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An information field theory approach to Bayesian state and parameter
  estimation in dynamical systems

An information field theory approach to Bayesian state and parameter estimation in dynamical systems

3 June 2023
Kairui Hao
Ilias Bilionis
ArXivPDFHTML

Papers citing "An information field theory approach to Bayesian state and parameter estimation in dynamical systems"

2 / 2 papers shown
Title
NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework
NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework
O. Hennigh
S. Narasimhan
M. A. Nabian
Akshay Subramaniam
Kaustubh Tangsali
M. Rietmann
J. Ferrandis
Wonmin Byeon
Z. Fang
S. Choudhry
PINN
AI4CE
91
126
0
14 Dec 2020
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and
  Inverse PDE Problems with Noisy Data
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data
Liu Yang
Xuhui Meng
George Karniadakis
PINN
170
756
0
13 Mar 2020
1