Dynamical systems see widespread use in natural sciences like physics, biology, and
chemistry, as well as engineering disciplines such as circuit analysis, computational fluid …

We propose a methodology for the identification of nonlinear state–space models from
input/output data using machine-learning techniques based on autoencoders and neural …

Abstract Using Artificial Neural Networks (ANN) for nonlinear system identification has
proven to be a promising approach, but despite of all recent research efforts, many practical …

In this paper, we consider the identification problem for nonlinear state-space models with
skewed measurement noises. The generalized hyperbolic skew Student'st (GHSkewt) …

Deep learning is a topic of considerable current interest. The availability of massive data
collections and powerful software resources has led to an impressive amount of results in …

Surrogate models play a vital role in overcoming the computational challenge in designing
and analyzing nonlinear dynamic systems, especially in the presence of uncertainty. This …

In this paper, we propose a probabilistic physics-guided framework, termed Physics-guided
Deep Markov Model (PgDMM). The framework targets the inference of the characteristics …

We use Koopman theory to develop a data-driven nonlinear model reduction and
identification strategy for multiple-input multiple-output (MIMO) input-affine dynamical …

In this work, we propose an operator learning framework for accelerating nonlinear adaptive
con-trol. We define three operator mappings in adaptive control-the parameter identifier …

This paper proposes a sparse Bayesian treatment of deep neural networks (DNNs) for
system identification. Although DNNs show impressive approximation ability in various …