Climate modeling is entering a new era—one powered by artificial intelligence. Scientists at New York University’s Courant Institute of Mathematical Sciences and the Center for Data Science have developed Samudra, a groundbreaking 3D neural network that emulates the ocean with remarkable speed and precision.
Named after the Sanskrit word for “ocean”, Samudra captures essential ocean variables such as sea surface height, temperature, salinity, and currents throughout the ocean’s depth. What sets it apart is its efficiency: Samudra runs 100 times faster than traditional ocean models while requiring far less computational power—a leap forward for sustainable and scalable climate prediction.
According to Professor Laure Zanna, the project’s lead researcher, the model learns directly from real-world ocean data much like AI-based weather forecasting systems. “Once trained, you can unleash it and run it for years and years,” explains Carlos Fernandez-Granda, Director of NYU’s Center for Data Science. “It delivers long-term ocean simulations that are both fast and realistic.”
This innovation is especially critical given the oceans’ central role in regulating Earth’s climate—absorbing over 90% of excess heat and 25% of carbon dioxide emissions. By simulating ocean dynamics more efficiently, Samudra promises to improve climate projections, policy planning, and risk mitigation strategies around the globe.
As climate change continues to reshape global weather systems, tools like Samudra demonstrate how AI and data science can accelerate scientific discovery while reducing costs. This fusion of computational modeling and machine learning may define the next generation of environmental analytics and oceanography.
At DatalytIQs Academy, we adapt such breakthroughs to teach learners the power of AI-driven climate modeling, environmental data analysis, and computational sustainability—equipping the next generation to harness data for a more resilient planet.
Credits:
-
Author: Carly Thompson (New York University)
-
Editors: Lisa Lock, Andrew Zinin
-
Image Credit: New York University
-
Adapted by: DatalytIQs Academy
