7 citations · 12 across the 4 of their papers we have counts for
4 papers
Formulation and Learning Of The Forecasting Problem
Edoardo Calvello, Elizabeth Carlson, Nikola Kovachki +2
Machine learning has opened new frontiers in purely data-driven forecasting of dynamical systems; the resulting methods are showing considerable promise in some application domains…
Continuum Attention for Neural Operators
Edoardo Calvello, Nikola B. Kovachki, Matthew E. Levine +1
Transformers, and the attention mechanism in particular, have become ubiquitous in machine learning. Their success in modeling nonlocal, long-range correlations has led to their wi…
Learning About Structural Errors in Models of Complex Dynamical Systems
Jin-Long Wu, Matthew E. Levine, Tapio Schneider +1
Complex dynamical systems are notoriously difficult to model because some degrees of freedom (e.g., small scales) may be computationally unresolvable or are incompletely understood…
A Framework for Machine Learning of Model Error in Dynamical Systems
Matthew E. Levine, Andrew M. Stuart
The development of data-informed predictive models for dynamical systems is of widespread interest in many disciplines. We present a unifying framework for blending mechanistic and…