Lagrangian Flow Networks for Conservation Laws

Fabricio Arend Torres, Marcello Massimo Negri, Marco Inversi, Jonathan Aellen, Volker Roth

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Keywords: Physics-informed Neural Network, Fluid Dynamics, Conservation Law, Partial Differential Equation, Conditional Normalizing Flows, Bird-Migration

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TL;DR: An approach for modeling fluid densities and velocities continuously in space and time while satisfying the continuity equation by construction.

Abstract:

We introduce Lagrangian Flow Networks (LFlows) for modeling fluid densities and velocities continuously in space and time.
By construction, the proposed LFlows satisfy the continuity equation,
a PDE describing mass conservation in its differential 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 the 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 numerical ODE solvers or PINNs is that the analytic expression of the velocity is always consistent with changes in density.
Furthermore, we require neither expensive numerical solvers, nor additional penalties to enforce the PDE.
LFlows show higher predictive accuracy in density modeling tasks compared to competing models in 2D and 3D,
while being computationally efficient.
As a real-world application, we model bird migration based on sparse weather radar measurements.

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Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

Submission Number: 6062