Search Results for author:
Found 4 papers, 1 papers with code
A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network
To mitigate global warming, greenhouse gas sources need to be resolved at a high spatial resolution and monitored in time to ensure the reduction and ultimately elimination of the pollution source.
Machine Guided Discovery of Novel Carbon Capture Solvents
The increasing importance of carbon capture technologies for deployment in remediating CO2 emissions, and thus the necessity to improve capture materials to allow scalability and efficiency, faces the challenge of materials development, which can require substantial costs and time.
S3RP: Self-Supervised Super-Resolution and Prediction for Advection-Diffusion Process
We present a super-resolution model for an advection-diffusion process with limited information.
Physics-Informed Neural Network Super Resolution for Advection-Diffusion Models
Physics-informed neural networks (NN) are an emerging technique to improve spatial resolution and enforce physical consistency of data from physics models or satellite observations.
