Towards Gaussian Process for operator learning: An uncertainty aware resolution independent operator learning algorithm for computational mechanics
The growing demand for accurate, efficient, and scalable solutions in computational
mechanics highlights the need for advanced operator learning algorithms that can efficiently …
mechanics highlights the need for advanced operator learning algorithms that can efficiently …
Linearization Turns Neural Operators into Function-Valued Gaussian Processes
Modeling dynamical systems, eg in climate and engineering sciences, often necessitates
solving partial differential equations. Neural operators are deep neural networks designed to …
solving partial differential equations. Neural operators are deep neural networks designed to …