scholarly journals Combining Physically Based Modeling and Deep Learning for Fusing GRACE Satellite Data: Can We Learn From Mismatch?

2019 ◽  
Vol 55 (2) ◽  
pp. 1179-1195 ◽  
Author(s):  
Alexander Y. Sun ◽  
Bridget R. Scanlon ◽  
Zizhan Zhang ◽  
David Walling ◽  
Soumendra N. Bhanja ◽  
...  
Keyword(s):  
Deep Learning ◽  
Satellite Data ◽  
Physically Based Modeling ◽  
Physically Based ◽  
Grace Satellite

INTEGRATING GRACE SATELLITE DATA AND GROUND-BASED MONITORING AND MODELING TO ASSESS WATER STORAGE CHANGES IN U.S. AQUIFERS

2019 ◽  
Author(s):  
Bridget R. Scanlon ◽  
◽  
Ashraf Rateb ◽  
Zizhan Zhang ◽  
Don Pool ◽  
...  
Keyword(s):  
Satellite Data ◽  
Water Storage ◽  
Grace Satellite

Physically Based Modeling of Cold Load Pickup

1981 ◽  
Vol PER-1 (9) ◽  
pp. 27-28 ◽  
Author(s):  
Satoru Ihara ◽  
Fred C. Schweppe
Keyword(s):  
Physically Based Modeling ◽  
Physically Based

Physically based modeling of the mechanical behavior of TRIP steels

2008 ◽  
Vol 1 (S1) ◽  
pp. 57-60 ◽  
Author(s):  
J. Bouquerel ◽  
K. Verbeken ◽  
J. Van Slycken ◽  
P. Verleysen ◽  
Y. Houbaert
Keyword(s):  
Mechanical Behavior ◽  
Physically Based Modeling ◽  
Trip Steels ◽  
Physically Based

Using deep learning to nowcast the spatial coverage of convection from Himawari-8 satellite data

2021 ◽  
Author(s):  
Ryan Lagerquist ◽  
Jebb Q. Stewart ◽  
Imme Ebert-Uphoff ◽  
Christina Kumler
Keyword(s):  
Deep Learning ◽  
Satellite Data ◽  
Geographic Location ◽  
Ground Truth ◽  
Atmospheric Science ◽  
Skill Score ◽  
Time Of Day ◽  
Lead Times ◽  
Weather Radars ◽  
Spatial Coverage

AbstractPredicting the timing and location of thunderstorms (“convection”) allows for preventive actions that can save both lives and property. We have applied U-nets, a deep-learning-based type of neural network, to forecast convection on a grid at lead times up to 120 minutes. The goal is to make skillful forecasts with only present and past satellite data as predictors. Specifically, predictors are multispectral brightness-temperature images from the Himawari-8 satellite, while targets (ground truth) are provided by weather radars in Taiwan. U-nets are becoming popular in atmospheric science due to their advantages for gridded prediction. Furthermore, we use three novel approaches to advance U-nets in atmospheric science. First, we compare three architectures – vanilla, temporal, and U-net++ – and find that vanilla U-nets are best for this task. Second, we train U-nets with the fractions skill score, which is spatially aware, as the loss function. Third, because we do not have adequate ground truth over the full Himawari-8 domain, we train the U-nets with small radar-centered patches, then apply trained U-nets to the full domain. Also, we find that the best predictions are given by U-nets trained with satellite data from multiple lag times, not only the present. We evaluate U-nets in detail – by time of day, month, and geographic location – and compare to persistence models. The U-nets outperform persistence at lead times ≥ 60 minutes, and at all lead times the U-nets provide a more realistic climatology than persistence. Our code is available publicly.

Physically based modeling of shear modulus-temperature relationship for thermosets

2015 ◽  
Vol 55 (12) ◽  
pp. 2893-2898 ◽  
Author(s):  
Wei Sun ◽  
Anastasios P. Vassilopoulos ◽  
Thomas Keller
Keyword(s):  
Shear Modulus ◽  
Temperature Relationship ◽  
Physically Based Modeling ◽  
Physically Based
Sign in / Sign up

Export Citation Format

Share Document