Search Results for author:
Found 8 papers, 3 papers with code
Deep Active Learning for Scientific Computing in the Wild
Deep learning (DL) is revolutionizing the scientific computing community.
Transformers For Recognition In Overhead Imagery: A Reality Check
In this paper we systematically compare the impact of adding transformer structures into state-of-the-art segmentation models for overhead imagery.
Towards Robust Deep Active Learning for Scientific Computing
Deep learning (DL) is revolutionizing the scientific computing community.
Utilizing geospatial data for assessing energy security: Mapping small solar home systems using unmanned aerial vehicles and deep learning
In this work, we explore the viability and cost-performance tradeoff of using automatic SHS detection on unmanned aerial vehicle (UAV) imagery as an alternative to satellite imagery.
Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions
To this end, we propose a physics-infused deep neural network based on the Blaschke products for phase retrieval.
Benchmarking Data-driven Surrogate Simulators for Artificial Electromagnetic Materials
Recently, it has been shown that deep learning can be an alternative solution to infer the relationship between an AEM geometry and its properties using a (relatively) small pool of CEMS data.
GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery
Energy system information valuable for electricity access planning such as the locations and connectivity of electricity transmission and distribution towers, termed the power grid, is often incomplete, outdated, or altogether unavailable.
Benchmarking deep inverse models over time, and the neural-adjoint method
We consider the task of solving generic inverse problems, where one wishes to determine the hidden parameters of a natural system that will give rise to a particular set of measurements.
