171 citations · 898 across the 46 of their papers we have counts for
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Hessian-based sampling for high-dimensional model reduction
Peng Chen, Omar Ghattas
In this work we develop a Hessian-based sampling method for the construction of goal-oriented reduced order models with high-dimensional parameter inputs. Model reduction is known…
Sparse polynomial approximations for affine parametric saddle point problems
Peng Chen, Omar Ghattas
In this work we study convergence properties of sparse polynomial approximations for a class of affine parametric saddle point problems. Such problems can be found in many computat…
A comparative study of structural similarity and regularization for joint inverse problems governed by PDEs
Benjamin Crestel, Georg Stadler, Omar Ghattas
Joint inversion refers to the simultaneous inference of multiple parameter fields from observations of systems governed by single or multiple forward models. In many cases these pa…
Scalable matrix-free adaptive product-convolution approximation for locally translation-invariant operators
Nick Alger, Vishwas Rao, Aaron Myers +2
We present an adaptive grid matrix-free operator approximation scheme based on a "product-convolution" interpolation of convolution operators. This scheme is appropriate for operat…
Taylor approximation and variance reduction for PDE-constrained optimal control under uncertainty
Peng Chen, Umberto Villa, Omar Ghattas
In this work we develop a scalable computational framework for the solution of PDE-constrained optimal control under high-dimensional uncertainty. Specifically, we consider a mean-…