Decoupling Density Dynamics: A Neural Operator Framework for Adaptive Multi-Fluid Interactions

Abstract

The dynamic interface prediction of multi-density fluids presents a fundamental challenge across computational fluid dynamics and graphics, rooted in nonlinear momentum transfer. We present Density-Conditioned Dynamic Convolution, a novel neural operator framework that establishes differentiable density-dynamics mapping through decoupled operator response. The core theoretical advancement lies in continuously adaptive neighborhood kernels that transform local density distributions into tunable filters, enabling unified representation from homogeneous media to multi-phase fluid. Experiments demonstrate autonomous evolution of physically consistent interface separation patterns in density contrast scenarios, including cocktail and bidirectional hourglass flow. Quantitative evaluation shows improved computational efficiency compared to a SPH method and qualitatively plausible interface dynamics, with a larger time step size.