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Found 4 papers, 1 papers with code
A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime
The automated construction of coarse-grained models represents a pivotal component in computer simulation of physical systems and is a key enabler in various analysis and design tasks related to uncertainty quantification.
Small Data Image Classification
Uncertainty Quantification
+1
A data-driven model order reduction approach for Stokes flow through random porous media
Direct numerical simulation of Stokes flow through an impermeable, rigid body matrix by finite elements requires meshes fine enough to resolve the pore-size scale and is thus a computationally expensive task.
Bayesian model and dimension reduction for uncertainty propagation: applications in random media
Both components are represented with latent variables in a probabilistic graphical model and are simultaneously trained using Stochastic Variational Inference methods.
Probabilistic Reduced-Order Modeling for Stochastic Partial Differential Equations
We discuss a Bayesian formulation to coarse-graining (CG) of PDEs where the coefficients (e. g. material parameters) exhibit random, fine scale variability.
