Physics-Informed Machine Learning Model-Based Corrector for Deformation-Based Fluid Control

Physics-informed ML corrector for fluid-simulation VFX rendering.

A system includes a hardware processor, a machine learning (ML) model-based corrector trained to predict a deformation of a velocity field, and a system memory storing software code. The hardware processor is configured to execute the software code to receive a deformation template and a deformed velocity field produced based on the deformation template, predict, using the ML model-based corrector based on the deformation template and the deformed velocity field, a correction to the deformed velocity field, and correct the deformed velocity field, using the correction, to provide a corrected velocity field. In some implementations, the hardware processor is further configured to execute the software code to advect the corrected velocity field to provide a density field of a corrected simulation of a deformation of a fluid or a viscoelastic material, and produce, using the density field, the corrected simulation.

BACKGROUND

Controlling fluid simulations is notoriously difficult due to its computational cost, as well as the fact that user control inputs can cause unphysical motion. Conventionally, artists have relied on force-based fluid control techniques that use artificial force fields that can be computed by optimization or through heuristics. Optimization methods match specific objectives at an undesirably high computational cost, while heuristics provide a solution that typically does not satisfy target keyframes. Both conventional approaches define objectives by computing differences between a simulated density field and a target density field at a given frame. When simulated and objective density fields do not overlap, e.g., if the target field undergoes extreme deformations, these methods cannot provide meaningful gradients for the optimization or heuristics computation, and artificially computed force fields will not be able to properly guide simulated deformations.

Alternatively, fluids can be controlled by direct manipulation of pre-simulated fluid data. For instance, volumetric flow data can be deformed with the underlying deformation grid, and various fluid scenes may be stitched or sculpted for resizing. While these techniques offer some level of post-processing functionality, they rely on computationally expensive optimizations or re-simulations. Thus, there is a need in the art for a faster and more computationally efficient solution for accurately simula