We introduce Lagrangian Flow Networks (LFlows) for modeling fluid densities and velocities continuously in space and time. The proposed LFlows satisfy by construction the continuity equation, a PDE describing mass conservation in its differentiable form. Our model is based on the insight that solutions to the continuity equation can be expressed as time-dependent density transformations via differentiable and invertible maps. This follows from classical theory of existence and uniqueness of Lagrangian flows for smooth vector fields. Hence, we model fluid densities by transforming a base density with parameterized diffeomorphisms conditioned on time. The key benefit compared to methods relying on Neural-ODE or PINNs is that the analytic expression of the velocity is always consistent with the density. Furthermore, there is no need for expensive numerical solvers, nor for enforcing the PDE with penalty methods. Lagrangian Flow Networks show improved predictive accuracy on synthetic density modeling tasks compared to competing models in both 2D and 3D. We conclude with a real-world application of modeling bird migration based on sparse weather radar measurements.

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