SRNN: Spatiotemporal Relational Neural Network for Intuitive Physics Understanding
Fuente:
arXiv
Guardado en:
| Autor principal: | Yang, Fei |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
PIAD-SRNN: Physics-Informed Adaptive Decomposition in State-Space RNN
por: Mohammadshirazi, Ahmad, et al.
Publicado: (2024)
por: Mohammadshirazi, Ahmad, et al.
Publicado: (2024)
On the Impact of Class Imbalance on the Learning Dynamics of Deep Neural Networks:An Intuitive Insight
por: Mustapha, Ismail B., et al.
Publicado: (2026)
por: Mustapha, Ismail B., et al.
Publicado: (2026)
On the Spatiotemporal Dynamics of Generalization in Neural Networks
por: Wei, Zichao
Publicado: (2026)
por: Wei, Zichao
Publicado: (2026)
Over-squashing in Spatiotemporal Graph Neural Networks
por: Marisca, Ivan, et al.
Publicado: (2025)
por: Marisca, Ivan, et al.
Publicado: (2025)
G-PARC: Graph-Physics Aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics on Unstructured Meshes
por: Beerman, Jack T., et al.
Publicado: (2026)
por: Beerman, Jack T., et al.
Publicado: (2026)
Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation
por: Wang, Yu, et al.
Publicado: (2023)
por: Wang, Yu, et al.
Publicado: (2023)
UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction
por: Yu, Dahai, et al.
Publicado: (2025)
por: Yu, Dahai, et al.
Publicado: (2025)
Neural Point Process for Learning Spatiotemporal Event Dynamics
por: Zhou, Zihao, et al.
Publicado: (2021)
por: Zhou, Zihao, et al.
Publicado: (2021)
Understanding Pooling in Graph Neural Networks
por: Grattarola, Daniele, et al.
Publicado: (2021)
por: Grattarola, Daniele, et al.
Publicado: (2021)
PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies
por: Der, Audrey, et al.
Publicado: (2024)
por: Der, Audrey, et al.
Publicado: (2024)
Graph In-Context Operator Networks for Generalizable Spatiotemporal Prediction
por: Wu, Chenghan, et al.
Publicado: (2026)
por: Wu, Chenghan, et al.
Publicado: (2026)
Unifying Adversarial Perturbation for Graph Neural Networks
por: Yang, Jinluan, et al.
Publicado: (2025)
por: Yang, Jinluan, et al.
Publicado: (2025)
Implicit Neural Differential Model for Spatiotemporal Dynamics
por: Akhare, Deepak, et al.
Publicado: (2025)
por: Akhare, Deepak, et al.
Publicado: (2025)
Neural Spatiotemporal Point Processes: Trends and Challenges
por: Mukherjee, Sumantrak, et al.
Publicado: (2025)
por: Mukherjee, Sumantrak, et al.
Publicado: (2025)
Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics
por: Minartz, Koen, et al.
Publicado: (2023)
por: Minartz, Koen, et al.
Publicado: (2023)
The Little Book of Generative AI Foundations: An Intuitive Mathematical Primer
por: Chen, Tianhua
Publicado: (2026)
por: Chen, Tianhua
Publicado: (2026)
Learning to Play Video Games with Intuitive Physics Priors
por: Jaiswal, Abhishek, et al.
Publicado: (2024)
por: Jaiswal, Abhishek, et al.
Publicado: (2024)
Systematic Relational Reasoning With Epistemic Graph Neural Networks
por: Khalid, Irtaza, et al.
Publicado: (2024)
por: Khalid, Irtaza, et al.
Publicado: (2024)
Understanding Task Representations in Neural Networks via Bayesian Ablation
por: Nam, Andrew, et al.
Publicado: (2025)
por: Nam, Andrew, et al.
Publicado: (2025)
Understanding the Generalization of Stochastic Gradient Adam in Learning Neural Networks
por: Tang, Xuan, et al.
Publicado: (2025)
por: Tang, Xuan, et al.
Publicado: (2025)
Relational Composition in Neural Networks: A Survey and Call to Action
por: Wattenberg, Martin, et al.
Publicado: (2024)
por: Wattenberg, Martin, et al.
Publicado: (2024)
A Relational Inductive Bias for Dimensional Abstraction in Neural Networks
por: Campbell, Declan, et al.
Publicado: (2024)
por: Campbell, Declan, et al.
Publicado: (2024)
Younger: The First Dataset for Artificial Intelligence-Generated Neural Network Architecture
por: Yang, Zhengxin, et al.
Publicado: (2024)
por: Yang, Zhengxin, et al.
Publicado: (2024)
A Novel Spatiotemporal Coupling Graph Convolutional Network
por: Bi, Fanghui
Publicado: (2024)
por: Bi, Fanghui
Publicado: (2024)
Complex Physics-Informed Neural Network
por: Si, Chenhao, et al.
Publicado: (2025)
por: Si, Chenhao, et al.
Publicado: (2025)
Physics-Informed Neural Networks and Extensions
por: Raissi, Maziar, et al.
Publicado: (2024)
por: Raissi, Maziar, et al.
Publicado: (2024)
Spatiotemporal Graph Neural Networks in short term load forecasting: Does adding Graph Structure in Consumption Data Improve Predictions?
por: Nguyen, Quoc Viet, et al.
Publicado: (2025)
por: Nguyen, Quoc Viet, et al.
Publicado: (2025)
Compressing Neural Networks Using Tensor Networks with Exponentially Fewer Variational Parameters
por: Qing, Yong, et al.
Publicado: (2023)
por: Qing, Yong, et al.
Publicado: (2023)
Multi-Relational Graph Neural Network for Out-of-Domain Link Prediction
por: Sattar, Asma, et al.
Publicado: (2024)
por: Sattar, Asma, et al.
Publicado: (2024)
Hyperbolic Hypergraph Neural Networks for Multi-Relational Knowledge Hypergraph Representation
por: Li, Mengfan, et al.
Publicado: (2024)
por: Li, Mengfan, et al.
Publicado: (2024)
Interpretable Air Pollution Forecasting by Physics-Guided Spatiotemporal Decoupling
por: Zhang, Zhiguo, et al.
Publicado: (2025)
por: Zhang, Zhiguo, et al.
Publicado: (2025)
Learning Higher-Order Structure from Incomplete Spatiotemporal Data: Multi-Scale Hypergraph Laplacians with Neural Refinement
por: Wu, Keshu, et al.
Publicado: (2026)
por: Wu, Keshu, et al.
Publicado: (2026)
Densely Multiplied Physics Informed Neural Networks
por: Jiang, Feilong, et al.
Publicado: (2024)
por: Jiang, Feilong, et al.
Publicado: (2024)
Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Physical Dynamics Learning
por: Zheng, Zinan, et al.
Publicado: (2024)
por: Zheng, Zinan, et al.
Publicado: (2024)
SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases
por: Liu, Yang, et al.
Publicado: (2023)
por: Liu, Yang, et al.
Publicado: (2023)
Understanding Sparse Neural Networks from their Topology via Multipartite Graph Representations
por: Cunegatti, Elia, et al.
Publicado: (2023)
por: Cunegatti, Elia, et al.
Publicado: (2023)
Understanding the Countably Infinite: Neural Network Models of the Successor Function and its Acquisition
por: Gupta, Vima, et al.
Publicado: (2023)
por: Gupta, Vima, et al.
Publicado: (2023)
Rethinking Input Domains in Physics-Informed Neural Networks via Geometric Compactification Mappings
por: Huang, Zhenzhen, et al.
Publicado: (2026)
por: Huang, Zhenzhen, et al.
Publicado: (2026)
A Survey of Test-Time Compute: From Intuitive Inference to Deliberate Reasoning
por: Ji, Yixin, et al.
Publicado: (2025)
por: Ji, Yixin, et al.
Publicado: (2025)
Relating-Up: Advancing Graph Neural Networks through Inter-Graph Relationships
por: Zou, Qi, et al.
Publicado: (2024)
por: Zou, Qi, et al.
Publicado: (2024)
Ejemplares similares
-
PIAD-SRNN: Physics-Informed Adaptive Decomposition in State-Space RNN
por: Mohammadshirazi, Ahmad, et al.
Publicado: (2024) -
On the Impact of Class Imbalance on the Learning Dynamics of Deep Neural Networks:An Intuitive Insight
por: Mustapha, Ismail B., et al.
Publicado: (2026) -
On the Spatiotemporal Dynamics of Generalization in Neural Networks
por: Wei, Zichao
Publicado: (2026) -
Over-squashing in Spatiotemporal Graph Neural Networks
por: Marisca, Ivan, et al.
Publicado: (2025) -
G-PARC: Graph-Physics Aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics on Unstructured Meshes
por: Beerman, Jack T., et al.
Publicado: (2026)