Summer 2025 Lecture in Climate Data Science: TOBIAS FINN

LEAP’s primary research strategy is to improve near-term climate projections by merging physical modeling with machine learning across a continuum from expertise in climate science and climate modeling to cutting-edge machine learning algorithms. The benefits will be significant for both the climate and data sciences communities. Climate scientists and modelers struggle to fully integrate the wealth of existing datasets into their models, while machine learning algorithms have been good at emulating and interpolating but have difficulties extrapolating or predicting extremes. By combining both approaches, LEAP will trigger a significant advancement for data science algorithms applied to physical problems. LEAP will incorporate physics and causal mechanisms into machine learning algorithms for better generalization and extrapolation, while optimally using the wealth of data available to climate science, in order to better predict the future.

LEAP Center

JOIN THE LEARNING THE EARTH WITH ARTIFICIAL INTELLIGENCE + PHYSICS (LEAP) CENTER AT COLUMBIA FOR A SEMINAR ON CLIMATE DATA SCIENCE.

Columbia Innovation Hub - Tang Family Hall

<h3>TITLE: "GOING WITH THE FLOW: TOWARDS PHYSICALLY CONSISTENT DATA-DRIVEN EARTH SYSTEM MODELS"</h3><h3>SPEAKER: Tobias Finn (École des Pont ParisTech/CEREA)</h3><p><br></p><h3>Date: June 5, 2025</h3><h3>Time: 12:00 p.m.</h3><h3>Format: Hybrid</h3><h3>Virtual: Zoom link provided upon registration</h3><h3>In-person: Columbia Innovation Hub, 2276 12th Avenue, Second Floor, Room 202, New York, NY 10027</h3><p><em><em>Tobias will present remotely, but attendees are welcome to gather and watch together at the Columbia Engineering Innovation Hub.</em></em></p><p>*Please note that in-person space is limited.*</p><p>__________________________________________________________________________________</p><p><strong><strong>Abstract</strong></strong>: The AI revolution has transformed Earth system modelling. Deep neural networks trained on large datasets for short-term forecasts predict the temporal evolution of the atmosphere and other components of the Earth system at unprecedented speeds, while reaching the accuracy of the best geophysical models. However, due to their training protocol, these deterministic models have issues with long-term stability and physical consistency. Yet, there is hope to resolve these issues with generative models, which have recently revolutionised image and video generation.</p><p>In this talk, I take you on a journey that brings us from simple dynamics like Lorenz 1963 to the rough waters in the Arctic Ocean and around Antarctica. Starting from first principles, we will see how these generative models learn to iterate from noise to clean data. When we apply them to forecasting, we can produce large ensembles from a single set of initial conditions. Equipped with this capability, I will show that these models can learn emergent properties of the system, while outperforming deterministic forecasting models. Furthermore, we will exploit prior-known physical properties to efficiently train models for local-to-global scales, which, applied to forecasting, can stably project to long-term conditions. Additionally exhibiting a previously unseen physical consistency, these models are a leap forward in the efficiency and realism of data-driven Earth system models.</p><p><strong><strong>Bio</strong></strong>: Tobias is a post-doctoral scientist, who has studied meteorology and Earth system modelling in Hamburg. He works at the intersection of these fields with machine learning with a special focus on generative methods and data assimilation. He explores how these methods work at an underlying level, and how we can harness them to learn more about the physics of the Earth system.</p><p><br></p><p>Learn More: <a href="https://leap.columbia.edu" title="https://leap.columbia.edu" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer"><strong><strong>LEAP</strong></strong></a></p>

<p><strong><strong>TOBIAS FINN</strong></strong><br>École des Ponts ParisTech</p>

Summer 2025 Lecture in Climate Data Science: TOBIAS FINN

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Summer 2025 Lecture in Climate Data Science: TOBIAS FINN

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