96 citations · 269 across the 8 of their papers we have counts for
4 papers · 1 filter
Port-metriplectic neural networks: thermodynamics-informed machine learning of complex physical systems
Quercus Hernández, Alberto Badías, Francisco Chinesta +1
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 thermodynamic…
Thermodynamics-informed graph neural networks
Quercus Hernández, Alberto Badías, Francisco Chinesta +1
In this paper we present a deep learning method to predict the temporal evolution of dissipative dynamic systems. We propose using both geometric and thermodynamic inductive biases…
Neural Network Layer Algebra: A Framework to Measure Capacity and Compression in Deep Learning
Alberto Badias, Ashis Banerjee
We present a new framework to measure the intrinsic properties of (deep) neural networks. While we focus on convolutional networks, our framework can be extrapolated to any network…
Structure-preserving neural networks
Quercus Hernández, Alberto Badias, David Gonzalez +2
We develop a method to learn physical systems from data that employs feedforward neural networks and whose predictions comply with the first and second principles of thermodynamics…