Search Results for author:
Found 7 papers, 4 papers with code
Hamiltonian Neural Networks with Automatic Symmetry Detection
Recently, Hamiltonian neural networks (HNN) have been introduced to incorporate prior physical knowledge when learning the dynamical equations of Hamiltonian systems.
Multi-objective low-thrust spacecraft trajectory design using reachability analysis
One of the fundamental problems in spacecraft trajectory design is finding the optimal transfer trajectory that minimizes the propellant consumption and transfer time simultaneously.
The Past Does Matter: Correlation of Subsequent States in Trajectory Predictions of Gaussian Process Models
Computing the distribution of trajectories from a Gaussian Process model of a dynamical system is an important challenge in utilizing such models.
Discrete Lagrangian Neural Networks with Automatic Symmetry Discovery
By one of the most fundamental principles in physics, a dynamical system will exhibit those motions which extremise an action functional.
Verification of safety critical control policies using kernel methods
Hamilton-Jacobi reachability methods for safety-critical control have been well studied, but the safety guarantees derived rely on the accuracy of the numerical computation.
Efficient time stepping for numerical integration using reinforcement learning
While the classical schemes apply very generally and are highly efficient on regular systems, they can behave sub-optimal when an inefficient step rejection mechanism is triggered by structurally complex systems such as chaotic systems.
Learning ODE Models with Qualitative Structure Using Gaussian Processes
Recent advances in learning techniques have enabled the modelling of dynamical systems for scientific and engineering applications directly from data.
