We propose heavy ball neural ordinary differential equations (HBNODEs), leveraging the
continuous limit of the classical momentum accelerated gradient descent, to improve neural …

Abstract Neural Ordinary Differential Equations (NODEs) are a new class of models that
transform data continuously through infinite-depth architectures. The continuous nature of …

Bolted joints play a more and more important role in the structure with lighter weight and
heavier load. This paper aims to provide an overview of different experimental approaches …

This article introduces recurrent equilibrium networks (RENs), a new class of nonlinear
dynamical models for applications in machine learning, system identification, and control …

This tutorial paper provides an introduction to recently developed tools for machine learning,
especially learning dynamical systems (system identification), with stability and robustness …

This paper proposes a new lap-joint configuration, the so-called Orion beam. The new setup
is composed of two thin beams connected by three bolted joints with contact patches on …

This paper introduces recurrent equilibrium networks (RENs), a new class of nonlinear
dynamical models for applications in machine learning and system identification. The new …

This paper presents Mechanistic Neural Networks, a neural network design for machine
learning applications in the sciences. It incorporates a new Mechanistic Block in standard …

A known challenge when building nonlinear models from data is to limit the size of the
model in terms of the number of parameters. Especially for complex nonlinear systems …

Frequency response function (FRF) identification is often used as a basis for control systems
design and as a starting point for subsequent parametric system identification. The aim of …