

DUALFloodGNN: Physics-informed Graph Neural Network for Operational Flood Modeling
di: Acosta, Carlo Malapad, et al.
Pubblicazione: (2025)

Combinatorial Optimization with Automated Graph Neural Networks
di: Liu, Yang, et al.
Pubblicazione: (2024)

Commute Graph Neural Networks
di: Zhuo, Wei, et al.
Pubblicazione: (2024)

Faithful Interpretation for Graph Neural Networks
di: Hu, Lijie, et al.
Pubblicazione: (2024)

Self-Pro: A Self-Prompt and Tuning Framework for Graph Neural Networks
di: Gong, Chenghua, et al.
Pubblicazione: (2023)

Learning to Solve Combinatorial Optimization under Positive Linear Constraints via Non-Autoregressive Neural Networks
di: Wang, Runzhong, et al.
Pubblicazione: (2024)

Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited
di: He, Mingguo, et al.
Pubblicazione: (2022)

PAGE: Parametric Generative Explainer for Graph Neural Network
di: Qiu, Yang, et al.
Pubblicazione: (2024)

Exact Verification of Graph Neural Networks with Incremental Constraint Solving
di: Liu, Minghao, et al.
Pubblicazione: (2025)

All in One: Multi-Task Prompting for Graph Neural Networks (Extended Abstract)
di: Sun, Xiangguo, et al.
Pubblicazione: (2024)

Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation
di: Wang, Yu, et al.
Pubblicazione: (2023)

Conservation-informed Graph Learning for Spatiotemporal Dynamics Prediction
di: Mi, Yuan, et al.
Pubblicazione: (2024)

Feature Learning Dynamics in Infinite-Depth Neural Networks
di: Yao, Zihan, et al.
Pubblicazione: (2025)

Continuous Spiking Graph Neural Networks
di: Yin, Nan, et al.
Pubblicazione: (2024)

GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy
di: Peng, Tianhao, et al.
Pubblicazione: (2023)