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Deep neural network enabled corrective source term approach to hybrid
  analysis and modeling

Deep neural network enabled corrective source term approach to hybrid analysis and modeling

24 May 2021
Sindre Stenen Blakseth
Adil Rasheed
T. Kvamsdal
Omer San
ArXivPDFHTML

Papers citing "Deep neural network enabled corrective source term approach to hybrid analysis and modeling"

10 / 10 papers shown
Title
A Bi-fidelity DeepONet Approach for Modeling Uncertain and Degrading
  Hysteretic Systems
A Bi-fidelity DeepONet Approach for Modeling Uncertain and Degrading Hysteretic Systems
Subhayan De
P. Brewick
35
0
0
25 Apr 2023
Artificial intelligence-driven digital twin of a modern house
  demonstrated in virtual reality
Artificial intelligence-driven digital twin of a modern house demonstrated in virtual reality
Elias Mohammed Elfarri
Adil Rasheed
Omer San
29
16
0
14 Dec 2022
A novel corrective-source term approach to modeling unknown physics in
  aluminum extraction process
A novel corrective-source term approach to modeling unknown physics in aluminum extraction process
Haakon Robinson
E. Lundby
Adil Rasheed
J. Gravdahl
28
5
0
22 Sep 2022
Sparse deep neural networks for modeling aluminum electrolysis dynamics
Sparse deep neural networks for modeling aluminum electrolysis dynamics
E. Lundby
Adil Rasheed
I. Halvorsen
J. Gravdahl
29
14
0
13 Sep 2022
Combining physics-based and data-driven techniques for reliable hybrid
  analysis and modeling using the corrective source term approach
Combining physics-based and data-driven techniques for reliable hybrid analysis and modeling using the corrective source term approach
Sindre Stenen Blakseth
Adil Rasheed
T. Kvamsdal
Omer San
AI4CE
28
31
0
07 Jun 2022
Physics guided neural networks for modelling of non-linear dynamics
Physics guided neural networks for modelling of non-linear dynamics
Haakon Robinson
Suraj Pawar
Adil Rasheed
Omer San
PINN
AI4TS
AI4CE
32
47
0
13 May 2022
Supplementation of deep neural networks with simplified physics-based
  features to increase model prediction accuracy
Supplementation of deep neural networks with simplified physics-based features to increase model prediction accuracy
Nicholus R. Clinkinbeard
Nicole N. Hashemi
PINN
AI4CE
41
0
0
14 Apr 2022
Bi-fidelity Modeling of Uncertain and Partially Unknown Systems using
  DeepONets
Bi-fidelity Modeling of Uncertain and Partially Unknown Systems using DeepONets
Subhayan De
Matthew J. Reynolds
M. Hassanaly
Ryan N. King
Alireza Doostan
AI4CE
41
37
0
03 Apr 2022
Physics guided machine learning using simplified theories
Physics guided machine learning using simplified theories
Suraj Pawar
Omer San
Burak Aksoylu
Adil Rasheed
T. Kvamsdal
PINN
AI4CE
105
106
0
18 Dec 2020
A Probabilistic Graphical Model Foundation for Enabling Predictive
  Digital Twins at Scale
A Probabilistic Graphical Model Foundation for Enabling Predictive Digital Twins at Scale
Michael G. Kapteyn
Jacob V. R. Pretorius
Karen E. Willcox
39
215
0
10 Dec 2020
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