Advanced simulations reveal a new calcium carbonate phase whose unusual elastic behavior may explain puzzling seismic and electrical anomalies beneath ancient continents.
machine learning & AI
Posted inEditors' Highlights
Deep Learning Goes Multi-Tasking
In hydrological modeling, predicting multiple tasks helps in identifying physical rules and generalizations.
Posted inEditors' Highlights
As Simple as Possible: The Importance of Idealized Climate Models
As models that simulate Earth’s climate system become increasingly complex, the use of simpler and more flexible idealized models remains important for science and education.
Posted inResearch Spotlights
Machine Learning Simulates 1,000 Years of Climate
The Deep Learning Earth System Model is competitive with CMIP6 models and uses less computational power.
Posted inEditors' Highlights
Nearly 94 Million Boulders Mapped on the Moon Using Deep Learning
Scientists used a deep learning algorithm to map the size and location of nearly 94 million boulders on the lunar surface, highlighting differences in boulder densities and size distributions.
Posted inNews
Machine Learning Model Flags Early, Invisible Signs of Marsh Decline
Decreases in underground plant biomass could signal future marsh loss and prompt conservation measures.
Posted inEditors' Highlights
Rock Solid Augmentation: AI-Driven Digital Rock Analysis
Boosting digital rock images with AI-powered augmentation and quality analysis could improve subsurface engineering decisions.
Posted inResearch Spotlights
Storm Prediction Gets 10 Times Faster Thanks to AI
Forecasters hope new algorithms will lead to earlier warnings of when dangerous weather is on the way.
Posted inEditors' Highlights
A Leap Toward Next-Generation Ocean Models
GPU-optimized ocean modeling achieves decade-long simulations in a day, enabling mesoscale-resolving climate simulations that open new opportunities for long-term planning in a changing climate.
Posted inScience Updates
A Two-Step Approach to Training Earth Scientists in AI
Researchers learned machine learning methods during a boot camp, then applied their new knowledge to real-world research problems during a hackathon.