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.