Interpretable Conservation Law Estimation by Deriving the Symmetries of
Dynamics from Trained Deep Neural Networks

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Interpretable Conservation Law Estimation by Deriving the Symmetries of Dynamics from Trained Deep Neural Networks

31 December 2019

Yoh-ichi Mototake

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Papers citing "Interpretable Conservation Law Estimation by Deriving the Symmetries of Dynamics from Trained Deep Neural Networks"

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Title
Interpretable Machine Learning in Physics: A Review Sebastian Johann Wetzel Seungwoong Ha Raban Iten Miriam Klopotek Ziming Liu AI4CE 160 2 0 30 Mar 2025
Analysis of the Identifying Regulation with Adversarial Surrogates Algorithm Ron Teichner Ron Meir Michael Margaliot 62 2 0 05 May 2024
Discovering New Interpretable Conservation Laws as Sparse Invariants Ziming Liu Patrick Obin Sturm Saketh Bharadwaj Sam Silva M. Tegmark 41 5 0 31 May 2023
Learning quantum symmetries with interactive quantum-classical variational algorithms Jonathan Z. Lu R. A. Bravo Kaiying Hou Gebremedhin A. Dagnew S. Yelin K. Najafi 61 3 0 23 Jun 2022
AI Poincaré 2.0: Machine Learning Conservation Laws from Differential Equations Ziming Liu Varun Madhavan M. Tegmark PINN 98 28 0 23 Mar 2022
Convolutional Autoencoders for Reduced-Order Modeling Sreeram Venkat Ralph C. Smith C. Kelley 32 0 0 27 Aug 2021
Learning Hamiltonian dynamics by reservoir computer Han Zhang Huawei Fan Liang Wang Xingang Wang 22 3 0 24 Apr 2021
Discovering conservation laws from trajectories via machine learning Seungwoong Ha Hawoong Jeong PINN AI4CE 60 10 0 08 Feb 2021