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 …

In this paper, we introduce a modular deep neural network (DNN) framework for data-driven
reduced order modeling of dynamical systems relevant to fluid flows. We propose various …

We investigate the optimal model reduction problem for large-scale quadratic-bilinear (QB)
control systems. Our contributions are threefold. First, we discuss the variational analysis …

In the present work, we introduce a novel approach to enhance the precision of reduced
order models by exploiting a multi-fidelity perspective and DeepONets. Reduced models …

A computational framework is proposed for efficiently solving multidisciplinary analysis and
optimization (MDAO) problems in a relatively high-dimensional design parameter space. It …

In this work we develop a scalable computational framework for the solution of PDE-
constrained optimal control problems under high-dimensional uncertainty. Specifically, we …

In the present paper we propose reduced order methods as a reliable strategy to efficiently
solve parametrized optimal control problems governed by shallow waters equations in a …

In this paper, we show how to efficiently achieve thermal cloaking from a computational
standpoint in several virtual scenarios by controlling a distribution of active heat sources. We …

In this paper, we present Sorotoki, an open-source toolkit in MATLAB that offers a
comprehensive suite of tools for the design, modeling, and control of soft robots. The …

We propose an incremental algorithm to compute the proper orthogonal decomposition
(POD) of simulation data for a partial differential equation. Specifically, we modify an …