57 citations · 84 across the 6 of their papers we have counts for
8 papers
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…
A Thermodynamics-informed Active Learning Approach to Perception and Reasoning about Fluids
Beatriz Moya, Alberto Badias, David Gonzalez +2
Learning and reasoning about physical phenomena is still a challenge in robotics development, and computational sciences play a capital role in the search for accurate methods able…
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…
Physics perception in sloshing scenes with guaranteed thermodynamic consistency
Beatriz Moya, Alberto Badias, David Gonzalez +2
Physics perception very often faces the problem that only limited data or partial measurements on the scene are available. In this work, we propose a strategy to learn the full sta…
MORPH-DSLAM: Model Order Reduction for PHysics-based Deformable SLAM
Alberto Badias, Iciar Alfaro, David Gonzalez +2
We propose a new methodology to estimate the 3D displacement field of deformable objects from video sequences using standard monocular cameras. We solve in real time the complete (…