Search Results for author:
Found 8 papers, 2 papers with code
GEO-Bench: Toward Foundation Models for Earth Monitoring
Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to downstream tasks.
Multiscale Neural Operator: Learning Fast and Grid-independent PDE Solvers
Numerical simulations in climate, chemistry, or astrophysics are computationally too expensive for uncertainty quantification or parameter-exploration at high-resolution.
Spectral PINNs: Fast Uncertainty Propagation with Physics-Informed Neural Networks
We aim to quickly quantify the impact of uncertain parameters onto the solution of a PDE - that is - we want to perform fast uncertainty propagation.
WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data
We provide a thorough and extensible benchmark of leading deep learning-based super-resolution techniques, including the enhanced super-resolution generative adversarial network (ESRGAN) and an enhanced deep super-resolution (EDSR) network, on wind and solar data.
PCE-PINNs: Physics-Informed Neural Networks for Uncertainty Propagation in Ocean Modeling
Climate models project an uncertainty range of possible warming scenarios from 1. 5 to 5 degree Celsius global temperature increase until 2100, according to the CMIP6 model ensemble.
Physically-Consistent Generative Adversarial Networks for Coastal Flood Visualization
Our work aims to enable more visual communication of large-scale climate impacts via visualizing the output of coastal flood models as satellite imagery.
Technology Readiness Levels for Machine Learning Systems
The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end.
Physics-informed GANs for Coastal Flood Visualization
As climate change increases the intensity of natural disasters, society needs better tools for adaptation.
