Search Results for author:
Found 5 papers, 5 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.
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.
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.
Backward error analysis for variational discretisations of partial differential equations
Therefore, we study symmetric solutions of discretized partial differential equations that arise from a discrete variational principle.
Numerical Analysis Numerical Analysis 65D30, 70H25, 70H50, 35A15, 35B06, 35C07, 37K58
