27,550 research outputs found
Kinematic reduction of reaction-diffusion fronts with multiplicative noise: Derivation of stochastic sharp-interface equations
We study the dynamics of generic reaction-diffusion fronts, including pulses
and chemical waves, in the presence of multiplicative noise. We discuss the
connection between the reaction-diffusion Langevin-like field equations and the
kinematic (eikonal) description in terms of a stochastic moving-boundary or
sharp-interface approximation. We find that the effective noise is additive and
we relate its strength to the noise parameters in the original field equations,
to first order in noise strength, but including a partial resummation to all
orders which captures the singular dependence on the microscopic cutoff
associated to the spatial correlation of the noise. This dependence is
essential for a quantitative and qualitative understanding of fluctuating
fronts, affecting both scaling properties and nonuniversal quantities. Our
results predict phenomena such as the shift of the transition point between the
pushed and pulled regimes of front propagation, in terms of the noise
parameters, and the corresponding transition to a non-KPZ universality class.
We assess the quantitative validity of the results in several examples
including equilibrium fluctuations, kinetic roughening, and the noise-induced
pushed-pulled transition, which is predicted and observed for the first time.
The analytical predictions are successfully tested against rigorous results and
show excellent agreement with numerical simulations of reaction-diffusion field
equations with multiplicative noise.Comment: 17 pages, 6 figure
Analytical approximation to the multidimensional Fokker--Planck equation with steady state
The Fokker--Planck equation is a key ingredient of many models in physics,
and related subjects, and arises in a diverse array of settings. Analytical
solutions are limited to special cases, and resorting to numerical simulation
is often the only route available; in high dimensions, or for parametric
studies, this can become unwieldy. Using asymptotic techniques, that draw upon
the known Ornstein--Uhlenbeck (OU) case, we consider a mean-reverting system
and obtain its representation as a product of terms, representing short-term,
long-term, and medium-term behaviour. A further reduction yields a simple
explicit formula, both intuitive in terms of its physical origin and fast to
evaluate. We illustrate a breadth of cases, some of which are `far' from the OU
model, such as double-well potentials, and even then, perhaps surprisingly, the
approximation still gives very good results when compared with numerical
simulations. Both one- and two-dimensional examples are considered.Comment: Updated version as publishe
Controlled diffusion processes
This article gives an overview of the developments in controlled diffusion
processes, emphasizing key results regarding existence of optimal controls and
their characterization via dynamic programming for a variety of cost criteria
and structural assumptions. Stochastic maximum principle and control under
partial observations (equivalently, control of nonlinear filters) are also
discussed. Several other related topics are briefly sketched.Comment: Published at http://dx.doi.org/10.1214/154957805100000131 in the
Probability Surveys (http://www.i-journals.org/ps/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Variational approach for learning Markov processes from time series data
Inference, prediction and control of complex dynamical systems from time
series is important in many areas, including financial markets, power grid
management, climate and weather modeling, or molecular dynamics. The analysis
of such highly nonlinear dynamical systems is facilitated by the fact that we
can often find a (generally nonlinear) transformation of the system coordinates
to features in which the dynamics can be excellently approximated by a linear
Markovian model. Moreover, the large number of system variables often change
collectively on large time- and length-scales, facilitating a low-dimensional
analysis in feature space. In this paper, we introduce a variational approach
for Markov processes (VAMP) that allows us to find optimal feature mappings and
optimal Markovian models of the dynamics from given time series data. The key
insight is that the best linear model can be obtained from the top singular
components of the Koopman operator. This leads to the definition of a family of
score functions called VAMP-r which can be calculated from data, and can be
employed to optimize a Markovian model. In addition, based on the relationship
between the variational scores and approximation errors of Koopman operators,
we propose a new VAMP-E score, which can be applied to cross-validation for
hyper-parameter optimization and model selection in VAMP. VAMP is valid for
both reversible and nonreversible processes and for stationary and
non-stationary processes or realizations
Heat release by controlled continuous-time Markov jump processes
We derive the equations governing the protocols minimizing the heat released
by a continuous-time Markov jump process on a one-dimensional countable state
space during a transition between assigned initial and final probability
distributions in a finite time horizon. In particular, we identify the
hypotheses on the transition rates under which the optimal control strategy and
the probability distribution of the Markov jump problem obey a system of
differential equations of Hamilton-Bellman-Jacobi-type. As the state-space mesh
tends to zero, these equations converge to those satisfied by the diffusion
process minimizing the heat released in the Langevin formulation of the same
problem. We also show that in full analogy with the continuum case, heat
minimization is equivalent to entropy production minimization. Thus, our
results may be interpreted as a refined version of the second law of
thermodynamics.Comment: final version, section 2.1 revised, 26 pages, 3 figure
The instanton method and its numerical implementation in fluid mechanics
A precise characterization of structures occurring in turbulent fluid flows
at high Reynolds numbers is one of the last open problems of classical physics.
In this review we discuss recent developments related to the application of
instanton methods to turbulence. Instantons are saddle point configurations of
the underlying path integrals. They are equivalent to minimizers of the related
Freidlin-Wentzell action and known to be able to characterize rare events in
such systems. While there is an impressive body of work concerning their
analytical description, this review focuses on the question on how to compute
these minimizers numerically. In a short introduction we present the relevant
mathematical and physical background before we discuss the stochastic Burgers
equation in detail. We present algorithms to compute instantons numerically by
an efficient solution of the corresponding Euler-Lagrange equations. A second
focus is the discussion of a recently developed numerical filtering technique
that allows to extract instantons from direct numerical simulations. In the
following we present modifications of the algorithms to make them efficient
when applied to two- or three-dimensional fluid dynamical problems. We
illustrate these ideas using the two-dimensional Burgers equation and the
three-dimensional Navier-Stokes equations
Additive noise effects in active nonlinear spatially extended systems
We examine the effects of pure additive noise on spatially extended systems
with quadratic nonlinearities. We develop a general multiscale theory for such
systems and apply it to the Kuramoto-Sivashinsky equation as a case study. We
first focus on a regime close to the instability onset (primary bifurcation),
where the system can be described by a single dominant mode. We show
analytically that the resulting noise in the equation describing the amplitude
of the dominant mode largely depends on the nature of the stochastic forcing.
For a highly degenerate noise, in the sense that it is acting on the first
stable mode only, the amplitude equation is dominated by a pure multiplicative
noise, which in turn induces the dominant mode to undergo several critical
state transitions and complex phenomena, including intermittency and
stabilisation, as the noise strength is increased. The intermittent behaviour
is characterised by a power-law probability density and the corresponding
critical exponent is calculated rigorously by making use of the first-passage
properties of the amplitude equation. On the other hand, when the noise is
acting on the whole subspace of stable modes, the multiplicative noise is
corrected by an additive-like term, with the eventual loss of any stabilised
state. We also show that the stochastic forcing has no effect on the dominant
mode dynamics when it is acting on the second stable mode. Finally, in a regime
which is relatively far from the instability onset, so that there are two
unstable modes, we observe numerically that when the noise is acting on the
first stable mode, both dominant modes show noise-induced complex phenomena
similar to the single-mode case
A primer on noise-induced transitions in applied dynamical systems
Noise plays a fundamental role in a wide variety of physical and biological
dynamical systems. It can arise from an external forcing or due to random
dynamics internal to the system. It is well established that even weak noise
can result in large behavioral changes such as transitions between or escapes
from quasi-stable states. These transitions can correspond to critical events
such as failures or extinctions that make them essential phenomena to
understand and quantify, despite the fact that their occurrence is rare. This
article will provide an overview of the theory underlying the dynamics of rare
events for stochastic models along with some example applications
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