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Found 6 papers, 0 papers with code
Neural Operator induced Gaussian Process framework for probabilistic solution of parametric partial differential equations
The study of neural operators has paved the way for the development of efficient approaches for solving partial differential equations (PDEs) compared with traditional methods.
Discovering stochastic partial differential equations from limited data using variational Bayes inference
We propose a novel framework for discovering Stochastic Partial Differential Equations (SPDEs) from data.
A Bayesian Framework for learning governing Partial Differential Equation from Data
To accelerate the overall process, a variational Bayes-based approach for discovering partial differential equations is proposed.
MAntRA: A framework for model agnostic reliability analysis
A two-stage approach is adopted: in the first stage, an efficient variational Bayesian equation discovery algorithm is developed to determine the governing physics of an underlying stochastic differential equation (SDE) from measured output data.
On spike-and-slab priors for Bayesian equation discovery of nonlinear dynamical systems via sparse linear regression
The problem of discovering governing equations is cast as that of selecting relevant variables from a predetermined dictionary of basis variables and solved via sparse Bayesian linear regression.
Model Selection
Variable Selection
Methodology
Systems and Control
Systems and Control
Applications
A Gaussian process latent force model for joint input-state estimation in linear structural systems
A novel methodology using Gaussian process latent force models is proposed to tackle the problem in a stochastic setting.
