Author

Peng-Zhan Jin

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Preprint Sep 2026

Learning Operators of Geometry with an Interface Autoencoder

Geometry-dependent PDEs define operators whose inputs and outputs may each consist of an oriented interface and a function on that interface, whereas most existing neural operators are formulated on fixed domains. In this paper, we first establish an approximation theorem for continuous operators between general state...

Ai-Qing Zhu, Peng-Zhan Jin · 0 citations

#machine learning Preprint Sep 2026

MENO: Memory-Efficient Neural Operator

We propose the Memory-Efficient Neural Operator (MENO) as a high-performance PDE neural solver based on the Manifold Function Encoder (MFE). MENO features three primary advantages: (1) MENO has a significantly smaller memory footprint and much faster training speed than other popular architectures, with the memory foot...

Sheng-Yang Xu, Wei-Jun Zhang, Jun Hu et al. · 0 citations