skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Stochastic Hydrodynamics of Complex Fluids: Discretisation and Entropy Production
Many complex fluids can be described by continuum hydrodynamic field equations, to which noise must be added in order to capture thermal fluctuations. In almost all cases, the resulting coarse-grained stochastic partial differential equations carry a short-scale cutoff, which is also reflected in numerical discretisation schemes. We draw together our recent findings concerning the construction of such schemes and the interpretation of their continuum limits, focusing, for simplicity, on models with a purely diffusive scalar field, such as ‘Model B’ which describes phase separation in binary fluid mixtures. We address the requirement that the steady-state entropy production rate (EPR) must vanish for any stochastic hydrodynamic model in a thermal equilibrium. Only if this is achieved can the given discretisation scheme be relied upon to correctly calculate the nonvanishing EPR for ‘active field theories’ in which new terms are deliberately added to the fluctuating hydrodynamic equations that break detailed balance. To compute the correct probabilities of forward and time-reversed paths (whose ratio determines the EPR), we must make a careful treatment of so-called ‘spurious drift’ and other closely related terms that depend on the discretisation scheme. We show that such subtleties can arise not only in the temporal discretisation (as is well documented for stochastic ODEs with multiplicative noise) but also from spatial discretisation, even when noise is additive, as most active field theories assume. We then review how such noise can become multiplicative via off-diagonal couplings to additional fields that thermodynamically encode the underlying chemical processes responsible for activity. In this case, the spurious drift terms need careful accounting, not just to evaluate correctly the EPR but also to numerically implement the Langevin dynamics itself.  more » « less
Award ID(s):
2019745
PAR ID:
10416730
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Entropy
Volume:
24
Issue:
2
ISSN:
1099-4300
Page Range / eLocation ID:
254
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; (, IMA Journal of Numerical Analysis)
    null (Ed.)
    Abstract We propose some new mixed finite element methods for the time-dependent stochastic Stokes equations with multiplicative noise, which use the Helmholtz decomposition of the driving multiplicative noise. It is known (Langa, J. A., Real, J. & Simon, J. (2003) Existence and regularity of the pressure for the stochastic Navier--Stokes equations. Appl. Math. Optim., 48, 195--210) that the pressure solution has low regularity, which manifests in suboptimal convergence rates for well-known inf-sup stable mixed finite element methods in numerical simulations; see Feng X., & Qiu, H. (Analysis of fully discrete mixed finite element methods for time-dependent stochastic Stokes equations with multiplicative noise. arXiv:1905.03289v2 [math.NA]). We show that eliminating this gradient part from the noise in the numerical scheme leads to optimally convergent mixed finite element methods and that this conceptual idea may be used to retool numerical methods that are well known in the deterministic setting, including pressure stabilization methods, so that their optimal convergence properties can still be maintained in the stochastic setting. Computational experiments are also provided to validate the theoretical results and to illustrate the conceptual usefulness of the proposed numerical approach. 
    more » « less
  2. ; ; (, Quarterly Journal of the Royal Meteorological Society)
    Abstract Data assimilation methods often also employ the same discrete dynamical model used to evolve the state estimate in time to propagate an approximation of the state estimation error covariance matrix. Four‐dimensional variational methods, for instance, evolve the covariance matrix implicitly via discrete tangent linear dynamics. Ensemble methods, while not forming this matrix explicitly, approximate its evolution at low rank from the evolution of the ensemble members. Such approximate evolution schemes for the covariance matrix imply an approximate evolution of the estimation error variances along its diagonal. For states that satisfy the advection equation, the continuity equation, or related hyperbolic partial differential equations (PDEs), the estimation error variance itself satisfies a known PDE, so the accuracy of the various approximations to the variances implied by the discrete covariance propagation can be determined directly. Experiments conducted by the atmospheric chemical constituent data assimilation community have indicated that such approximate variance evolution can be highly inaccurate. Through careful analysis and simple numerical experiments, we show that such poor accuracy must be expected, due to the inherent nature of discrete covariance propagation, coupled with a special property of the continuum covariance dynamics for states governed by these types of hyperbolic PDE. The intuitive explanation for this inaccuracy is that discrete covariance propagation involves approximating diagonal elements of the covariance matrix with combinations of off‐diagonal elements, even when there is a discontinuity in the continuum covariance dynamics along the diagonal. Our analysis uncovers the resulting error terms that depend on the ratio of the grid spacing to the correlation length, and these terms become very large when correlation lengths begin to approach the grid scale, for instance, as gradients steepen near the diagonal. We show that inaccurate variance evolution can manifest itself as both spurious loss and gain of variance. 
    more » « less
  3. ; ; ; ; ; (, Proceedings of the National Academy of Sciences)
    Recent advances in high-resolution imaging techniques and particle-based simulation methods have enabled the precise microscopic characterization of collective dynamics in various biological and engineered active matter systems. In parallel, data-driven algorithms for learning interpretable continuum models have shown promising potential for the recovery of underlying partial differential equations (PDEs) from continuum simulation data. By contrast, learning macroscopic hydrodynamic equations for active matter directly from experiments or particle simulations remains a major challenge, especially when continuum models are not known a priori or analytic coarse graining fails, as often is the case for nondilute and heterogeneous systems. Here, we present a framework that leverages spectral basis representations and sparse regression algorithms to discover PDE models from microscopic simulation and experimental data, while incorporating the relevant physical symmetries. We illustrate the practical potential through a range of applications, from a chiral active particle model mimicking nonidentical swimming cells to recent microroller experiments and schooling fish. In all these cases, our scheme learns hydrodynamic equations that reproduce the self-organized collective dynamics observed in the simulations and experiments. This inference framework makes it possible to measure a large number of hydrodynamic parameters in parallel and directly from video data. 
    more » « less
  4. ; ; (, IMA Journal of Numerical Analysis)
    Abstract In this paper, a higher order time-discretization scheme is proposed, where the iterates approximate the solution of the stochastic semilinear wave equation driven by multiplicative noise with general drift and diffusion. We employ variational method for its error analysis and prove an improved convergence order of $$\frac 32$$ for the approximates of the solution. The core of the analysis is Hölder continuity in time and moment bounds for the solutions of the continuous and the discrete problem. Computational experiments are also presented. 
    more » « less
  5. (, Annual Review of Condensed Matter Physics)
    With the growing number of microscale devices from computer memory to microelectromechanical systems, such as lab-on-a-chip biosensors, and the increased ability to experimentally measure at the micro- and nanoscale, modeling systems with stochastic processes is a growing need across science. In particular, stochastic partial differential equations (SPDEs) naturally arise from continuum models—for example, a pillar magnet's magnetization or an elastic membrane's mechanical deflection. In this review, I seek to acquaint the reader with SPDEs from the point of view of numerically simulating their finite-difference approximations, without the rigorous mathematical details of assigning probability measures to the random field solutions. I stress that these simulations with spatially uncorrelated noise may not converge as the grid size goes to zero in the way that one expects from deterministic convergence of numerical schemes in two or more spatial dimensions. I then present some models with spatially correlated noise that maintain sampling of the physically relevant equilibrium distribution. Numerical simulations are presented to demonstrate the dynamics; the code is publicly available on GitHub. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email