English(EN) Beyond Affine Reconstruction: Multi-Resolution Transformer DeepONets for the Radiative Transfer Equation

Transformer DeepONets 推进辐射传输方程求解

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员开发了一种新颖的基于 Transformer 的 DeepONet 架构，旨在提高求解辐射传输方程 (RTE) 的准确性和灵活性，RTE 是火灾模拟的关键组成部分。该新模型使用 Perceiver 编码器处理不同分辨率下的温度和吸收系数场，并将它们映射到固定大小的潜在表示。然后，交叉注意力解码器促进辐射强度的非仿射重建，使模型能够处理不同分辨率的输入而无需重采样。实验表明，这种方法优于传统的基于自动编码器的 DeepONets，能够保留小尺度结构并实现更低的训练误差。 AI

影响这种新架构通过提高辐射传输方程求解的准确性和效率，有望显著加速火灾模拟。

排序理由该集群包含一篇研究论文，详细介绍了求解科学方程的新模型架构。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Tanguy Az\'ema, Marc Bocquet, Fatiha Nmira, Xiaoyi Lu, Jean-Louis Consalvi, Sibo Cheng · 2026-10-28 04:00

超越仿射重建：用于辐射传输方程的多分辨率Transformer DeepONets

arXiv:2610.33668v1 Announce Type: new Abstract: Radiative heat transfer plays a dominant role in fire simulations, where solving the radiative transfer equation (RTE) is a computational bottleneck. Neural operators can accelerate these computations by learning solution operators …

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超越仿射重建：用于辐射传输方程的多分辨率Transformer DeepONets

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