The paper introduces KAINN, a hybrid neural‑mechanistic model that augments the Agriculture‑informed Neural Network with domain knowledge on fertilizer diffusion, soil respiration, and water‑filled porosity to predict nitrous oxide emissions from agriculture. Experiments across CNN, LSTM, and Transformer architectures show that KAINN achieves lower root mean square error, lower mean absolute error, and higher R-squared values compared to purely data‑driven models and the original AINN. The learned interfaces exhibit smoother, more physically consistent parameter trajectories with reduced uncertainty.
By Ci Lin, Futong Li, Rose Chong-Wu, Tet Yeap, Iluju Kiringa
arXiv:2607. 20778v1 Announce Type: new Abstract: Weather forecasting foundation models (FMs) are increasingly fine-tuned to predict air quality, offering fast global pollution forecasts at lower computational cost than conventional chemical transport models.
By Jason Y. Hu, Ivan Higuera-Mendieta, Patrick Obin Sturm, Makoto M. Kelp
The paper introduces a Physics Informed Recurrent Neural Network (PIRNN) that simultaneously predicts target time series and unobservable intermediate physical variables, enhancing robustness and interpretability. It adapts to any physical model with multiple equations and variables, demonstrated on groundwater level predictions using the Gardenia model. Experiments on twelve real‑world datasets show PIRNN outperforming several neural network baselines and the Gardenia model, with an ablation study confirming the value of physical knowledge.
By Etienne Lehembre (CA, LIFO), Pascal Audigane (BRGM), Vincent Nguyen (LIFO), Christel Vrain (LIFO, CA), Thi-Bich-Hanh Dao (LIFO, CA)
arXiv:2608. 00212v1 Announce Type: new Abstract: Spatial atomic layer deposition (SALD) is a leading atmospheric-pressure, high-throughput route to industrial ALD, but design and control are limited by the cost of predicting surface coverage: high-fidelity CFD is far too slow for operating-window scans, while analytic models miss transport modulation such as the gas curtain.
By Ning Hu, Chang Liu, Yunlei Jiang, Yuan Dong
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:2606. 17413v1 Announce Type: new Abstract: Space-based monitoring of atmospheric carbon dioxide (CO2) is essential for constraining the global carbon budget.
By Alejandro Calle-Saldarriaga, Felix Jimenez, Jack Grosskreuz, Jiazheng Wang, Jonathan Hobbs, Matthias Katzfuss