arXiv subjects

Inés P. Mariño

Publications and source records attributed to Inés P. Mariño.

6 recordsLinked to original sources

arXiv2026

Stochastic stability of master--slave synchronization for a class of PDEs with Burgers-type nonlinearity

We investigate the stochastic stability of master--slave synchronization for a class of nonlinear evolution equations sharing a Burgers-type convective nonlinearity and a polynomial linear differential operator, including the Burgers, Kuramoto--Sivashinsky, Kawahara, Benney--Lin, and Nikolaevskiy equations. Under periodic boundary conditions, each equation is represented through a Fourier truncation of order $K$, yielding a real state of one-sided complex Fourier coefficients, which is coupled to a slave system driven by observed master data. We first establish local exponential stability of the deterministic zero-error synchronization solution under a simple condition on the coupling strength in the slave model. We then introduce observational noise in the coupling signals, which turns the slave system into an Itô diffusion and prevents exact synchronization. Comparing the stochastic synchronization error with the deterministic reference error obtained from noiseless observations, we show that bounded master and reference paths and sufficiently strong scalar coupling yield an $\mathcal O(σ^2)$ mean-square bound on their deviation, together with a tail-probability estimate; the bound becomes time-uniform when the deterministic slave field and its spatial derivative can be bounded uniformly in time. Both results use a Fourier-space norm induced by the physical-space $L^2$-norm, so Parseval's identity transfers the spectral bounds directly to the physical fields. Under additional regularity assumptions on the smoothness of the deterministic master and slave fields, we also obtain a mean-square bound on the stochastic synchronization error with respect to the infinite-dimensional (non-truncated) master system. These analytical results are illustrated by the numerical simulation of two master--slave systems, built around the Kawahara and Nikolaevskiy equations.

math-ph↗

arXiv2026

The thermodynamic efficiency of coupled chaotic dissipative structures

Dissipative structures are open dynamical systems that sustain coherent macroscopic organization by continuously exchanging energy and matter with their environment and generating entropy. A recent thermodynamic analysis of the paradigmatic Malkus--Lorenz waterwheel interpreted the Lorenz system as an engine, deriving an exact formula for its thermodynamic efficiency, and showing that efficiency tends to increase as the system is driven far from equilibrium while displaying sharp drops near the Hopf subcritical bifurcation to chaos. Here, we extend that single-engine framework to coupled dissipative structures. We introduce two canonical couplings -- master-slave coupling (series) and symmetric diffusive coupling (parallel) -- and prove two fundamental association laws allowing us to reduce the composite systems to an equivalent engine with a specified efficiency. We then apply these abstract results to coupled Lorenz waterwheels, deriving efficiency formulas consistent with the underlying power balance. We perform numerical simulations confirming that (a) series coupling induces an increase in thermodynamic efficiency, (b) parallel coupling averages the efficiency of engines and increases total energy flow, (c) synchronization is typically neutral or beneficial for efficiency except in narrow parameter regions, and (d) coupling modifies the curvature of entropy-generation trends. Our theorems suggest a mathematically rigorous and transparent route to define and compute thermodynamic efficiency for generalized flow networks, with potential application to complex systems energetics.

nlin.CD↗

arXiv2024

A master-slave coupling scheme for synchronization and parameter estimation in the generalized Kuramoto-Sivashinsky equation

The problem of estimating the constant parameters of the Kuramoto-Sivashinsky (KS) equation from observed data has received attention from researchers in physics, applied mathematics and statistics. This is motivated by the various physical applications of the equation and, also, because it often serves as a test model for the study of space-time pattern formation. Remarkably, most existing inference techniques rely on statistical tools, which are computationally very costly yet do not exploit the dynamical features of the system. In this paper we introduce a simple, online parameter estimation method that relies on the synchronization properties of the KS equation. In particular, we describe a master-slave setup where the slave model is driven by observations from the master system. The slave dynamics are data-driven and designed to continuously adapt the model parameters until identical synchronization with the master system is achieved. We provide a simple analysis that supports the proposed approach and also present and discuss the results of an extensive set of computer simulations. Our numerical study shows that the proposed method is computationally fast and also robust to initialization errors, observational noise and variations in the spatial resolution of the numerical scheme used to integrate the KS equation.

math-ph↗

arXiv2018

A probabilistic scheme for joint parameter estimation and state prediction in complex dynamical systems

Many problems in the geophysical sciences demand the ability to calibrate the parameters and predict the time evolution of complex dynamical models using sequentially-collected data. Here we introduce a general methodology for the joint estimation of the static parameters and the forecasting of the state variables of nonlinear, and possibly chaotic, dynamical models. The proposed scheme is essentially probabilistic. It aims at recursively computing the sequence of joint posterior probability distributions of the unknown model parameters and its (time varying) state variables conditional on the available observations. The latter are possibly partial and contaminated by noise. The new framework combines a Monte Carlo scheme to approximate the posterior distribution of the fixed parameters with filtering (or {\em data assimilation}) techniques to track and predict the distribution of the state variables. For this reason, we refer to the proposed methodology as {\em nested filtering}. In this paper we specifically explore the use of Gaussian filtering methods, but other approaches fit naturally within the new framework. As an illustrative example, we apply three different implementations of the methodology to the tracking of the state, and the estimation of the fixed parameters, of a stochastic two-scale Lorenz 96 system. This model is commonly used to assess data assimilation procedures in meteorology. For this example, we compare different nested filters and show estimation and forecasting results for a 4,000-dimensional system.

stat.CO↗

arXiv2018

Multiplex decomposition of non-Markovian dynamics and the hidden layer reconstruction problem

Elements composing complex systems usually interact in several different ways and as such the interaction architecture is well modelled by a multiplex network. However often this architecture is hidden, as one usually only has experimental access to an aggregated projection. A fundamental challenge is thus to determine whether the hidden underlying architecture of complex systems is better modelled as a single interaction layer or results from the aggregation and interplay of multiple layers. Here we show that using local information provided by a random walker navigating the aggregated network one can decide in a robust way if the underlying structure is a multiplex or not and, in the former case, to determine the most probable number of hidden layers. As a byproduct, we show that the mathematical formalism also provides a principled solution for the optimal decomposition and projection of complex, non-Markovian dynamics into a Markov switching combination of diffusive modes. We validate the proposed methodology with numerical simulations of both (i) random walks navigating hidden multiplex networks (thereby reconstructing the true hidden architecture) and (ii) Markovian and non-Markovian continuous stochastic processes (thereby reconstructing an effective multiplex decomposition where each layer accounts for a different diffusive mode). We also state and prove two existence theorems guaranteeing that an exact reconstruction of the dynamics in terms of these hidden jump-Markov models is always possible for arbitrary finite-order Markovian and fully non-Markovian processes. Finally, we showcase the applicability of the method to experimental recordings from (i) the mobility dynamics of human players in an online multiplayer game and (ii) the dynamics of RNA polymerases at the single-molecule level.

physics.soc-ph↗

arXiv2015

An iterative importance sampler for Bayesian parameter estimation in stochastic models of multicellular clocks

We investigate a stochastic version of the synthetic multicellular clock model proposed by Garcia-Ojalvo, Elowitz and Strogatz. By introducing dynamical noise in the model and assuming that the partial observations of the system can be contaminated by additive noise, we enable a principled mechanism to represent experimental uncertainties in the synthesis of the multicellular system and pave the way for the design of probabilistic methods for the estimation of any unknowns in the model. Within this setup, we investigate the use of an iterative importance sampling scheme, termed nonlinear population Monte Carlo (NPMC), for the Bayesian estimation of the model parameters. The algorithm yields a stochastic approximation of the posterior probability distribution of the unknown parameters given the available data (partial and possibly noisy observations). We prove a new theoretical result for this algorithm, which indicates that the approximations converge almost surely to the actual distributions, even when the weights in the importance sampling scheme cannot be computed exactly. We also provide a detailed numerical assessment of the stochastic multicellular model and the accuracy of the proposed NPMC algorithm, including a comparison with the popular particle Metropolis-Hastings algorithm of Andrieu {\em et al.}, 2010, applied to the same model and an approximate Bayesian computation sequential Monte Carlo method introduced by Mariño {\em et al.}, 2013.

stat.CO↗