We show that the constructed networks are able to learn the physics of complex systems by parts, thus alleviating the burden associated to the experimental characterization and posterior learning process of this kind of systems.
The imminent impact of immersive technologies in society urges for active research in real-time and interactive physics simulation for virtual worlds to be realistic.
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 to provide explanations for past events and rigorous forecasts of future situations.
The present paper aims at analyzing the topological content of the complex trajectories that weeder-autonomous robots follow in operation.
Physics perception very often faces the problem that only limited data or partial measurements on the scene are available.
The concept of Hybrid Twin (HT) has recently received a growing interest thanks to the availability of powerful machine learning techniques.
We propose a new methodology to estimate the 3D displacement field of deformable objects from video sequences using standard monocular cameras.
We present an algorithm to learn the relevant latent variables of a large-scale discretized physical system and predict its time evolution using thermodynamically-consistent deep neural networks.
The method employs a minimum amount of data by enforcing the metriplectic structure of dissipative Hamiltonian systems in the form of the so-called General Equation for the Non-Equilibrium Reversible-Irreversible Coupling, GENERIC [M. Grmela and H. C Oettinger (1997).