FTD-GNO: Memory-Efficient Graph Neural Operators through Functional Tensor Decomposition of the Kernel

cs.LG, cs.AI

Submitted: 2026-10-03

Updated: 2026-10-03

Comments: 19 pages and 2 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Sources

• Neuro-Symbolic Multitasking: A Unified Framework for Discovering Generalizable Solutions to PDE Families
• Analysis of a fully discretized FDM-FEM scheme for solving thermo-elastic-damage coupled nonlinear PDE systems
• A Library for Learning Neural Operators
• Neural Operator: Graph Kernel Network for Partial Differential Equations
• Fourier Neural Operator for Parametric Partial Differential Equations
• Factorized Fourier Neural Operators
• Multi-Grid Tensorized Fourier Neural Operator for High-Resolution PDEs
• TensorGRaD: Tensor Gradient Robust Decomposition for Memory-Efficient Neural Operator Training
• Spectral Networks and Locally Connected Networks on Graphs

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks