arXiv Machine Learning August 11, 2026 By Sani Biswas, Khursheed J. Ansari, Md. Nasim Akhtar

A Coupled Physics-Informed Neural Network for Greenhouse Climate State Reconstruction and Parameter Identification under Sparse Sensor Measurements

Read the original on arXiv Machine Learning →

arXiv:2605. 02524v2 Announce Type: replace Abstract: Accurate reconstruction of greenhouse climate variables from sparse sensor measurements is essential for intelligent environmental monitoring, automated climate control, and precision agriculture.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 8

A Mechanism-Coupled Split Window Network for Medium- to High-Resolution Land Surface Temperature Retrieval

arXiv:2509. 04991v2 Announce Type: replace-cross Abstract: Land surface temperature (LST) is a fundamental physical variable in land-atmosphere interactions, surface energy budgets, and climate processes.

By Tian Xie, Menghui Jiang, Chao Zeng, Huifang Li, Guanhao Zhang, Chan Li, Huanfeng Shen

arXiv Machine Learning

Jul 14

Parameter estimation for land-surface models using Neural Physics

arXiv:2505. 02979v4 Announce Type: replace-cross Abstract: We propose a novel inverse-modelling approach that estimates the parameters of a simple land-surface model (LSM) by assimilating data into a differentiable, physics-based forward model formulated using convolutional operations.

By Ruiyue Huang, Claire E. Heaney, Maarten van Reeuwijk

arXiv Machine Learning

Jun 4

Reconstructing Unobservable Temperature Fields via Simulation-Aided Intelligent Sensing

arXiv:2606. 04582v1 Announce Type: cross Abstract: Real-time monitoring of the temperature distribution within components and sub-structures is a challenging topic in many systems due to restrictions on feasible sensor locations.

By Monika Stipsitz, H\`elios Sanchis-Alepuz, Jacob Reynvaan, Silvester Sabathiel

arXiv Machine Learning

Jun 5

The Equilibrium Response of Atmospheric Machine-Learning Models to Uniform Sea Surface Temperature Warming

arXiv:2510. 02415v3 Announce Type: replace-cross Abstract: Machine learning models for the global atmosphere that are capable of producing stable, multi-year simulations of Earth's climate have recently been developed.

By Bosong Zhang, Timothy M. Merlis

benchmarks

arXiv Machine Learning

Jul 15

Robustness of Deep Learning Models for PV Power Forecasting under NWP Forecast Errors: A Spatiotemporal and Physically Interpretable Analysis

arXiv:2607. 12954v1 Announce Type: cross Abstract: Engineering use of AI forecasting models requires not only high nominal accuracy but also predictable behavior under uncertain inputs.

By Dandan Chen, Yan Zhao, Xuepeng Chen

arXiv Machine Learning

Jun 17

Physics-Constrained Neural Networks for Improved Short-Term Weather Forecasting: A Case Study over the South Pacific

arXiv:2606. 17659v1 Announce Type: new Abstract: This study introduces enhancements to physics-constrained neural networks (PCNNs) that improve the accuracy and stability of hybrid short-term weather forecasting models.

By Egor Bugaev, Fedor Buzaev, Dmitry Efremenko, Denis Derkach, Fedor Ratnikov

benchmarks

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A Mechanism-Coupled Split Window Network for Medium- to High-Resolution Land Surface Temperature Retrieval

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Reconstructing Unobservable Temperature Fields via Simulation-Aided Intelligent Sensing

The Equilibrium Response of Atmospheric Machine-Learning Models to Uniform Sea Surface Temperature Warming

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