This is the official repository for the paper MinRNNs for Lagrangian-Based Simulations of Transient Flow Problems.
This repository contains the code for the surrogate model of 2D Lagrangian fluid and multimaterial simulations.
The repository also includes the appendix of the paper in the docs directory.
ICCS camera ready version of the paper is available here.
Qualitative results: minLSTM/minGRU train 350–400% faster than LSTM/GRU, matching or surpassing their accuracy.
Snapshots from groundtruth data for multi-material multi-phase simulation. Top-left: homogeneous liquid; top-middle: snow; top-right: rope and jelly ball; bottom-left: liquid and snow; bottom-middle: rope and snow; bottom-right: rope and liquid.
Motivated by the need for faster yet accurate surrogate modeling of continuum simulations, we investigate whether the recently proposed minimal recurrent networks (minLSTM and minGRU [1] (also available at https://github.com/BorealisAI/minRNNs)) can benefit particle-based fluid and soft-solid simulations. To our knowledge, this is the first work applying these minimal RNNs to Lagrangian data from 2D continuum simulation, including single-phase fluids and multi-material interactions. We embed minLSTM and minGRU in an MLP-based encoder–decoder and compare them against (i) a classical LSTM, and (ii) an MLP baseline with no recurrent core. Where prior studies of minRNNs focused on simpler time-series tasks, our results show that minLSTM and minGRU remain highly effective in these physics-driven settings: they train approximately 350–400% faster than the standard LSTM or GRU, while matching—and often surpassing—their accuracy. Thus, for particle-based continuum simulations, minimal recurrent architectures offer a superior trade-off between computational overhead and predictive performance, thereby advancing real-time or high-fidelity simulation workflows in engineering and visual effects. We conclude that minimal RNNs are well-suited for surrogate modeling of fluid and soft-solid dynamics.