An information field theory approach to Bayesian state and parameter estimation in dynamical systems
From MaRDI portal
Publication:6560717
DOI10.1016/j.jcp.2024.113139MaRDI QIDQ6560717
Publication date: 23 June 2024
Published in: Journal of Computational Physics (Search for Journal in Brave)
variational inferenceBayesian system identificationBayesian state estimationmodel-form uncertaintyinformation field theoryphysics-informed functional prior
Parametric inference (62Fxx) Artificial intelligence (68Txx) Probabilistic methods, stochastic differential equations (65Cxx)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
- B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
- Multi-fidelity Bayesian neural networks: algorithms and applications
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Sequential Monte Carlo Methods in Practice
- Sequential Monte Carlo Methods for Dynamic Systems
- Filtering via Simulation: Auxiliary Particle Filters
- Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
- SMC2: An Efficient Algorithm for Sequential Analysis of State Space Models
- On Information and Sufficiency
- A Stochastic Approximation Method
- Field Dynamics Inference for Local and Causal Interactions
- Physics-informed information field theory for modeling physical systems with uncertainty quantification
This page was built for publication: An information field theory approach to Bayesian state and parameter estimation in dynamical systems