Kavli Institute for Theoretical Physics Courses

Using AI to Facilitate Environmental Justice - The Need for Ethical & Responsible AI

Explores the application of AI in environmental justice, emphasizing ethical considerations and responsible practices for weather and climate research. Discusses potential impacts and challenges in this emerging field.

• YouTube
• 44 minutes
• Self-Paced
• Free Video

Deep Learning and Energy Models for Fine Dead Wood Segmentation - Jacquelyn Shelton

Explore deep learning and energy models for fine dead wood segmentation in climate research, focusing on carbon cycle implications and advanced image analysis techniques.

• YouTube
• 41 minutes
• Self-Paced
• Free Video

Atmospheric Radiation - Using Machine Learning for the Unknowable and Uncomputable - Robert Pincus

Exploring machine learning applications in atmospheric radiation to tackle unknowable and uncomputable aspects, enhancing climate modeling and understanding of Earth's energy balance.

• YouTube
• 45 minutes
• Self-Paced
• Free Video

Skillful El Nino Prediction Beyond Predictability Barriers - Henk Dijkstra

Explores advanced techniques for predicting El Niño events beyond traditional limitations, discussing innovative approaches to enhance climate forecasting accuracy and extend prediction timeframes.

• YouTube
• 42 minutes
• Self-Paced
• Free Video

Relating Coastal Sea Level to Its Drivers in the Interior - Abigail Bodner

Explore the connection between coastal sea levels and interior drivers using advanced data analysis and machine learning techniques to enhance climate change understanding and prediction.

• YouTube
• 39 minutes
• Self-Paced
• Free Video

Deep Learning for Subseasonal Global Precipitation Prediction - Maria Molina

Explore deep learning techniques for predicting global precipitation patterns on a subseasonal timescale, advancing climate science through innovative machine learning approaches.

• YouTube
• 46 minutes
• Self-Paced
• Free Video

Objective Discovery of Dominant Dynamical Regimes - Bryan Kaiser - Climate-C21

Discover dominant dynamical regimes in climate systems using machine learning and big data. Explore objective methods to advance theoretical understanding and inform future climate predictions at regional scales.

• YouTube
• 44 minutes
• Self-Paced
• Free Video

Revealing the Impact of Global Heating on the Meridional Overturning Circulation

Explore the effects of global heating on ocean circulation using transparent machine learning techniques. Gain insights into climate system dynamics and future regional impacts.

• YouTube
• 45 minutes
• Self-Paced
• Free Video

Causal Inference for Earth System Sciences - Jakob Runge

Exploring causal inference techniques for Earth system sciences, focusing on advanced methods to uncover complex relationships in climate data and improve understanding of multi-scale processes.

• YouTube
• 45 minutes
• Self-Paced
• Free Video

Deep Unsupervised Learning for Climate Informatics - Claire Monteleoni

Exploring deep unsupervised learning techniques for climate data analysis, focusing on innovative approaches to extract insights from complex Earth system observations and modeling data.

• YouTube
• 46 minutes
• Self-Paced
• Free Video

Representation Learning and Custom Loss Functions for Atmospheric Data - Christian Lessig

Explores representation learning and custom loss functions for atmospheric data analysis, advancing climate science through machine learning techniques to extract insights from complex Earth system observations.

• YouTube
• 43 minutes
• Self-Paced
• Free Video

Gaussianizing the Earth - Gus Camps-Valls

Exploring Earth system dynamics through machine learning and big data analysis, focusing on multi-scale processes and causal inference to advance climate science and inform future predictions.

• YouTube
• 43 minutes
• Self-Paced
• Free Video

Exploring Physical and Machine Learning Approaches for Stochastic Modeling and Ensemble Prediction of Weather and Climate

Explores innovative approaches combining physical principles and machine learning for stochastic modeling and ensemble prediction in weather and climate systems, addressing challenges in multi-scale processes and future projections.

• YouTube
• 42 minutes
• Self-Paced
• Free Video

Data-Driven Subgrid-Scale Modeling - Stability, Extrapolation, & Interpretation

Explores data-driven subgrid-scale modeling in climate science, focusing on stability, extrapolation, and interpretation. Discusses advancements in theoretical understanding and the potential of machine learning to address complex climate processes.

• YouTube
• 39 minutes
• Self-Paced
• Free Video

Practical Integrability - David Weiss

Explores universal aspects of non-equilibrium many-body physics, focusing on novel phases and universality classes beyond equilibrium paradigms. Bridges statistical, AMO, condensed matter, and high-energy physics.

• YouTube
• 31 minutes
• Self-Paced
• Free Video