3 citations · 5 across the 8 of their papers we have counts for
9 papers
From objective discovery to prediction of global ocean eco-provinces: A pathway for trustworthy learning
Makayla McDevitt, Maike Sonnewald, Stephanie Dutkiewicz
Marine ecosystems are increasingly impacted by climate change, necessitating tools to identify and predict spatial habitat information. To build such tools, ecological marine provi…
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…
Setting the Standard: Recommended Practices for Data Preprocessing in Data-Driven Climate Prediction
Jason C. Furtado, Maria J. Molina, Marybeth C. Arcodia +12
Artificial intelligence (AI) - and specifically machine learning (ML) - applications for climate prediction across timescales are proliferating quickly. The emergence of these meth…
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…
Building Understandable Messaging for Policy and Evidence Review (BUMPER) with AI
Katherine A. Rosenfeld, Maike Sonnewald, Sonia J. Jindal +2
We introduce a framework for the use of large language models (LLMs) in Building Understandable Messaging for Policy and Evidence Review (BUMPER). LLMs are proving capable of provi…
A Moonshot for AI Oracles in the Sciences
Bryan Kaiser, Tailin Wu, Maike Sonnewald +2
Nobel laureate Philip Anderson and Elihu Abrahams once stated that, "even if machines did contribute to normal science, we see no mechanism by which they could create a Kuhnian rev…
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…