Coarse-Graining Hamiltonian Systems Using WSINDy
- URL: http://arxiv.org/abs/2310.05879v2
- Date: Thu, 30 Nov 2023 04:10:19 GMT
- Title: Coarse-Graining Hamiltonian Systems Using WSINDy
- Authors: Daniel A. Messenger, Joshua W. Burby, David M. Bortz
- Abstract summary: We show that WSINDy can successfully identify a reduced Hamiltonian system in the presence of large intrinsics.
WSINDy naturally preserves the Hamiltonian structure by restricting to a trial basis of Hamiltonian vector fields.
We also provide a contribution to averaging theory by proving that first-order averaging at the level of vector fields preserves Hamiltonian structure in nearly-periodic Hamiltonian systems.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The Weak-form Sparse Identification of Nonlinear Dynamics algorithm (WSINDy)
has been demonstrated to offer coarse-graining capabilities in the context of
interacting particle systems (https://doi.org/10.1016/j.physd.2022.133406). In
this work we extend this capability to the problem of coarse-graining
Hamiltonian dynamics which possess approximate symmetries associated with
timescale separation. Such approximate symmetries often lead to the existence
of a Hamiltonian system of reduced dimension that may be used to efficiently
capture the dynamics of the symmetry-invariant dependent variables. Deriving
such reduced systems, or approximating them numerically, is an ongoing
challenge. We demonstrate that WSINDy can successfully identify this reduced
Hamiltonian system in the presence of large intrinsic perturbations while
remaining robust to extrinsic noise. This is significant in part due to the
nontrivial means by which such systems are derived analytically. WSINDy also
naturally preserves the Hamiltonian structure by restricting to a trial basis
of Hamiltonian vector fields. The methodology is computational efficient, often
requiring only a single trajectory to learn the global reduced Hamiltonian, and
avoiding forward solves in the learning process. Using nearly-periodic
Hamiltonian systems as a prototypical class of systems with approximate
symmetries, we show that WSINDy robustly identifies the correct leading-order
system, with dimension reduced by at least two, upon observation of the
relevant degrees of freedom. We also provide a contribution to averaging theory
by proving that first-order averaging at the level of vector fields preserves
Hamiltonian structure in nearly-periodic Hamiltonian systems. We provide
physically relevant examples, namely coupled oscillator dynamics, the
H\'enon-Heiles system for stellar motion within a galaxy, and the dynamics of
charged particles.
Related papers
- Learning Generalized Hamiltonians using fully Symplectic Mappings [0.32985979395737786]
Hamiltonian systems have the important property of being conservative, that is, energy is conserved throughout the evolution.
In particular Hamiltonian Neural Networks have emerged as a mechanism to incorporate structural inductive bias into the NN model.
We show that symplectic schemes are robust to noise and provide a good approximation of the system Hamiltonian when the state variables are sampled from a noisy observation.
arXiv Detail & Related papers (2024-09-17T12:45:49Z) - Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems [77.88054335119074]
We use FNOs to model the evolution of random quantum spin systems.
We apply FNOs to a compact set of Hamiltonian observables instead of the entire $2n$ quantum wavefunction.
arXiv Detail & Related papers (2024-09-05T07:18:09Z) - Data-Driven Identification of Quadratic Representations for Nonlinear
Hamiltonian Systems using Weakly Symplectic Liftings [8.540823673172403]
This work is based on a lifting hypothesis, which posits that nonlinear Hamiltonian systems can be written as nonlinear systems with cubic Hamiltonians.
We propose a methodology to learn quadratic dynamical systems, enforcing the Hamiltonian structure in combination with a weakly-enforced symplectic auto-encoder.
arXiv Detail & Related papers (2023-08-02T11:26:33Z) - Robust Hamiltonian Engineering for Interacting Qudit Systems [50.591267188664666]
We develop a formalism for the robust dynamical decoupling and Hamiltonian engineering of strongly interacting qudit systems.
We experimentally demonstrate these techniques in a strongly-interacting, disordered ensemble of spin-1 nitrogen-vacancy centers.
arXiv Detail & Related papers (2023-05-16T19:12:41Z) - Capturing dynamical correlations using implicit neural representations [85.66456606776552]
We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data.
In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
arXiv Detail & Related papers (2023-04-08T07:55:36Z) - Hamiltonian Neural Networks with Automatic Symmetry Detection [0.0]
Hamiltonian neural networks (HNN) have been introduced to incorporate prior physical knowledge.
We enhance HNN with a Lie algebra framework to detect and embed symmetries in the neural network.
arXiv Detail & Related papers (2023-01-19T07:34:57Z) - Decimation technique for open quantum systems: a case study with
driven-dissipative bosonic chains [62.997667081978825]
Unavoidable coupling of quantum systems to external degrees of freedom leads to dissipative (non-unitary) dynamics.
We introduce a method to deal with these systems based on the calculation of (dissipative) lattice Green's function.
We illustrate the power of this method with several examples of driven-dissipative bosonic chains of increasing complexity.
arXiv Detail & Related papers (2022-02-15T19:00:09Z) - Learning Hamiltonians of constrained mechanical systems [0.0]
Hamiltonian systems are an elegant and compact formalism in classical mechanics.
We propose new approaches for the accurate approximation of the Hamiltonian function of constrained mechanical systems.
arXiv Detail & Related papers (2022-01-31T14:03:17Z) - Exact solutions of interacting dissipative systems via weak symmetries [77.34726150561087]
We analytically diagonalize the Liouvillian of a class Markovian dissipative systems with arbitrary strong interactions or nonlinearity.
This enables an exact description of the full dynamics and dissipative spectrum.
Our method is applicable to a variety of other systems, and could provide a powerful new tool for the study of complex driven-dissipative quantum systems.
arXiv Detail & Related papers (2021-09-27T17:45:42Z) - Stoquasticity in circuit QED [78.980148137396]
We show that scalable sign-problem free path integral Monte Carlo simulations can typically be performed for such systems.
We corroborate the recent finding that an effective, non-stoquastic qubit Hamiltonian can emerge in a system of capacitively coupled flux qubits.
arXiv Detail & Related papers (2020-11-02T16:41:28Z) - Nonseparable Symplectic Neural Networks [23.77058934710737]
We propose a novel neural network architecture, Nonseparable Symplectic Neural Networks (NSSNNs)
NSSNNs uncover and embed the symplectic structure of a nonseparable Hamiltonian system from limited observation data.
We show the unique computational merits of our approach to yield long-term, accurate, and robust predictions for large-scale Hamiltonian systems.
arXiv Detail & Related papers (2020-10-23T19:50:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.