78 research outputs found
Hybrid deterministic stochastic systems with microscopic look-ahead dynamics
We study the impact of stochastic mechanisms on a coupled hybrid system consisting of a general advection-diffusion-reaction partial differential equation and a spatially distributed stochastic lattice noise model. The stochastic dynamics include both spin-flip and spin-exchange type interparticle interactions. Furthermore, we consider a new, asymmetric, single exclusion pro- cess, studied elsewhere in the context of traffic flow modeling, with an one-sided interaction potential which imposes advective trends on the stochastic dynamics. This look-ahead stochastic mechanism is responsible for rich nonlinear behavior in solutions. Our approach relies heavily on first deriving approximate differential mesoscopic equations. These approximations become exact either in the long range, Kac interaction partial differential equation case, or, given sufficient time separation con- ditions, between the partial differential equation and the stochastic model giving rise to a stochastic averaging partial differential equation. Although these approximations can in some cases be crude, they can still give a first indication, via linearized stability analysis, of the interesting regimes for the stochastic model. Motivated by this linearized stability analysis we choose particular regimes where interacting nonlinear stochastic waves are responsible for phenomena such as random switching, convective instability, and metastability, all driven by stochasticity. Numerical kinetic Monte Carlo simulations of the coarse grained hybrid system are implemented to assist in producing solutions and understanding their behavior
Coupled coarse graining and Markov Chain Monte Carlo for lattice systems
We propose an efficient Markov Chain Monte Carlo method for sampling
equilibrium distributions for stochastic lattice models, capable of handling
correctly long and short-range particle interactions. The proposed method is a
Metropolis-type algorithm with the proposal probability transition matrix based
on the coarse-grained approximating measures introduced in a series of works of
M. Katsoulakis, A. Majda, D. Vlachos and P. Plechac, L. Rey-Bellet and
D.Tsagkarogiannis,. We prove that the proposed algorithm reduces the
computational cost due to energy differences and has comparable mixing
properties with the classical microscopic Metropolis algorithm, controlled by
the level of coarsening and reconstruction procedure. The properties and
effectiveness of the algorithm are demonstrated with an exactly solvable
example of a one dimensional Ising-type model, comparing efficiency of the
single spin-flip Metropolis dynamics and the proposed coupled Metropolis
algorithm.Comment: 20 pages, 4 figure
Coarse-graining schemes for stochastic lattice systems with short and long-range interactions
We develop coarse-graining schemes for stochastic many-particle microscopic
models with competing short- and long-range interactions on a d-dimensional
lattice. We focus on the coarse-graining of equilibrium Gibbs states and using
cluster expansions we analyze the corresponding renormalization group map. We
quantify the approximation properties of the coarse-grained terms arising from
different types of interactions and present a hierarchy of correction terms. We
derive semi-analytical numerical schemes that are accompanied with a posteriori
error estimates for coarse-grained lattice systems with short and long-range
interactions.Comment: 31 pages, 2 figure
Mutual Information for Explainable Deep Learning of Multiscale Systems
Timely completion of design cycles for complex systems ranging from consumer
electronics to hypersonic vehicles relies on rapid simulation-based
prototyping. The latter typically involves high-dimensional spaces of possibly
correlated control variables (CVs) and quantities of interest (QoIs) with
non-Gaussian and possibly multimodal distributions. We develop a
model-agnostic, moment-independent global sensitivity analysis (GSA) that
relies on differential mutual information to rank the effects of CVs on QoIs.
The data requirements of this information-theoretic approach to GSA are met by
replacing computationally intensive components of the physics-based model with
a deep neural network surrogate. Subsequently, the GSA is used to explain the
network predictions, and the surrogate is deployed to close design loops.
Viewed as an uncertainty quantification method for interrogating the surrogate,
this framework is compatible with a wide variety of black-box models. We
demonstrate that the surrogate-driven mutual information GSA provides useful
and distinguishable rankings on two applications of interest in energy storage.
Consequently, our information-theoretic GSA provides an "outer loop" for
accelerated product design by identifying the most and least sensitive input
directions and performing subsequent optimization over appropriately reduced
parameter subspaces.Comment: 27 pages, 8 figures. Added additional example
Time--Splitting Schemes and Measure Source Terms for a Quasilinear Relaxing System
Several singular limits are investigated in the context of a
system arising for instance in the modeling of chromatographic processes. In
particular, we focus on the case where the relaxation term and a
projection operator are concentrated on a discrete lattice by means of Dirac
measures. This formulation allows to study more easily some time-splitting
numerical schemes
A relative entropy rate method for path space sensitivity analysis of stationary complex stochastic dynamics
We propose a new sensitivity analysis methodology for complex stochastic
dynamics based on the Relative Entropy Rate. The method becomes computationally
feasible at the stationary regime of the process and involves the calculation
of suitable observables in path space for the Relative Entropy Rate and the
corresponding Fisher Information Matrix. The stationary regime is crucial for
stochastic dynamics and here allows us to address the sensitivity analysis of
complex systems, including examples of processes with complex landscapes that
exhibit metastability, non-reversible systems from a statistical mechanics
perspective, and high-dimensional, spatially distributed models. All these
systems exhibit, typically non-gaussian stationary probability distributions,
while in the case of high-dimensionality, histograms are impossible to
construct directly. Our proposed methods bypass these challenges relying on the
direct Monte Carlo simulation of rigorously derived observables for the
Relative Entropy Rate and Fisher Information in path space rather than on the
stationary probability distribution itself. We demonstrate the capabilities of
the proposed methodology by focusing here on two classes of problems: (a)
Langevin particle systems with either reversible (gradient) or non-reversible
(non-gradient) forcing, highlighting the ability of the method to carry out
sensitivity analysis in non-equilibrium systems; and, (b) spatially extended
Kinetic Monte Carlo models, showing that the method can handle high-dimensional
problems
Large deviations for the macroscopic motion of an interface
We study the most probable way an interface moves on a macroscopic scale from an initial to a final position within a fixed time in the context of large deviations for a stochastic microscopic lattice system of Ising spins with Kac interaction evolving in time according to Glauber (non-conservative) dynamics. Such interfaces separate two stable phases of a ferromagnetic system and in the macroscopic scale are represented by sharp transitions. We derive quantitative estimates for the upper and the lower bound of the cost functional that penalizes all possible deviations and obtain explicit error terms which are valid also in the macroscopic scale. Furthermore, using the result of a companion paper about the minimizers of this cost functional for the macroscopic motion of the interface in a fixed time, we prove that the probability of such events can concentrate on nucleations should the transition happen fast enough
Coarse-grained stochastic processes for microscopic lattice systems
Diverse scientific disciplines ranging from materials science to catalysis to biomolecular dynamics to climate modeling involve nonlinear interactions across a large range of physically significant length scales. Here a class of coarse-grained stochastic processes and corresponding Monte Carlo simulation methods, describing computationally feasible mesoscopic length scales, are derived directly from microscopic lattice systems. It is demonstrated below that the coarse-grained stochastic models can capture large-scale structures while retaining significant microscopic information. The requirement of detailed balance is used as a systematic design principle to guarantee correct noise fluctuations for the coarse-grained model. The coarse-grained stochastic algorithms provide large computational savings without increasing programming complexity or computer time per executive event compared to microscopic Monte Carlo simulations
- …