Abstract: Supervised operator learning is an emerging machine learning paradigm with applications to modeling the evolution maps of spatio-temporal dynamical systems and approximating general black-box relationships between functional data. We propose a novel operator learning method, LOCA (Learning Operators with Coupled Attention), motivated from the attention mechanism. The input functions are mapped to a finite set of features which are then averaged with attention weights that depend on the output query locations. By coupling these attention weights together with an integral transform, LOCA is able to explicitly learn correlations in the target output functions, enabling us to approximate nonlinear operators even when the number of output function measurements is very small.

Abstract: Embodiments of the present disclosure are generally related to a method and apparatus for performing operations in subterranean wellbores. More specifically, the present disclosure provides a novel downhole tractor for use in a wellbore. The tractor includes a body and a plurality of wheels. The wheels are configured to convert rotation of the body into linear motion along the wellbore. In one embodiment, the tractor is hydraulically driven. Additionally, or alternatively, the tractor may be electrically driven. Optionally, rotation of a string of pipe interconnected to the tractor drives the tractor.