arXiv:2608. 14935v1 Announce Type: cross Abstract: Turbulent fluxes between the surface and the atmosphere are typically parameterized using empirically fit relationships.
By Susan Dettling, Sue Ellen Haupt, Thomas Brummet, Patrick Hawbecker, Branko Kosovi\'c, David John Gagne
arXiv:2606. 28519v1 Announce Type: new Abstract: Training operator-learning models for large-scale problems governed by partial differential equations (PDEs) is challenging due to the curse of dimensionality, memory constraints, and limited training data.
By Christian Munoz, Alexandre Tartakovsky
arXiv:2610.09871v1 Announce Type: cross
Abstract: Implicit modeling and Relative Geologic Time are geological modeling techniques that enable more efficient, faster, less biased and more reproducible...
By Stefan Carpentier, Jan Diederik van Wees, Eva de Boever, Jan Niederau, Camille Chapeland, Suzanne Atkins, Boris Boullenger, Jens Wollenweber
The paper introduces a text-informed training data filtering approach that uses large language models to combine unstructured operational text with tabular sensor data for anomaly detection and dataset masking. Applied to a virtual flow meter for oil and gas, the multimodal method reduces mean absolute percentage error from 7.1% to 5.4% compared to sensor-only filtering. Ablation studies confirm that operator comments provide additional filtering signals beyond sensor data alone.
By Kristoffer Hjelmen Nesland (University of Stavanger, Stavanger, Norway, Solution Seeker AS, Oslo, Norway), Bjarne Grimstad (Solution Seeker AS, Oslo, Norway), Antorweep Chakravorty (University of Stavanger, Stavanger, Norway), Dan Sui (University of Stavanger, Stavanger, Norway)
The paper introduces a lightweight deep learning framework that forecasts Brazilian soybean yields using only routine weather data and two simple static inputs (crop year and agro-environmental label). Across 20 seasons, transformer-based models achieved the highest accuracy, outperforming traditional ridge regression and a moving‑average baseline by nearly 48%. Ablation studies show that the static inputs and spatial expansion improve performance without adding complexity, and SHAP analysis highlights the importance of crop year and weather variables in driving yield variations.
By Fernando Dupin da Cunha Mello (Stricto Sensu Department, SENAI CIMATEC University, Salvador, Bahia, Brazil), Prashant Kumar (Global Centre for Clean Air Research), Erick G. Sperandio Nascimento (Stricto Sensu Department, SENAI CIMATEC University, Salvador, Bahia, Brazil)
Drift Field Net (DFN) is a deep neural network that predicts ocean surface flow fields from satellite observations, trained via a two‑stage strategy combining simulated data pretraining and Lagrangian fine‑tuning with an advection‑consistent loss. DFN improves particle trajectory forecasts, reducing mean positioning error by 20 km over a 7‑day period compared to an operational physics‑based model, and further decreasing error by 10 km when the advection loss is applied. The study demonstrates that incorporating Lagrangian constraints into deep‑learning training enhances ocean surface flow prediction accuracy.
By Th\'eo Archambault, Pierre Garcia, Mattia Romero, Anastase Charantonis, Dominique B\'er\'eziat