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20172019
most citedA physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime

40 citations · 74 across the 4 of their papers we have counts for

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Showing 2017Show all

2 papers · 1 filter

stat.ML2017★ 29 cited

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…

stat.ML2017★ 5 cited

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…