Reduced-Order Variational Deterministic-Particle-Based Scheme for Fokker-Planck Equations in Microscopic Polymer Dynamics

TL;DR

This paper introduces a model reduction technique using POD to accelerate the variational deterministic-particle-based scheme for Fokker-Planck equations in complex polymer fluid simulations, maintaining accuracy while significantly reducing computational cost.

Contribution

It develops a reduced-order model that integrates POD with VDS, enabling efficient 3D simulations of multi-bead polymers with minimal loss of accuracy.

Findings

Reduced model achieves about 6% relative error.

Computational time is reduced to about 6% of original.

Degrees of freedom are decreased to 0.1% of original.

Abstract

This study proposes an acceleration technique for the computational challenges in extending the variational deterministic-particle-based scheme (VDS) [Bao et al., Journal of Computational Physics 522 (2025) 113589] to 3D complex fluid simulations with multi-bead polymers. While the original VDS effectively captures configuration space dynamics for 2D dumbbell polymers, its direct extensions reveal critical scalability limitations. The growing configuration space dimensionality necessitates prohibitively large particle ensembles to maintain distributional accuracy, so its quadratic computational cost scaling impedes practical applications. In this paper, we develop a model reduction framework integrating proper orthogonal decomposition (POD) to speed up the computation of the VDS for microscopic Fokker-Planck equations. Numerical validation using bead-spring chain models in simple shear flow demonstrates that the computational efficiency of the reduced model increases systematically with molecular complexity. The reduced-order model introduces about $6\%$ relative error in predicting the dynamics while requiring only about $6\%$ of the original computational time for $4$-bead chain polymers, where the relative numerical error of the reference dynamics is about $5\% \sim 10\%$, and the degrees of freedom can be reduced significantly to about $0.1\%$ of the original model, which means the low-dimensional structure is found by POD. This establishes a practical pathway for multiscale and complex fluid simulations.