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Design of experiments for the calibration of history-dependent models
  via deep reinforcement learning and an enhanced Kalman filter

Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter

27 September 2022
Ruben Villarreal
Nikolaos N. Vlassis
Nhon N. Phan
Tommie A. Catanach
Reese E. Jones
N. Trask
S. Kramer
WaiChing Sun
    OffRL
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Papers citing "Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter"

3 / 3 papers shown
Title
Towards scientific machine learning for granular material simulations -- challenges and opportunities
Towards scientific machine learning for granular material simulations -- challenges and opportunities
Marc Fransen
Andreas Fürst
D. Tunuguntla
Daniel N. Wilke
Benedikt Alkin
...
Takayuki Shuku
WaiChing Sun
T. Weinhart
Dongwei Ye
Hongyang Cheng
AI4CE
26
0
0
01 Apr 2025
A review on data-driven constitutive laws for solids
A review on data-driven constitutive laws for solids
J. Fuhg
G. A. Padmanabha
N. Bouklas
B. Bahmani
WaiChing Sun
Nikolaos N. Vlassis
Moritz Flaschel
P. Carrara
L. Lorenzis
AI4CE
AILaw
21
31
0
06 May 2024
Uncertainty Quantification of Graph Convolution Neural Network Models of
  Evolving Processes
Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes
J. Hauth
C. Safta
Xun Huan
Ravi G. Patel
Reese E. Jones
BDL
UQCV
16
2
0
17 Feb 2024
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