51 citations · 66 across the 10 of their papers we have counts for
5 papers · 1 filter
Theory-to-Practice Gap for Neural Networks and Neural Operators
Philipp Grohs, Samuel Lanthaler, Margaret Trautner
This work studies the sampling complexity of learning with ReLU neural networks and neural operators. For mappings belonging to relevant approximation spaces, we derive upper bound…
An operator learning perspective on parameter-to-observable maps
Daniel Zhengyu Huang, Nicholas H. Nelsen, Margaret Trautner
Computationally efficient surrogates for parametrized physical models play a crucial role in science and engineering. Operator learning provides data-driven surrogates that map bet…
Learn Like The Pro: Norms from Theory to Size Neural Computation
Margaret Trautner, Ziwei Li, Sai Ravela
The optimal design of neural networks is a critical problem in many applications. Here, we investigate how dynamical systems with polynomial nonlinearities can inform the design of…
Informative Neural Ensemble Kalman Learning
Margaret Trautner, Gabriel Margolis, Sai Ravela
In stochastic systems, informative approaches select key measurement or decision variables that maximize information gain to enhance the efficacy of model-related inferences. Neura…
Neural Integration of Continuous Dynamics
Margaret Trautner, Sai Ravela
Neural dynamical systems are dynamical systems that are described at least in part by neural networks. The class of continuous-time neural dynamical systems must, however, be numer…