Forward simulation-based uncertainty quantification that studies the
dis...
Least squares regression is a ubiquitous tool for building emulators (a....
Approximate solutions to large least squares problems can be computed
ef...
High-order interaction events are common in real-world applications. Lea...
Inverse problems and, in particular, inferring unknown or latent paramet...
Learning neural ODEs often requires solving very stiff ODE systems, prim...
Deep learning using neural networks is an effective technique for genera...
In simulation sciences, it is desirable to capture the real-world proble...
Proper orthogonal decomposition (POD) allows reduced-order modeling of
c...
Orthogonal polynomials of several variables have a vector-valued three-t...
A Gaussian process (GP) is a powerful and widely used regression techniq...
We consider the Bayesian approach to the linear Gaussian inference probl...
Physics-informed neural networks (PINNs) as a means of discretizing part...
Model Agnostic Meta-Learning (MAML) is widely used to find a good
initia...
Approximating a function with a finite series, e.g., involving polynomia...
Multifidelity simulation methodologies are often used in an attempt to
j...
Finite element simulations have been used to solve various partial
diffe...
Density tracking by quadrature (DTQ) is a numerical procedure for comput...
Multifidelity methods are widely used for statistical estimation of
quan...
Stochastic Galerkin formulations of the two-dimensional shallow water sy...
The paper is concerned with classic kernel interpolation methods, in add...
Multifidelity approximation is an important technique in scientific
comp...
As the use of spectral/hp element methods, and high-order finite element...
Associated to a finite measure on the real line with finite moments are
...
A weakly admissible mesh (WAM) on a continuum real-valued domain is a
se...
One of the major challenges for low-rank multi-fidelity (MF) approaches ...
The task of repeatedly solving parametrized partial differential equatio...
One of the open problems in the field of forward uncertainty quantificat...
Approximations of functions with finite data often do not respect certai...
A stochastic Galerkin formulation for a stochastic system of balanced or...
Performing uncertainty quantification (UQ) and sensitivity analysis (SA)...
We first propose a novel criterion that guarantees that an s-sparse sign...
We present a new hyperviscosity formulation for stabilizing radial basis...
We propose a novel approach to allocating resources for expensive simula...
We present a methodical procedure for topology optimization under uncert...
We present a new method for the solution of PDEs on manifolds M⊂R^d of c...
We present a systematic computational framework for generating positive
...