5 papers
A zero-one law for one-shot system identification
Nicolas Boullé, Diana Halikias, Samuel E. Otto +1
Can a model be identified from one experiment? We study analytic systems that are linearly parameterized by a combination of prescribed dictionary terms, such as partial differenti…
Uncovering Extreme Event Mechanisms for Prediction and Control with Sensitivity-Balanced Projections
Nicholas Zolman, Sajeda Mokbel, Samuel E. Otto +1
Extreme events -- such as earthquakes and coronal mass ejections -- are common in many chaotic dynamical systems, yet are difficult to characterize and predict due to the subtle in…
Machine Learning in Viscoelastic Fluids via Energy-Based Kernel Embedding
Samuel E. Otto, Cassio M. Oishi, Fabio Amaral +2
The ability to measure differences in collected data is of fundamental importance for quantitative science and machine learning, motivating the establishment of metrics grounded in…
Operator learning without the adjoint
Nicolas Boullé, Diana Halikias, Samuel E. Otto +1
There is a mystery at the heart of operator learning: how can one recover a non-self-adjoint operator from data without probing the adjoint? Current practical approaches suggest th…
A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning
Samuel E. Otto, Nicholas Zolman, J. Nathan Kutz +1
Symmetry is present throughout nature and continues to play an increasingly central role in physics and machine learning. Fundamental symmetries, such as Poincaré invariance, allow…