This article discusses the use of neural operators to efficiently approximate mappings between infinite-dimensional Banach spaces, allowing for real-time predictions for highly nonlinear and…
Browsing: Computational Efficiency
Orchid is a new sequence modeling architecture that integrates a data-dependent convolution mechanism to overcome the limitations of traditional attention-based models. It aims to…
A groundbreaking deep learning model, developed by researchers from KTH Royal Institute of Technology, is revolutionizing the field of aerodynamics by accurately predicting airflow…
Deep learning on groups is an area of geometric deep learning that is rapidly growing. Groups include homogenous spaces with global symmetries, such as…