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cs.LG2026
Parameter-Level Attribution of Symmetry in Trained Networks Though Parameter-Wise Functional Sensitivity
Alan Muriithi, Vedanta Thapar, Torben Berndt
When a network has learned a function with a known symmetry, can that symmetry be moved through the parametrisation---is there a motion in parameter space realising the group actio…
cs.LG2026
Machine Learning Hamiltonian Dynamical Systems with Sparse and Noisy Data
Vedanta Thapar, Abhinav Gupta
Machine learning has become a powerful tool for discovering governing laws of dynamical systems from data. However, most existing approaches degrade severely when observations are…
cs.LG2023
Applications of Machine Learning to Modelling and Analysing Dynamical Systems
Vedanta Thapar
We explore the use of Physics Informed Neural Networks to analyse nonlinear Hamiltonian Dynamical Systems with a first integral of motion. In this work, we propose an architecture…