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arXiv Machine Learning By Tanguy Az\'ema, Marc Bocquet, Fatiha Nmira, Xiaoyi Lu, Jean-Louis Consalvi, Sibo Cheng

Beyond Affine Reconstruction: Multi-Resolution Transformer DeepONets for the Radiative Transfer Equation

Read the original on arXiv Machine Learning →

The paper introduces a transformer-based DeepONet that uses a Perceiver encoder and cross‑attention decoder to model the radiative transfer equation (RTE) across multiple spatial resolutions. Unlike traditional DeepONets, this architecture avoids affine reconstruction, allowing a single model to handle inputs at varying resolutions without resampling. Experiments on a two‑dimensional pool‑fire dataset show that the new method outperforms autoencoder‑based DeepONets in predicting radiative intensity and incident radiation, with fewer parameters and better preservation of small‑scale structures.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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