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Coarse-graining Hamiltonian systems using WSINDy
Weak form equation learning and surrogate modeling has proven to be computationally
efficient and robust to measurement noise in a wide range of applications including ODE …
efficient and robust to measurement noise in a wide range of applications including ODE …
Lie group forced variational integrator networks for learning and control of robot systems
Incorporating prior knowledge of physics laws and structural properties of dynamical
systems into the design of deep learning architectures has proven to be a powerful …
systems into the design of deep learning architectures has proven to be a powerful …
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This article extends the study of dynamical properties of the symmetric McMillan map,
emphasizing its utility in understanding and modeling complex nonlinear systems. Although …
emphasizing its utility in understanding and modeling complex nonlinear systems. Although …
Latent space dynamics learning for stiff collisional-radiative models
In this work, we propose a data-driven method to discover the latent space and learn the
corresponding latent dynamics for a collisional-radiative (CR) model in radiative plasma …
corresponding latent dynamics for a collisional-radiative (CR) model in radiative plasma …
Projected Neural Differential Equations for Learning Constrained Dynamics
Neural differential equations offer a powerful approach for learning dynamics from data.
However, they do not impose known constraints that should be obeyed by the learned …
However, they do not impose known constraints that should be obeyed by the learned …
[KNIHA][B] Symplectic Numerical Integration at the Service of Accelerated Optimization and Structure-Preserving Dynamics Learning
V Duruisseaux - 2023 - search.proquest.com
Symplectic numerical integrators for Hamiltonian systems form the paramount class of
geometric numerical integrators, and have been very well investigated in the past forty …
geometric numerical integrators, and have been very well investigated in the past forty …