3 research outputs found
Learning fast, accurate, and stable closures of a kinetic theory of an active fluid
Important classes of active matter systems can be modeled using kinetic
theories. However, kinetic theories can be high dimensional and challenging to
simulate. Reduced-order representations based on tracking only low-order
moments of the kinetic model serve as an efficient alternative, but typically
require closure assumptions to model unrepresented higher-order moments. In
this study, we present a learning framework based on neural networks that
exploit rotational symmetries in the closure terms to learn accurate closure
models directly from kinetic simulations. The data-driven closures demonstrate
excellent a-priori predictions comparable to the state-of-the-art Bingham
closure. We provide a systematic comparison between different neural network
architectures and demonstrate that nonlocal effects can be safely ignored to
model the closure terms. We develop an active learning strategy that enables
accurate prediction of the closure terms across the entire parameter space
using a single neural network without the need for retraining. We also propose
a data-efficient training procedure based on time-stepping constraints and a
differentiable pseudo-spectral solver, which enables the learning of stable
closures suitable for a-posteriori inference. The coarse-grained simulations
equipped with data-driven closure models faithfully reproduce the mean velocity
statistics, scalar order parameters, and velocity power spectra observed in
simulations of the kinetic theory. Our differentiable framework also
facilitates the estimation of parameters in coarse-grained descriptions
conditioned on data
A fast Chebyshev method for the Bingham closure with application to active nematic suspensions
Continuum kinetic theories provide an important tool for the analysis and
simulation of particle suspensions. When those particles are anisotropic, the
addition of a particle orientation vector to the kinetic description yields a
dimensional theory which becomes intractable to simulate, especially in
three dimensions or near states where the particles are highly aligned.
Coarse-grained theories that track only moments of the particle distribution
functions provide a more efficient simulation framework, but require closure
assumptions. For the particular case where the particles are apolar, the
Bingham closure has been found to agree well with the underlying kinetic
theory; yet the closure is non-trivial to compute, requiring the solution of an
often nearly-singular nonlinear equation at every spatial discretization point
at every timestep. In this paper, we present a robust, accurate, and efficient
numerical scheme for evaluating the Bingham closure, with a controllable
error/efficiency tradeoff. To demonstrate the utility of the method, we carry
out high-resolution simulations of a coarse-grained continuum model for a
suspension of active particles in parameter regimes inaccessible to kinetic
theories. Analysis of these simulations reveals that inaccurately computing the
closure can act to effectively limit spatial resolution in the coarse-grained
fields. Pushing these simulations to the high spatial resolutions enabled by
our method reveals a coupling between vorticity and topological defects in the
suspension director field, as well as signatures of energy transfer between
scales in this active fluid model