research
∙
06/23/2021
Fast, high-fidelity Lyman α forests with convolutional neural networks
Full-physics cosmological simulations are powerful tools for studying th...
share
research
∙
03/16/2021
Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers
There is growing interest in data-driven weather prediction (DDWP), for ...
research
∙
01/12/2021
Estimating Galactic Distances From Images Using Self-supervised Representation Learning
We use a contrastive self-supervised learning framework to estimate dist...
research
∙
12/24/2020
Self-Supervised Representation Learning for Astronomical Images
Sky surveys are the largest data generators in astronomy, making automat...
research
∙
09/30/2020
Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations
Simulation of turbulent flows at high Reynolds number is a computational...
research
∙
05/01/2020
MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework
We propose MeshfreeFlowNet, a novel deep learning-based super-resolution...
research
∙
11/20/2019
Towards Physics-informed Deep Learning for Turbulent Flow Prediction
While deep learning has shown tremendous success in a wide range of doma...
research
∙
12/26/2017