What's Next? Predicting Hamiltonian Dynamics from Discrete Observations of a Vector Field

Zi-Yu Khoo, Delong Zhang, Stéphane Bressan
12/14/2023
cs.LGnlin.CD

No confident code implementation found for this paper.

Abstract

We present several methods for predicting the dynamics of Hamiltonian systems from discrete observations of their vector field. Each method is either informed or uninformed of the Hamiltonian property. We empirically and comparatively evaluate the methods and observe that information that the system is Hamiltonian can be effectively informed, and that different methods strike different trade-offs between efficiency and effectiveness for different dynamical systems.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit

81% match2026

Data-Driven Response Regime Exploration and Identification for Dynamical Systems

81% match2023

Physics-informed Discovery of State Variables in Second-Order and Hamiltonian Systems

80% match2024

Research Program: Theory of Learning in Dynamical Systems

80% match2025

PSI-SINDy: Post-Selection Inference for Sparse Identification of Nonlinear Dynamics

80% match1 repo562026

Combining Slow and Fast: Complementary Filtering for Dynamics Learning

79% match2023

Cite this paper

@article{khoo2023whats,
  title  = {What's Next? Predicting Hamiltonian Dynamics from Discrete Observations of a Vector Field},
  author = {Zi-Yu Khoo and Delong Zhang and Stéphane Bressan},
  year   = {2023},
  eprint = {2312.08944},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2312.08944v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2312.08944.svg)](https://codeofpaper.com/paper/2312.08944)

Discussion