Summary
The quasipotential serves as a natural extension of the potential function in nongradient systems, and it enables predicting the maximum likelihood transition paths, transition rates and expected exit times of metastable dynamical states. In this work, the combination of a neural network and a sparse regression identification algorithm is employed to perform the symbolic discovery of quasipotential functions. The data-driven approach is benchmarked in an archetypal system with know exact quasipotential and applied to the dynamics of a nanomechanical resonator.
