We propose a novel machine learning framework for solving optimization
p...
We explore using neural operators, or neural network representations of
...
Neural operators have gained significant attention recently due to their...
We address the solution of large-scale Bayesian optimal experimental des...
We present a parsimonious surrogate framework for learning high dimensio...
Many-query problems, arising from uncertainty quantification, Bayesian
i...
In this work we analyze the role nonlinear activation functions play at
...
Many tasks in engineering fields and machine learning involve minimizing...