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Physics-Based AI Delivers First Global Map of Carbon Cycling in Ocean Sediments
4+ day, 10+ hour ago (154+ words) Home | News | Research Researchers at The University of Manchester have developed a new physics-based artificial intelligence approach that, for the first time, enables accurate global-scale predictions of how dissolved organic carbon moves between seawater and marine sediments, a crucial but…...
Physics based AI unlocks first global predictions of carbon cycling in ocean sediments
4+ day, 11+ hour ago (555+ words) Researchers at The University of Manchester have developed a physics-based artificial intelligence framework that, for the first time, enables global-scale predictions of dissolved organic carbon cycling between seawater and marine sediments. The approach reveals a previously unquantified component of Earth's…...
Deep learning mirrors ecological time scales in water quality forecasting
1+ week, 15+ hour ago (483+ words) | Newswise Newswise Deep learning mirrors ecological time scales in water quality forecasting How deep-learning modules match ecological time scales for smarter water forecasts. This study compared nine time-series forecasting architectures across two reservoirs with contrasting trophic states (mesotrophic Königshütte with…...
木卫一隐藏热源首次曝光 - Eos
1+ week, 3+ day ago (69+ words) Sarah Stanley, a freelance writer for Eos, has a background in environmental microbiology but covers a wide range of science stories for a variety of audiences. She has also written for PLOS, the University of Washington, Kaiser Permanente, Stanford Medicine,…...
P1-KAN: An Effective Kolmogorov-Arnold Network for Hydraulic Valley
2+ week, 1+ day ago (71+ words) A new artificial-intelligence architecture designed to handle the jagged, unruly mathematics of real-world systems has outperformed both conventional neural networks and established optimization software in a demanding test involving a French hydraulic valley. Called P1-KAN, the model is a new…...
Applied Sciences, Vol. 16, Pages 8412: Discovering Multiple Conservation Laws from Trajectories by Machine Learning
2+ week, 5+ day ago (355+ words) Conservation laws are core concepts in dynamical system modeling and the study of physical symmetries. Although machine learning has achieved significant progress in discovering physical laws, existing methods often face challenges such as identifying only a single conserved quantity. To…...
Applied Sciences, Vol. 16, Pages 8304: A Dynamically Adaptive Cell-Centred Lattice Boltzmann Framework for Shallow-Water Flows with Wetting and Drying
3+ week, 2+ day ago (323+ words) Solving the shallow-water equations (SWEs) using the lattice Boltzmann method (LBM) can be computationally expensive on uniform grids. Here we develop a quadtree-based adaptive mesh refinement framework that preserves the Cartesian grid structure while enabling local refinement. A cell-centred data…...
Adversarial Apparel Patterns
4+ week, 1+ day ago (78+ words) For consumers, noRecognition offers a physical approach to reducing automated detection without blocking cameras from recording footage. The project reflects growing interest in adversarial privacy tools designed to interfere with algorithmic surveillance. The Conceptual 'PAT' Autonomous Delivery Robot is Reliable…...