The paper introduces a neural‑network based local‑box chemical kinetics solver for exoplanet atmospheres, employing a residual flow‑map architecture. It achieves microsecond‑scale inference with percent‑level accuracy across a wide range of temperatures, pressures, time steps, and compositional variations, outperforming other machine‑learning models and handling the extreme stiffness of atmospheric chemistry. The surrogate model offers a flexible, efficient alternative to classical solvers for state‑to‑state flow‑map problems in numerical simulations.
By Isaac Malsky, Xi Zhang, Tiffany Kataria, Matthew Graham, Ziyu Huang, Boris Bonev, Shang-Min Tsai, Elspeth K. H. Lee
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
arXiv:2501. 17296v4 Announce Type: replace-cross Abstract: Multiphysics simulations play an essential role in accurately modeling complex interactions across diverse scientific and engineering domains Although neural operators especially the Fourier Neural Operator FNO have significantly improved computational efficiency they often fail to effectively capture intricate correlations inherent in coupled physical processes To address this limitation we introduce COMPOL a novel coupled multiphysics operator learning framework COMPOL extends conventional operator architectures by incorporating sophisticated recurrent and attentionbased aggregation mechanisms effectively modeling interdependencies among interacting physical processes within latent feature spaces Our approach is architectureagnostic and seamlessly integrates into various neural operator frameworks that involve latent space transformations Extensive experiments on diverse benchmarksincluding biological reactiondiffusion systems patternforming chemical reactions multiphase geological flows and thermohydromechanical processes demonstrate that COMPOL consistently achieves superior predictive accuracy compared to stateoftheart methods.
By Junqi Qu, Tao Wang, Yushun Dong, Hewei Tang, Shibo Li
arXiv:2506. 19340v4 Announce Type: replace-cross Abstract: Whole-atmosphere models such as WACCM-X resolve coupling from the Earth surface to the Mesosphere-Lower-Thermosphere (MLT), and Ionosphere-Thermosphere (IT) systems with expensive computational costs.
By Jiahui Hu, Wenjun Dong
arXiv:2607. 28079v1 Announce Type: new Abstract: Chemical property prediction plays a critical role in accelerating scientific discovery in chemistry, materials science, and drug development.
By Tianyou Bai, Huan Wang, Mingchen Gao, Fangyue Lin, Pinze Ren, Zhenlin Zhao, Siming Dong
arXiv:2601.21151v3 Announce Type: replace
Abstract: Machine-learning approaches to weather forecasting often employ a monolithic architecture in which distinct physical mechanisms, such as advection,...
By Carlos A. Pereira, St\'ephane Gaudreault, Valentin Dallerit, Christopher Subich, Shoyon Panday, Siqi Wei, Sasa Zhang, Siddharth Rout, Eldad Haber, Raymond J. Spiteri, David Millard
arXiv:2606. 30961v1 Announce Type: cross Abstract: Advances in deep learning architectures and representations have enabled ML-driven chemical property prediction, but state-of-the-art (SOTA) models have remained largely confined to independent codebases and lack support for diverse chemical species.
By Jacob W. Toney, Samir Darouich, Yiran Wang, Aaron G. Garrison, Johannes K\"astner, Heather J. Kulik
arXiv:2603. 22320v3 Announce Type: replace Abstract: For decades, physics-based climate models have been used to provide insights for climate decision-making.
By Luca Schmidt, Nina Effenberger, Vitus Benson, Philine L. Bommer, Robert Brunstein, Mikel N. Legasa, Maxim Samarin, Maybritt Schillinger
arXiv:2610.16630v1 Announce Type: cross
Abstract: Accurate long-term air quality forecasting is challenging due to long-range transport, meteorological variability, and nonlinear chemical reactions u...
By Kien Le Trung, Tung Kieu, Dinh Duc Nha Nguyen, Cuong Do, Bin Yang, Phi Long Nguyen
arXiv:2603. 22320v2 Announce Type: replace Abstract: For decades, physics-based climate models have been used to provide insights for climate decision-making.
By Luca Schmidt, Nina Effenberger, Vitus Benson, Philine L. Bommer, Robert Brunstein, Mikel N. Legasa, Maxim Samarin, Maybritt Schillinger
arXiv:2605.16929v2 Announce Type: replace
Abstract: Global climate models are essential tools to simulate past and potential future pathways of climate change, as well as associated climate impacts....
By Graham Clyne, Julia Kaltenborn, Peer Nowack, Claire Monteleoni, Anastase Charantonis
arXiv:2602. 12706v2 Announce Type: replace Abstract: Neural operators have emerged as fast surrogate solvers for parametric partial differential equations (PDEs).
By Heechang Kim, Qianying Cao, Hyomin Shin, Seungchul Lee, George Em Karniadakis, Minseok Choi