Ph.D. student Phillip Si and Assistant Professor Peng Chen developed Latent-EnSF, a technique that improves how ML models assimilate data to make predictions.
Picture a single forecasting mistake triggering a cascade of negative consequences, such as surplus inventory, strained supplier relationships and disappointed customers. In today's world, accurate ...
On Tuesday, the peer-reviewed journal Science published a study that shows how an AI meteorology model from Google DeepMind called GraphCast has significantly outperformed conventional weather ...
Read more about From disease detection to biomass forecasting: AI improves aquaculture risk strategy on Devdiscourse ...
A range of national meteorological services across Europe and ECMWF have launched Anemoi, a framework for creating machine learning (ML) weather forecasting systems. Named after the Greek gods of the ...
A recent study, “Picking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns,” set out to answer a critical question: Can machine learning techniques improve the prediction ...
Read more about Artificial intelligence boosts financial forecasting accuracy in banking sector on Devdiscourse ...
The peer-reviewed research, published in npj Climate and Atmospheric Science, assesses the viability of applying a machine learning (ML) weather model to global seasonal forecasts, which are vital for ...
Joshua S. Fu received funding from U. S. EPA for wildfire and human health studies. Wildfire smoke from Canada’s extreme fire season has left a lot of people thinking about air quality and wondering ...
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