MENO: Memory-Efficient Neural Operator

arXiv:2609.27739 · cs.LG · Submitted 2026-09-23 · Read on arXiv

cs.LG

Submitted: 2026-09-23

Updated: 2026-09-23

Code: https://github.com/jpzxshi/MENO

Terminology

• Accuracy — 12× in this paper · explained in 1 episode
• Transformer — 8× in this paper · explained in 2 episodes
• DeepONet — 6× in this paper · explained in 2 episodes
• Generalization — 6× in this paper · explained in 13 episodes
• Manifold Function Encoder — 5× in this paper
• Benchmark — 4× in this paper · explained in 2 episodes
• Decode — 4× in this paper · explained in 2 episodes
• Memory-Efficient Neural Operator — 4× in this paper
• Adaptability — 2× in this paper · explained in 1 episode
• Fidelity — 2× in this paper · explained in 2 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• AB-UPT: Scaling Neural CFD Surrogates for High-Fidelity Automotive Aerodynamics Simulations via Anchored-Branched Universal Physics Transformers
• AhmedML: High-Fidelity Computational Fluid Dynamics Dataset for Incompressible, Low-Speed Bluff Body Aerodynamics
• MgNO: Efficient Parameterization of Linear Operators via Multigrid
• Gaussian Error Linear Units (GELUs)
• Manifold Function Encoder: Identifying Different Functions Defined on Different Manifolds
• Fourier Neural Operator with Learned Deformations for PDEs on General Geometries
• Fourier Neural Operator for Parametric Partial Differential Equations
• Decoupled Weight Decay Regularization
• DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
• Transolver++: An Accurate Neural Solver for PDEs on Million-Scale Geometries
• Learning Mesh-Based Simulation with Graph Networks
• U-NO: U-shaped Neural Operators
• Transolver: A Fast Transformer Solver for PDEs on General Geometries
• DIMON: Learning Solution Operators of Partial Differential Equations on a Diffeomorphic Family of Domains
• Blending Neural Operators and Relaxation Methods in PDE Numerical Solvers
• Transolver-3: Scaling Up Transformer Solvers to Industrial-Scale Geometries

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