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Found 5 papers, 1 papers with code
Coarse-Graining Hamiltonian Systems Using WSINDy
In this work we extend this capability to the problem of coarse-graining Hamiltonian dynamics which possess approximate symmetries associated with timescale separation.
Approximation of nearly-periodic symplectic maps via structure-preserving neural networks
This neural network architecture, which we call symplectic gyroceptron, ensures that the resulting surrogate map is nearly-periodic and symplectic, and that it gives rise to a discrete-time adiabatic invariant and a long-time stability.
Stabilized Neural Ordinary Differential Equations for Long-Time Forecasting of Dynamical Systems
We present a data-driven modeling method that accurately captures shocks and chaotic dynamics by proposing a novel architecture, stabilized neural ordinary differential equation (ODE).
High-Precision Inversion of Dynamic Radiography Using Hydrodynamic Features
Radiography is often used to probe complex, evolving density fields in dynamic systems and in so doing gain insight into the underlying physics.
Integrability, Normal Forms, and Magnetic Axis Coordinates
In this paper, the existence of exact, smooth normal forms for integrable magnetic fields near elliptic and hyperbolic magnetic axes is established.
Mathematical Physics Mathematical Physics 78A25, 37J35 (Primary), 53D10, 70H06 (Secondary)
