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20082026
most citedPhysics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data

1.1k citations · 1.4k across the 43 of their papers we have counts for

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2 papers · 1 filter

math-ph2008★ 72 cited

A multi-resolution, non-parametric, Bayesian framework for identification of spatially-varying model parameters

P. S. Koutsourelakis

This paper proposes a hierarchical, multi-resolution framework for the identification of model parameters and their spatially variability from noisy measurements of the response or…

stat.CO2008★ 1 cited

Uncertainty quantification in complex systems using approximate solvers

Phaedon-Stelios Koutsourelakis

This paper proposes a novel uncertainty quantification framework for computationally demanding systems characterized by a large vector of non-Gaussian uncertainties. It combines st…