The past few years have witnessed an increased interest in learning Hamiltonian dynamics
in deep learning frameworks. As an inductive bias based on physical laws, Hamiltonian …

Pseudo-Hamiltonian neural networks (PHNN) were recently introduced for learning
dynamical systems that can be modelled by ordinary differential equations. In this paper, we …

A complete structure-preserving learning scheme for single-input/single-output (SISO) linear
port-Hamiltonian systems is proposed. The construction is based on the solution, when …

Hybrid machine learning based on Hamiltonian formulations has recently been successfully
demonstrated for simple mechanical systems, both energy conserving and not energy …

Neural networks have gained much interest because of their effectiveness in many
applications. However, their mathematical properties are generally not well understood. If …

In this paper, we propose a three-layer distributed simulation network architecture, which
consists of a distributed virtual simulation network, a perception and control subnetwork, and …

We introduce the mean inverse integrator (MII), a novel approach to increase the accuracy
when training neural networks to approximate vector fields of dynamical systems from noisy …

Neural networks are the state-of-the-art for many approximation tasks in high-dimensional
spaces, as supported by an abundance of experimental evidence. However, we still need a …

We present and analyze a framework for designing symplectic neural networks (SympNets)
based on geometric integrators for Hamiltonian differential equations. The SympNets are …

This thesis is concerned with the modeling of nonlinear components and circuits for
simulations in audio applications. Our goal is to propose models that are sufficiently …