3 citations · 5 across the 8 of their papers we have counts for
5 papers · 1 filter
Sparse probes and murky physics: a case study of interpretability challenges in a foundation model for continuum dynamics
Katherine Rosenfeld, Maike Sonnewald
Generative AI emulators are increasingly used in scientific domains where we already have strong theory, benchmarks, and physical intuition. This raises a central evaluation and in…
OceanCBM: A Concept Bottleneck Model for Mechanistic Interpretability in Ocean Forecasting
Sanah Suri, Kieran Ringel, Maike Sonnewald
Extreme ocean phenomena are challenging not only to predict but to diagnose, as accurate forecasts alone do not reveal the underlying physical drivers. While recent machine learnin…
Unveiling 3D Ocean Biogeochemical Provinces in the North Atlantic: A Systematic Comparison and Validation of Clustering Methods
Yvonne Jenniges, Maike Sonnewald, Sebastian Maneth +2
Defining ocean regions and water masses helps to understand marine processes and can serve downstream tasks such as defining marine protected areas. However, such definitions often…
The Importance of Architecture Choice in Deep Learning for Climate Applications
Simon Dräger, Maike Sonnewald
Machine Learning has become a pervasive tool in climate science applications. However, current models fail to address nonstationarity induced by anthropogenic alterations in greenh…
Objective discovery of dominant dynamical processes with intelligible machine learning
Bryan E. Kaiser, Juan A. Saenz, Maike Sonnewald +1
The advent of big data has vast potential for discovery in natural phenomena ranging from climate science to medicine, but overwhelming complexity stymies insight. Existing theory…