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A particle-based workflow for approximating the time-dependent law of finite-volume discretizations of the Dean–Kawasaki model is proposed, which accurately captures the site-wise correlations and other observables of the true model.
Abstract. We propose a particle-based workflow for approximating the time-dependent law of finite-volume discretizations of the Dean–Kawasaki model. After discretization, the state is a nonnegative vector whose total mass is conserved by the finite-volume scheme, and it is therefore supported on a probability simplex....
An efficient numerical approach for compressing a high-dimensional discrete distribution function into a non-negative tensor train (NTT) format and observing that the proposed NTT fitting procedure exhibits drastically faster convergence than an alternative multiplicative update method that has been previously proposed...
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