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A Robust Learning Methodology for Uncertainty-aware Scientific Machine
  Learning models

A Robust Learning Methodology for Uncertainty-aware Scientific Machine Learning models

5 September 2022
Erbet Costa Almeida
C. Rebello
M. Fontana
L. Schnitman
Idelfonso B. R. Nogueira
ArXivPDFHTML

Papers citing "A Robust Learning Methodology for Uncertainty-aware Scientific Machine Learning models"

2 / 2 papers shown
Title
Digital Twin Framework for Optimal and Autonomous Decision-Making in
  Cyber-Physical Systems: Enhancing Reliability and Adaptability in the Oil and
  Gas Industry
Digital Twin Framework for Optimal and Autonomous Decision-Making in Cyber-Physical Systems: Enhancing Reliability and Adaptability in the Oil and Gas Industry
C. Rebello
Johannes Jäschkea
Idelfonso B. R. Nogueira
AI4CE
24
0
0
21 Nov 2023
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,145
0
06 Jun 2015
1