arXiv Machine Learning

Machine-Learning Emulation of Satellite Greenhouse Gas Retrievals: Stability over Time

arXiv:2606. 09313v1 Announce Type: new Abstract: Retrieval algorithms are used to estimate atmospheric concentrations of greenhouse gases (GHGs), such as carbon dioxide (CO2) and methane (CH4), by solving inverse problems from high-spectral-resolution satellite radiance measurements.

arXiv Machine Learning
1d ago

Efficient Neural-Network-Based High-Resolution Radiative Transfer for CO___ Retrieval, and Application to Interferometric Sensing

arXiv:2608. 14645v1 Announce Type: new Abstract: Studying climate change requires reducing uncertainties in CO2 and CH4 emission estimates to better distinguish anthropogenic from natural sources, which motivates spaceborne measurements with improved revisit frequency and spatial coverage.

By Jordan Lontsi Tedongmo (CB), Yann Ferrec (CB, IFUMI), Laurence Croiz\'e (CB, IFUMI), Pablo Mus\'e (CB, IFUMI), Gabriele Facciolo (CB), Andr\'es Almansa (MAP5 - UMR 8145, IFUMI)
arXiv Machine Learning
Aug 11

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

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.

By Sani Biswas, Khursheed J. Ansari, Md. Nasim Akhtar
arXiv Machine Learning
6d ago

Temperature-Driven Sequential Modeling for the Prediction of Annual Power Conversion Efficiency Profiles of Organic Photovoltaic Materials: Douala Case Study

arXiv:2608. 11261v1 Announce Type: cross Abstract: Organic photovoltaic (OPV) materials are promising candidates for distributed solar energy in tropical regions, yet existing virtual screening tools report static power conversion efficiency (PCE) values at standard testing conditions (STC) that fail to capture the temperature-driven performance degradation experienced under real deployment conditions.

By Steve Cabrel Teguia Kouam, Rockefeller Rockefeller, Raoult Dabou Teukam, Jean-Pierre Tchapet Njafa, Patrick Sorrel Mvoto Kongo, Jean-Pierre Nguenang, Serge Guy Nana Engo