Integrating scientific knowledge with machine learning for engineering and environmental systems
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
Increasing the accuracy and resolution of precipitation forecasts using deep generative models
Accurately forecasting extreme rainfall is notoriously difficult, but is also ever more crucial for
society as climate change increases the frequency of such extremes. Global numerical …
society as climate change increases the frequency of such extremes. Global numerical …
[HTML][HTML] Long-term fatigue estimation on offshore wind turbines interface loads through loss function physics-guided learning of neural networks
F de N Santos, P D'Antuono, K Robbelein, N Noppe… - Renewable Energy, 2023 - Elsevier
Offshore wind turbines are exposed during their serviceable lifetime to a wide range of loads
from aero-, hydro-and structural dynamics. This complex loading scenario will have an …
from aero-, hydro-and structural dynamics. This complex loading scenario will have an …
Advancing parsimonious deep learning weather prediction using the HEALPix mesh
M Karlbauer, N Cresswell‐Clay… - Journal of Advances …, 2024 - Wiley Online Library
We present a parsimonious deep learning weather prediction model to forecast seven
atmospheric variables with 3‐hr time resolution for up to 1‐year lead times on a 110‐km …
atmospheric variables with 3‐hr time resolution for up to 1‐year lead times on a 110‐km …
Streamflow estimation in a mediterranean watershed using neural network models: A detailed description of the implementation and optimization
This study compares the performance of three different neural network models to estimate
daily streamflow in a watershed under a natural flow regime. Based on existing and public …
daily streamflow in a watershed under a natural flow regime. Based on existing and public …
[HTML][HTML] On the deep learning approach for improving the representation of urban climate: The Paris urban heat island and temperature extremes
As cities encompass most of the global population, it is crucial to understand the effects of
climate change in an urban context to develop tailored adaptation and mitigation strategies …
climate change in an urban context to develop tailored adaptation and mitigation strategies …
Uncertainty calibration of passive microwave brightness temperatures predicted by Bayesian deep learning models
P Ortiz, E Casas, M Orescanin… - … Intelligence for the …, 2023 - journals.ametsoc.org
Visible and infrared radiance products of geostationary orbiting platforms provide virtually
continuous observations of Earth. In contrast, low-Earth orbiters observe passive microwave …
continuous observations of Earth. In contrast, low-Earth orbiters observe passive microwave …
Physics-constrained deep learning postprocessing of temperature and humidity
Weather forecasting centers currently rely on statistical postprocessing methods to minimize
forecast error. This improves skill but can lead to predictions that violate physical principles …
forecast error. This improves skill but can lead to predictions that violate physical principles …
SMLFire1. 0: a stochastic machine learning (SML) model for wildfire activity in the western United States
The annual area burned due to wildfires in the western United States (WUS) increased by
more than 300% between 1984 and 2020. However, accounting for the nonlinear, spatially …
more than 300% between 1984 and 2020. However, accounting for the nonlinear, spatially …
Reconstruction of surface kinematics from sea surface height using neural networks
Abstract The Surface Water and Ocean Topography (SWOT) satellite is expected to observe
sea surface height (SSH) down to scales approaching∼ 15 km, revealing submesoscale …
sea surface height (SSH) down to scales approaching∼ 15 km, revealing submesoscale …