ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2104.08134
  4. Cited By
Integrating Domain Knowledge in Data-driven Earth Observation with
  Process Convolutions

Integrating Domain Knowledge in Data-driven Earth Observation with Process Convolutions

16 April 2021
D. Svendsen
M. Piles
Jordi Munoz-Marí
D. Luengo
Luca Martino
Gustau Camps-Valls
ArXiv (abs)PDFHTML

Papers citing "Integrating Domain Knowledge in Data-driven Earth Observation with Process Convolutions"

11 / 11 papers shown
Title
Machine Learning Information Fusion in Earth Observation: A
  Comprehensive Review of Methods, Applications and Data Sources
Machine Learning Information Fusion in Earth Observation: A Comprehensive Review of Methods, Applications and Data Sources
S. Salcedo-Sanz
Pedram Ghamisi
M. Piles
M. Werner
L. Cuadra
Á. Moreno-Martínez
E. Izquierdo-Verdiguier
Jordi Munoz-Marí
Amir H. Mosavi
G. Camps-Valls
78
180
0
07 Dec 2020
Fusing Optical and SAR time series for LAI gap filling with multioutput
  Gaussian processes
Fusing Optical and SAR time series for LAI gap filling with multioutput Gaussian processes
L. Pipia
Jordi Munoz-Marí
E. Amin
Santiago Belda
Gustau Camps-Valls
J. Verrelst
41
81
0
05 Dec 2020
Active emulation of computer codes with Gaussian processes --
  Application to remote sensing
Active emulation of computer codes with Gaussian processes -- Application to remote sensing
D. Svendsen
Luca Martino
Gustau Camps-Valls
40
54
0
13 Dec 2019
Using Physics-Informed Super-Resolution Generative Adversarial Networks
  for Subgrid Modeling in Turbulent Reactive Flows
Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows
Mathis Bode
M. Gauding
Zeyu Lian
D. Denker
M. Davidovic
K. Kleinheinz
J. Jitsev
H. Pitsch
AI4CE
65
21
0
26 Nov 2019
Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in
  Simulating Lake Temperature Profiles
Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles
X. Jia
J. Willard
Anuj Karpatne
J. Read
Jacob Aaron Zwart
M. Steinbach
Vipin Kumar
PINNAI4CE
43
213
0
31 Oct 2018
Deep Learning for Physical Processes: Incorporating Prior Scientific
  Knowledge
Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge
Emmanuel de Bézenac
Arthur Pajot
Patrick Gallinari
PINNAI4CE
109
318
0
21 Nov 2017
Joint Gaussian Processes for Biophysical Parameter Retrieval
Joint Gaussian Processes for Biophysical Parameter Retrieval
D. Svendsen
Luca Martino
M. Campos-Taberner
F. J. García-Haro
Gustau Camps-Valls
42
49
0
14 Nov 2017
The Mythos of Model Interpretability
The Mythos of Model Interpretability
Zachary Chase Lipton
FaML
183
3,708
0
10 Jun 2016
Structure Discovery in Nonparametric Regression through Compositional
  Kernel Search
Structure Discovery in Nonparametric Regression through Compositional Kernel Search
David Duvenaud
J. Lloyd
Roger C. Grosse
J. Tenenbaum
Zoubin Ghahramani
97
511
0
20 Feb 2013
Linear Latent Force Models using Gaussian Processes
Linear Latent Force Models using Gaussian Processes
Mauricio A. Alvarez
D. Luengo
Neil D. Lawrence
100
124
0
13 Jul 2011
Kernels for Vector-Valued Functions: a Review
Kernels for Vector-Valued Functions: a Review
Mauricio A. Alvarez
Lorenzo Rosasco
Neil D. Lawrence
GP
218
930
0
30 Jun 2011
1