arXiv Machine Learning June 17, 2026 By Egor Bugaev, Fedor Buzaev, Dmitry Efremenko, Denis Derkach, Fedor Ratnikov

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

Read the original on arXiv Machine Learning →

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.

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

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arXiv Machine Learning

Aug 4

NORi: An ML-Augmented Ocean Boundary Layer Parameterization

arXiv:2512. 04452v3 Announce Type: replace-cross Abstract: NORi is a machine learning (ML) parameterization of ocean boundary layer turbulence that is physics-based and augmented with neural networks.

By Xin Kai Lee, Ali Ramadhan, Andre Souza, Gregory LeClaire Wagner, Simone Silvestri, John Marshall, Raffaele Ferrari

benchmarks

arXiv Machine Learning

Aug 12

Deep Learning-Based Statistical Downscaling of Sea Surface Temperature Using a Residual Corrective Neural Network

arXiv:2608. 10022v1 Announce Type: cross Abstract: The large-scale oceanic and atmospheric forecasts provided by global climate models typically lack sufficient resolution to accurately capture the response of the coastal ocean to atmospheric forcing and coastal circulation that drive fine-scale SST variability.

By Onkar Jadhav, Tim French, Ivica Janekovic, Nicole L. Jones, Matthew Rayson

arXiv AI

Jul 14

Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector

arXiv:2607. 10208v1 Announce Type: cross Abstract: Accurate meteorological forecasting is essential for agricultural planning, irrigation management, and environmental decision support.

By Piotr Sikora, Sotirios Kontogiannis

arXiv Machine Learning

Aug 11

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

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By Sani Biswas, Khursheed J. Ansari, Md. Nasim Akhtar

arXiv Machine Learning

Jul 17

Subgrid-Scale Parameterization in Burgers' Equation Using Structure-Preserving Neural Networks and Entropy Variables

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By Aijaz Nazir, Ilya Timofeyev

arXiv Machine Learning

5d ago

Virtual Temperature Sensors in Power Transformers Using Neural Ordinary Differential Equations

arXiv:2608. 13260v1 Announce Type: new Abstract: Accurate modeling and forecasting of power transformer thermal behavior are critical for reliability, asset lifetime, and optimized power system operation.

By Berk Hadzhamolla, Alexander Johannes Stasik, Signe Riemer-S{\o}rensen

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