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Found 6 papers, 1 papers with code
Variational Bayesian surrogate modelling with application to robust design optimisation
The assumed prior probability density of the surrogate is a Gaussian process.
Stochastic PDE representation of random fields for large-scale Gaussian process regression and statistical finite element analysis
We use the SPDE representation to develop a scalable framework for large-scale statistical finite element analysis and Gaussian process (GP) regression on complex geometries.
Random Grid Neural Processes for Parametric Partial Differential Equations
We introduce a new class of spatially stochastic physics and data informed deep latent models for parametric partial differential equations (PDEs) which operate through scalable variational neural processes.
Fully probabilistic deep models for forward and inverse problems in parametric PDEs
In the posited probabilistic model, both the forward and inverse maps are approximated as Gaussian distributions with a mean and covariance parameterized by deep neural networks.
Computational modelling and data-driven homogenisation of knitted membranes
The trained GPR model encodes the nonlinearities and anisotropies present in the microscale and serves as a material model for the membrane response of the macroscale shell.
The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions
From the outset, we postulate a data-generating model which additively decomposes data into a finite element, a model misspecification and a noise component.
Methodology Numerical Analysis Numerical Analysis
