3,883 research outputs found
Probabilistic Reduced-Order Modeling for Stochastic Partial Differential Equations
We discuss a Bayesian formulation to coarse-graining (CG) of PDEs where the
coefficients (e.g. material parameters) exhibit random, fine scale variability.
The direct solution to such problems requires grids that are small enough to
resolve this fine scale variability which unavoidably requires the repeated
solution of very large systems of algebraic equations. We establish a
physically inspired, data-driven coarse-grained model which learns a low-
dimensional set of microstructural features that are predictive of the
fine-grained model (FG) response. Once learned, those features provide a sharp
distribution over the coarse scale effec- tive coefficients of the PDE that are
most suitable for prediction of the fine scale model output. This ultimately
allows to replace the computationally expensive FG by a generative proba-
bilistic model based on evaluating the much cheaper CG several times. Sparsity
enforcing pri- ors further increase predictive efficiency and reveal
microstructural features that are important in predicting the FG response.
Moreover, the model yields probabilistic rather than single-point predictions,
which enables the quantification of the unavoidable epistemic uncertainty that
is present due to the information loss that occurs during the coarse-graining
process
Coarse-Graining Auto-Encoders for Molecular Dynamics
Molecular dynamics simulations provide theoretical insight into the
microscopic behavior of materials in condensed phase and, as a predictive tool,
enable computational design of new compounds. However, because of the large
temporal and spatial scales involved in thermodynamic and kinetic phenomena in
materials, atomistic simulations are often computationally unfeasible.
Coarse-graining methods allow simulating larger systems, by reducing the
dimensionality of the simulation, and propagating longer timesteps, by
averaging out fast motions. Coarse-graining involves two coupled learning
problems; defining the mapping from an all-atom to a reduced representation,
and the parametrization of a Hamiltonian over coarse-grained coordinates.
Multiple statistical mechanics approaches have addressed the latter, but the
former is generally a hand-tuned process based on chemical intuition. Here we
present Autograin, an optimization framework based on auto-encoders to learn
both tasks simultaneously. Autograin is trained to learn the optimal mapping
between all-atom and reduced representation, using the reconstruction loss to
facilitate the learning of coarse-grained variables. In addition, a
force-matching method is applied to variationally determine the coarse-grained
potential energy function. This procedure is tested on a number of model
systems including single-molecule and bulk-phase periodic simulations.Comment: 8 pages, 6 figure
Persistent accelerations disentangle Lagrangian turbulence
Particles in turbulence frequently encounter extreme accelerations between
extended periods of quiescence. The occurrence of extreme events is closely
related to the intermittent spatial distribution of intense flow structures
such as vorticity filaments. This mixed history of flow conditions leads to
very complex particle statistics with a pronounced scale dependence, which
presents one of the major challenges on the way to a non-equilibrium
statistical mechanics of turbulence. Here, we introduce the notion of
persistent Lagrangian acceleration, quantified by the squared particle
acceleration coarse-grained over a viscous time scale. Conditioning Lagrangian
particle data from simulations on this coarse-grained acceleration, we find
remarkably simple, close-to-Gaussian statistics for a range of Reynolds
numbers. This opens the possibility to decompose the complex particle
statistics into much simpler sub-ensembles. Based on this observation, we
develop a comprehensive theoretical framework for Lagrangian single-particle
statistics that captures the acceleration, velocity increments as well as
single-particle dispersion
A First Principle Approach to Rescale the Dynamics of Simulated Coarse-Grained Macromolecular Liquids
We present a detailed derivation and testing of our approach to rescale the
dynamics of mesoscale simulations of coarse-grained polymer melts (I. Y.
Lyubimov et al. J. Chem. Phys. \textbf{132}, 11876, 2010). Starting from the
first-principle Liouville equation and applying the Mori-Zwanzig projection
operator technique, we derive the Generalized Langevin Equations (GLE) for the
coarse-grained representations of the liquid. The chosen slow variables in the
projection operators define the length scale of coarse graining. Each polymer
is represented at two levels of coarse-graining: monomeric as a bead-and-spring
model and molecular as a soft-colloid. In the long-time regime where the
center-of-mass follows Brownian motion and the internal dynamics is completely
relaxed, the two descriptions must be equivalent. By enforcing this formal
relation we derive from the GLEs the analytical rescaling factors to be applied
to dynamical data in the coarse-grained representation to recover the monomeric
description. Change in entropy and change in friction are the two corrections
to be accounted for to compensate the effects of coarse-graining on the polymer
dynamics. The solution of the memory functions in the coarse-grained
representations provides the dynamical rescaling of the friction coefficient.
The calculation of the internal degrees of freedom provides the correction of
the change in entropy due to coarse-graining. The resulting rescaling formalism
is a function of the coarse-grained model and thermodynamic parameters of the
system simulated. The rescaled dynamics obtained from mesoscale simulations of
polyethylene, represented as soft colloidal particles, by applying our
rescaling approach shows a good agreement with data of translational diffusion
measured experimentally and from simulations. The proposed method is used to
predict self-diffusion coefficients of new polyethylene samples.Comment: 21 pages, 6 figures, 6 tables. Submitted to Phys. Rev.
Asymptotically safe f(R)-gravity coupled to matter I: the polynomial case
We use the functional renormalization group equation for the effective
average action to study the non-Gaussian renormalization group fixed points
(NGFPs) arising within the framework of f(R)-gravity minimally coupled to an
arbitrary number of scalar, Dirac, and vector fields. Based on this setting we
provide comprehensible estimates which gravity-matter systems give rise to
NGFPs suitable for rendering the theory asymptotically safe. The analysis
employs an exponential split of the metric fluctuations and retains a
7-parameter family of coarse-graining operators allowing the inclusion of
non-trivial endomorphisms in the regularization procedure. For vanishing
endomorphisms, it is established that gravity coupled to the matter content of
the standard model of particle physics and many beyond the standard model
extensions exhibit NGFPs whose properties are strikingly similar to the case of
pure gravity: there are two UV-relevant directions and the position and
critical exponents converge rapidly when higher powers of the scalar curvature
are included. Conversely, none of the phenomenologically interesting
gravity-matter systems exhibits a stable NGFP when a Type II coarse graining
operator is employed. Our analysis resolves this tension by demonstrating that
the NGFPs seen in the two settings belong to different universality classes.Comment: 49 pages, 5 figure
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