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Mamba Neural Operator: Who Wins? Transformers vs. State-Space Models for Pdes

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.jcp.2018.10.045openalex-extracted

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

inferredcites →https://doi.org/10.1038/s41467-018-07210-0openalex-extracted

Deep learning for universal linear embeddings of nonlinear dynamics

inferredcites →https://doi.org/10.1063/1.3498547openalex-extracted

Comparison Between Different Numerical Methods for Discretization of PDEs-A Short Review

inferredcites →https://doi.org/10.1162/neco.2006.18.7.1527openalex-extracted

A Fast Learning Algorithm for Deep Belief Nets

inferredcites →https://doi.org/10.1609/aaai.v33i01.33016706openalex-extracted

Neural Speech Synthesis with Transformer Network

inferredcites →https://doi.org/10.48550/arxiv.2310.20092openalex-extracted

The Missing U for Efficient Diffusion Models

inferredcites →https://doi.org/10.7551/mitpress/7496.003.0016openalex-extracted

Scaling Learning Algorithms toward AI

inferredcites →https://doi.org/10.1073/pnas.2101784118openalex-snapshot

Machine learning–accelerated computational fluid dynamics

inferredcites →https://doi.org/10.1111/cgf.13620openalex-snapshot

Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow

inferredcites →https://doi.org/10.5802/smai-jcm.74openalex-snapshot

Model Reduction And Neural Networks For Parametric PDEs

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1016/j.cma.2025.118675openalex-snapshot

Data generation with optimal experimental design for operator learning

inferred← citeshttps://doi.org/10.1016/j.jcp.2025.114272openalex-snapshot

DeepOMamba: State-space model for spatio-temporal PDE neural operator learning

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.