English(EN) A Machine Learning-Enhanced Hopf-Cole Formulation for Nonlinear Gas Flow in Porous Media

机器学习框架增强多孔介质气体流动模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-09 04:00

研究人员开发了一种新颖的机器学习框架，以改进多孔介质中气体流动的建模。该方法结合了Klinkenberg增强的本构关系和Hopf-Cole变换来线性化控制方程。使用共享主干神经网络架构和深度最小二乘求解器来精确预测压力和速度场，并支持用于参数估计的反向建模。 AI

影响该框架为模拟气体传输和估算复杂地质构造中的流动特性提供了一种更准确、计算效率更高的方法。

排序理由该集群包含一篇研究论文，详细介绍了多孔介质中气体流动的新方法。[lever_c_demoted from research: ic=1 ai=0.7]

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arXiv cs.LG TIER_1 English(EN) · V. S. Maduri, K. B. Nakshatrala · 2026-06-09 04:00

用于多孔介质非线性气体流动的机器学习增强Hopf-Cole公式

arXiv:2603.11250v2 Announce Type: replace-cross Abstract: Accurate modeling of gas flow through porous media is critical for many technological applications, including reservoir performance prediction, carbon capture and sequestration, and fuel cells and batteries. However, such …