Coarse-graining Hamiltonian systems using WSINDy

DA Messenger, JW Burby, DM Bortz - Scientific Reports, 2024 - nature.com
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 …

Lie group forced variational integrator networks for learning and control of robot systems

V Duruisseaux, TP Duong, M Leok… - … for Dynamics and …, 2023 - proceedings.mlr.press
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 …

Latent space dynamics learning for stiff collisional-radiative models

X **e, Q Tang, X Tang - Machine Learning: Science and …, 2024 - iopscience.iop.org
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 …

Projected Neural Differential Equations for Learning Constrained Dynamics

A White, A Büttner, M Gelbrecht, V Duruisseaux… - arxiv preprint arxiv …, 2024 - arxiv.org
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 …

[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 …