ABSTRACT Traditional physics‐based fluid simulations typically rely on manual modeling and incremental adjustments to achieve desired effects, which can limit objectivity and generalizability to new scenarios. To address these challenges, we propose a novel neural fluid simulator that integrates visual priors from 2D image sequences with physically constrained continuous convolution. Specifically, we extract and refine point clouds from image sequences, then infer the kinetic properties of the fluid. We introduce an energy‐based physical constraint and incorporate it into a continuous convolution solver. By iteratively optimizing these inputs to enforce physical laws—particularly incompressibility—the solver produces accurate fluid motion predictions. Our approach uniquely combines visual data and physical constraints, enhancing the realism and accuracy while providing stronger generalization of fluid simulations.
Du et al. (2026) studied this question.