Machine Learning methods are emerging as faster and efficient alternatives to numerical simulation techniques. The field of Scientific Computing has started adopting these data-driven approaches to faithfully model physical phenomena using scattered, noisy observations from coarse-grained grid-based simulations.
In this paper, we investigate data-driven modeling of Bose-Einstein Condensates (BECs). In particular, we use Gaussian Processes (GPs) to model the ground state wave function of BECs as a function of scattering parameters from the dimensionless Gross Pitaveskii Equation (GPE).
Experimental results illustrate the ability of GPs to accurately reproduce ground state wave functions using a limited number of data points from simulations. Consistent performance across different configurations of BECs, namely Scalar and Vectorial BECs generated under different potentials, including harmonic, double well and optical lattice potentials pronounce the versatility of our method. Comparison with existing data-driven models indicates that our model achieves similar accuracy with only a small fraction (1/50th) of data points used by existing methods, in addition to modeling uncertainty from data. When used as a simulator post-training, our model generates ground state wave functions 36× faster than Trotter Suzuki, a numerical approximation technique that uses Imaginary time evolution.
Our method is quite general; with minor changes, it can be applied to similar quantum many-body problems.