English(EN) Physics-Informed Neural Learning for State Reconstruction and Parameter Identification in Coupled Greenhouse Climate Dynamics

PINNs通过整合物理学与神经网络改进气候建模

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-04 12:26

研究人员开发了一种耦合的物理信息神经网络（PINN）方法，用于温室气候动力学的状态重构和参数识别。该方法将物理定律融入神经网络训练中，即使在数据稀疏或嘈杂的情况下也能更准确地估算温度和湿度。与纯粹的数据驱动方法相比，PINN框架在推断湿度动力学方面表现出优越的性能，并成功识别了控制系统的关键物理参数。 AI

影响这项研究强调了物理信息学习在数据稀缺环境系统中改进气候建模的潜力。

排序理由该集群包含一篇详细介绍机器学习新方法的学术论文。

在 arXiv cs.LG 阅读 →

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报道来源 [2]

arXiv cs.LG TIER_1 English(EN) · Sani Biswas, Khursheed J. Ansari, Md. Nasim Akhtar · 2026-05-05 04:00

Physics-Informed Neural Learning for State Reconstruction and Parameter Identification in Coupled Greenhouse Climate Dynamics

arXiv:2605.02524v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have recently emerged as a promising framework for integrating data-driven learning with physical knowledge. In this work, we propose a coupled PINN approach for the joint reconstruction of i…
arXiv cs.LG TIER_1 English(EN) · Md. Nasim Akhtar · 2026-05-04 12:26

Physics-Informed Neural Learning for State Reconstruction and Parameter Identification in Coupled Greenhouse Climate Dynamics

Physics-informed neural networks (PINNs) have recently emerged as a promising framework for integrating data-driven learning with physical knowledge. In this work, we propose a coupled PINN approach for the joint reconstruction of indoor temperature and humidity dynamics in green…

报道来源 [2]

Physics-Informed Neural Learning for State Reconstruction and Parameter Identification in Coupled Greenhouse Climate Dynamics

Physics-Informed Neural Learning for State Reconstruction and Parameter Identification in Coupled Greenhouse Climate Dynamics

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