Oct 10, 2023 · Abstract page for arXiv paper 2310.06241: A Bayesian framework for discovering interpretable Lagrangian of dynamical systems from data.
A probabilistic framework is introduced for discovering interpretable Lagrangian. •. It is applicable to both discrete and continuous systems.
A Bayesian framework for discovering interpretable Lagrangian of dynamical systems from data ; Journal: Mechanical Systems and Signal Processing, 2024, p. 111737.
Feb 9, 2023 · In this article, we propose a novel data-driven machine-learning algorithm to automate the discovery of interpretable Lagrangian from data. The ...
Missing: Bayesian | Show results with:Bayesian
Jul 18, 2024 · Our work titled "A Bayesian Framework for Discovering Interpretable Lagrangian of Dynamical Systems from Data" has been accepted in the ...
We introduce Discovery of Dynamical Systems via Moving Horizon Optimization (DySMHO), a scalable machine learning framework for identifying governing laws in ...
A sparse Bayesian framework for discovering interpretable nonlinear stochastic dynamical systems with Gaussian white noise ... We here propose a novel data-driven ...
Learning and predicting the dynamics of physical systems requires a profound understanding of the underlying physical laws.
A Bayesian framework for discovering interpretable Lagrangian of dynamical systems from data ... Discovering interpretable Lagrangian of dynamical systems from ...
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