Within the framework of computational plasticity, recent advances show t...
For the numerical simulation of time-dependent problems, recent works su...
We develop inductive biases for the machine learning of complex physical...
The imminent impact of immersive technologies in society urges for activ...
Thermodynamics could be seen as an expression of physics at a high epist...
Learning and reasoning about physical phenomena is still a challenge in
...
In this paper we present a deep learning method to predict the temporal
...
The present paper aims at analyzing the topological content of the compl...
Physics perception very often faces the problem that only limited data o...
The concept of Hybrid Twin (HT) has recently received a growing interest...
Regressions created from experimental or simulated data enable the
const...
We propose a new methodology to estimate the 3D displacement field of
de...
We present an algorithm to learn the relevant latent variables of a
larg...
We develop a method to learn physical systems from data that employs
fee...