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3rd Annual Workshop on AI/ML Methods in Weather and Climate Modelling
September 18-19, 2026
Research tutorials
From Physics to Farmers: Monsoon Dynamics and AI Forecasting for Agricultural Decisions
William Boos, UC Berkeley
Neural operators for weather and climate modeling
Souvik Chakraborty, IIT Delhi
Next-Generation Climate Models: From Physical Understanding and AI to Actionable Risk Projections
Tapio Schneider CalTech, Google Research
Research talks
Deep learning weather prediction for low compute scenarios
Bedartha Goswami, IISER Pune
Trusting Unverifiable Predictions: Can we use AI for climate modeling?
R. Saravanan, Texas A&M University
[post_title] => 3rd Annual Workshop on AI/ML Methods in Weather and Climate Modelling
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3rd Annual Workshop on AI/ML Methods in Weather and Climate Modelling
![](\"https://www.ashoka.edu.in/wp-content/uploads/2026/08/banner.webp\")
September 18-19, 2026
Research tutorials
![\"William](\"https://www.ashoka.edu.in/wp-content/uploads/2026/08/william-boos.png\")
From Physics to Farmers: Monsoon Dynamics and AI Forecasting for Agricultural Decisions
William Boos, UC Berkeley
![\"Souvik](\"https://www.ashoka.edu.in/wp-content/uploads/2026/08/square-image-3.jpg\")
Neural operators for weather and climate modeling
Souvik Chakraborty, IIT Delhi
![\"Tapio](\"https://www.ashoka.edu.in/wp-content/uploads/2026/08/square-image-2.jpg\")
Next-Generation Climate Models: From Physical Understanding and AI to Actionable Risk Projections
Tapio Schneider CalTech, Google Research
Research talks
![\"Bedartha](\"https://www.ashoka.edu.in/wp-content/uploads/2026/08/17a46a2d-8008-4ef1-9b07-4bcac54c12d7.jpg\")
Deep learning weather prediction for low compute scenarios
Bedartha Goswami, IISER Pune
![\"R](\"https://www.ashoka.edu.in/wp-content/uploads/2026/08/square-image.jpg\")
Trusting Unverifiable Predictions: Can we use AI for climate modeling?
R. Saravanan, Texas A&M University
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![\"Anand](\"https://www.ashoka.edu.in/wp-content/uploads/2026/09/Dr-Anand-Avinash-Deo.jpg\")
Anand Deo
Indian Institute of Management Bangalore
Abstract
We study stress-scenario generation for systems driven by multivariate heavy-tailed risk factors. Within regions where several losses are simultaneously large, stress analysis concerns both the conditional law of risk factors and the most plausible configurations producing them. We show that both tasks share the same limiting tail law but require distinct, nonnested validity criteria. Tail-measure preservation is necessary and sufficient for first-order validity of joint-stress probabilities and scaled conditional laws; tail-density preservation governs likelihood-based reverse stress testing. We develop SSGEN (Self-Similar Generative Estimation), a data-driven input model that exploits the polar decomposition of extremes by learning the angular law from intermediate exceedances and extrapolating radially through a Pareto law. We prove that SSGEN satisfies both criteria and derive convergence rates for the generated stressed law and reverse-stress solutions even without target-event observations. These guarantees transfer to decision problems based on stress-conditioned laws or likelihood-defined stress regions: optimal values and, under uniqueness, solutions converge.
Date: Wednesday, September 30, 2026
Time: 01:30 PM
Venue: AC-05-Lab-004 | Ramchandra Hall, North Campus, Ashoka University
[post_title] => Simulating Stress Laws under Extremal Dependence: Characterizing What An Input Model Must Preserve
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3rd Annual Workshop on AI/ML Methods in Weather and Climate Modelling
September 18-19, 2026
Research tutorials
From Physics to Farmers: Monsoon Dynamics and AI Forecasting for Agricultural Decisions
William Boos, UC Berkeley
Neural operators for weather and climate modeling
Souvik Chakraborty, IIT Delhi
Next-Generation Climate Models: From Physical Understanding and AI to Actionable Risk Projections
Tapio Schneider CalTech, Google Research
Research talks
Deep learning weather prediction for low compute scenarios
Bedartha Goswami, IISER Pune
Trusting Unverifiable Predictions: Can we use AI for climate modeling?
R. Saravanan, Texas A&M University
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