System identification is the art and science of building mathematical models of dynamic
systems from observed input–output data. It can be seen as the interface between the real …

A model-constrained adaptive sampling methodology is proposed for the reduction of large-
scale systems with high-dimensional parametric input spaces. Our model reduction method …

Learning predictive models from observations using deep neural networks (DNNs) is a
promising new approach to many real-world planning and control problems. However …

The residential and commercial building sector is known to use around 40% of the total end-
use energy and, hence, is considered to be the largest energy consumer sector in the world …

Solution of statistical inverse problems via the frequentist or Bayesian approaches described
in earlier chapters can be a computationally intensive endeavor, particularly when faced …

We present a composition rule involving quasiconvex functions that generalizes the
classical composition rule for convex functions. This rule complements well-known rules for …

This paper presents a parameterized reduction technique for highly nonlinear systems. In
our approach, we first approximate the nonlinear system with a convex combination of …

Abstract System identification is the art and science of building mathematical models of
dynamic systems from observed input-output data. It can be seen as the interface between …

In this paper we present a parameterized reduction technique for non-linear systems. Our
approach combines an existing non-parameterized trajectory piecewise linear method for …

Many critical electronic design automation (EDA) problems suffer from the curse of
dimensionality, ie, the very fast-scaling computational burden produced by large number of …