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

Deep neural networks (DNNs) training can be difficult due to vanishing and exploding
gradients during weight optimization through backpropagation. To address this problem, we …

Large-scale cyber-physical systems require that control policies are distributed, that is, that
they only rely on local real-time measurements and communication with neighboring agents …

Aiming at the limitation of Hamiltonian neural network in high-dimensional data processing
with non-observable physical quantity system, an embedded Hamiltonian dynamic …

Las tecnologías de simulación se han convertido en una herramienta útil para modelizar
muchos sistemas en una amplia variedad de disciplinas, desde las ciencias sociales a las …