Copertina

Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Minartz, Koen, Poels, Yoeri, Koop, Simon, Menkovski, Vlado
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Accelerating Simulation of Two-Phase Flows with Neural PDE Surrogates
di: Poels, Yoeri, et al.
Pubblicazione: (2024)

STFlow: Data-Coupled Flow Matching for Geometric Trajectory Simulation
di: Brinke, Kiet Bennema ten, et al.
Pubblicazione: (2025)

Understanding complex crowd dynamics with generative neural simulators
di: Minartz, Koen, et al.
Pubblicazione: (2024)

Efficient Probabilistic Modeling of Crystallization at Mesoscopic Scale
di: Timmer, Pol, et al.
Pubblicazione: (2024)

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

Deep Neural Cellular Potts Models
di: Minartz, Koen, et al.
Pubblicazione: (2025)

VADA: a Data-Driven Simulator for Nanopore Sequencing
di: Niederle, Jonas, et al.
Pubblicazione: (2024)

Equivariant Flow Matching for Symmetry-Breaking Bifurcation Problems
di: Hendriks, Fleur, et al.
Pubblicazione: (2025)

Robust Confinement State Classification with Uncertainty Quantification through Ensembled Data-Driven Methods
di: Poels, Yoeri, et al.
Pubblicazione: (2025)

LOREN: Low Rank-Based Code-Rate Adaptation in Neural Receivers
di: Van Bolderik, Bram, et al.
Pubblicazione: (2026)

Towards Fully Automated Molecular Simulations: Multi-Agent Framework for Simulation Setup and Force Field Extraction
di: Petković, Marko, et al.
Pubblicazione: (2025)

Plasma State Monitoring and Disruption Characterization using Multimodal VAEs
di: Poels, Yoeri, et al.
Pubblicazione: (2025)

Topological degree as a discrete diagnostic for disentanglement, with applications to the $Δ$VAE
di: Ravelonanosy, Mahefa Ratsisetraina, et al.
Pubblicazione: (2024)

On the Spatiotemporal Dynamics of Generalization in Neural Networks
di: Wei, Zichao
Pubblicazione: (2026)

Implicit Neural Differential Model for Spatiotemporal Dynamics
di: Akhare, Deepak, et al.
Pubblicazione: (2025)

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

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

Neural Point Process for Learning Spatiotemporal Event Dynamics
di: Zhou, Zihao, et al.
Pubblicazione: (2021)

"I May Not Have Articulated Myself Clearly": Diagnosing Dynamic Instability in LLM Reasoning at Inference Time
di: Chen, Jinkun, et al.
Pubblicazione: (2026)

Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Physical Dynamics Learning
di: Zheng, Zinan, et al.
Pubblicazione: (2024)

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

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

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

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

Generalized Reduction to the Isotropy for Flexible Equivariant Neural Fields
di: García-Castellanos, Alejandro, et al.
Pubblicazione: (2026)

Diagnosing Spectral Ceilings in Equivariant Neural Force Fields
di: Kim, Hyunmog
Pubblicazione: (2026)

Symmetry in the Wild: The Role of Equivariance in Neural Fluid Surrogates
di: Rygiel, Patryk, et al.
Pubblicazione: (2026)

Learning Plasma Dynamics and Robust Rampdown Trajectories with Predict-First Experiments at TCV
di: Wang, Allen M., et al.
Pubblicazione: (2025)

Over-squashing in Spatiotemporal Graph Neural Networks
di: Marisca, Ivan, et al.
Pubblicazione: (2025)

Neural Spatiotemporal Point Processes: Trends and Challenges
di: Mukherjee, Sumantrak, et al.
Pubblicazione: (2025)

Node Classification in Random Trees
di: Nuijten, Wouter W. L., et al.
Pubblicazione: (2023)

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

G-PARC: Graph-Physics Aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics on Unstructured Meshes
di: Beerman, Jack T., et al.
Pubblicazione: (2026)

Lie Neurons: Adjoint-Equivariant Neural Networks for Semisimple Lie Algebras
di: Lin, Tzu-Yuan, et al.
Pubblicazione: (2023)

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

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

Simulation-Free Differential Dynamics through Neural Conservation Laws
di: Hua, Mengjian, et al.
Pubblicazione: (2025)

SRNN: Spatiotemporal Relational Neural Network for Intuitive Physics Understanding
di: Yang, Fei
Pubblicazione: (2025)

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning
di: Chu, Xu, et al.
Pubblicazione: (2025)

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