From a functional standpoint, classic robots are not at all similar to biological systems. If
compared with rigid robots, animals' bodies look overly redundant, imprecise, and weak …

Thermodynamics could be seen as an expression of physics at a high epistemic level. As
such, its potential as an inductive bias to help machine learning procedures attain accurate …

We discuss the modelling framework of port-Hamiltonian descriptor systems and their use in
numerical simulation and control. The structure is ideal for automated network-based …

We develop inductive biases for the machine learning of complex physical systems based
on the port-Hamiltonian formalism. To satisfy by construction the principles of …

This paper presents a structure-preserving spatial discretization method for distributed
parameter port-Hamiltonian systems. The class of considered systems are hyperbolic …

In this paper we study the representation of partial differential equations (PDEs) as abstract
differential-algebraic equations (DAEs) with dissipative Hamiltonian structure (adHDAEs) …

We investigate an energy-based formulation of the two-field poroelasticity model and the
related multiple-network model as they appear in geosciences or medical applications. We …

In this two-parts paper, we present a systematic procedure to extend the known Hamiltonian
model of ideal inviscid fluid flow on Riemannian manifolds in terms of Lie–Poisson …

This paper presents a state of the art on port-Hamiltonian formulations for the modeling and
numerical simulation of open fluid systems. This literature review, with the help of more than …

Part I of this paper presented a systematic derivation of the Stokes–Dirac structure
underlying the port-Hamiltonian model of ideal fluid flow on Riemannian manifolds. Starting …