40 citations · 74 across the 4 of their papers we have counts for
4 papers
A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime
Constantin Grigo, Phaedon-Stelios Koutsourelakis
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 ta…
A data-driven model order reduction approach for Stokes flow through random porous media
Constantin Grigo, Phaedon-Stelios Koutsourelakis
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 com…
Bayesian model and dimension reduction for uncertainty propagation: applications in random media
Constantin Grigo, Phaedon-Stelios Koutsourelakis
Well-established methods for the solution of stochastic partial differential equations (SPDEs) typically struggle in problems with high-dimensional inputs/outputs. Such difficultie…
Probabilistic Reduced-Order Modeling for Stochastic Partial Differential Equations
Constantin Grigo, Phaedon-Stelios Koutsourelakis
We discuss a Bayesian formulation to coarse-graining (CG) of PDEs where the coefficients (e.g. material parameters) exhibit random, fine scale variability. The direct solution to s…