Application of machine learning technique for a fast forecast of
  aggregation kinetics in space-inhomogeneous systems
M. A. Larchenko R. R. Zagidullin V. V. Palyulin N. V. Brilliantov
Modeling of aggregation processes in space-inhomogeneous systems is extremely
numerically challenging since complicated aggregation equations -- Smoluchowski
equations are to be solved at each space point along with the computation of
particle propagation. Low rank approximation for the aggregation kernels can
significantly speed up the solution of Smoluchowski equations, while particle
propagation could be done in parallel. Yet the simulations with many aggregate
sizes remain quite resource-demanding. Here, we explore the way to reduce the
amount of direct computations with the use of modern machine learning (ML)
techniques. Namely, we propose to replace the actual numerical solution of the
Smoluchowki equations with the respective density transformations learned with
the application of the conditional normalising flow. We demonstrate that the ML
predictions for the space distribution of aggregates and their size
distribution requires drastically less computation time and agrees fairly well
with the results of direct numerical simulations. Such an opportunity of a
quick forecast of space-dependent particle size distribution could be important
in practice, especially for the online prediction and visualisation of
pollution processes, providing a tool with a reasonable tradeoff between the
prediction accuracy and the computational time.