A data-driven reduced basis (RB) method for parametrized time-dependent problems is
proposed. This method requires the offline preparation of a database comprising the time …

An adaptive and efficient approach for constructing reduced‐order models (ROMs) that are
robust to changes in parameters is developed. The approach is based on a greedy sampling …

Multifidelity surrogates (MFS) combine low-fidelity models with few high-fidelity samples to
infer the response of the high-fidelity model for design optimization or uncertainty …

A machine learning–based framework for modeling the error introduced by surrogate
models of parameterized dynamical systems is proposed. The framework entails the use of …

This work proposes a machine-learning framework for modeling the error incurred by
approximate solutions to parameterized dynamical systems. In particular, we extend the …

A new reduced-order model based on trajectory piecewise quadratic (TPWQ)
approximations and proper orthogonal decomposition (POD) is introduced and applied for …

This work proposes a machine-learning framework for constructing statistical models of
errors incurred by approximate solutions to parameterized systems of nonlinear equations …

A novel error estimation method for the parametric non-intrusive reduced order model (P-
NIROM) based on machine learning is presented. This method relies on constructing a set of …

Computational inverse problems related to partial differential equations (PDEs) often contain
nuisance parameters that cannot be effectively identified but still need to be considered as …

This chapter deals with kernel methods as a special class of techniques for surrogate
modeling. Kernel methods have proven to be efficient in machine learning, pattern …