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Found 5 papers, 3 papers with code
A Thermodynamics-informed Active Learning Approach to Perception and Reasoning about Fluids
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.
Physics perception in sloshing scenes with guaranteed thermodynamic consistency
Physics perception very often faces the problem that only limited data or partial measurements on the scene are available.
MORPH-DSLAM: Model Order Reduction for PHysics-based Deformable SLAM
We propose a new methodology to estimate the 3D displacement field of deformable objects from video sequences using standard monocular cameras.
Deep learning of thermodynamics-aware reduced-order models from data
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.
Structure-preserving 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).
