In uncertainty quantification, variance-based global sensitivity analysi...
Quadratization of polynomial and nonpolynomial systems of ordinary
diffe...
Forward simulation-based uncertainty quantification that studies the
dis...
We propose novel methods for Conditional Value-at-Risk (CVaR) estimation...
Digital twin models allow us to continuously assess the possible risk of...
Operator inference learns low-dimensional dynamical-system models with
p...
Reliable, risk-averse design of complex engineering systems with optimiz...
This work presents a non-intrusive model reduction method to learn
low-d...
We present Lift Learn, a physics-informed method for learning
low-di...
This paper presents a physics-based data-driven method to learn predicti...
We present a balanced truncation model reduction approach for a class of...
This paper develops a multifidelity method that enables estimation of fa...
This paper presents a structure-exploiting nonlinear model reduction met...