Numerical simulation of large-scale dynamical systems plays a fundamental role in studying
a wide range of complex physical phenomena; however, the inherent large-scale nature of …

One of the main approaches to model reduction of both linear and nonlinear dynamical
systems is by means of interpolation. Data-driven model reduction constitutes a special …

This work presents a nonintrusive projection-based model reduction approach for full
models based on time-dependent partial differential equations. Projection-based model …

In this work we present a Reduced Order Model which is specifically designed to deal with
turbulent flows in a finite volume setting. The method used to build the reduced order model …

Scientific Machine Learning (SciML) and Artificial Intelligence (AI) will have broad use and
transformative effects across the Department of Energy. Accordingly, the January 2018 Basic …

This work presents a non-intrusive model reduction method to learn low-dimensional
models of dynamical systems with non-polynomial nonlinear terms that are spatially local …

Dynamical systems are at the core of computational models for a wide range of complex
phenomena and, as a consequence, the simulation of dynamical systems has become a …

Model reduction is ubiquitous in computational science and engineering. It plays a key role
in making computationally tractable outer-loop applications that require simulating systems …

Besides circadian rhythms, oscillations cycling with a 12 hr period exist. However, the
prevalence, origin, regulation, and function of mammalian 12 hr rhythms remain elusive …

Estimating statistics of model outputs with the Monte Carlo method often requires a large
number of model evaluations. This leads to long runtimes if the model is expensive to …