arXiv subjects

Zochil González Arenas

Publications and source records attributed to Zochil González Arenas.

14 recordsLinked to original sources

arXiv2026

A covariant fermionic path integral for scalar Langevin processes with multiplicative white noise

We revisit the construction of the fermionic path-integral representation of overdamped scalar Langevin processes with multiplicative white noise, focusing on the covariance of the generating functional under non-linear changes of variables. We identify the transformations of the auxiliary (commuting and anticommuting) variables that ensure covariance under such transformations. The subtleties induced by the non-differentiable trajectories of the stochastic dynamics are encoded in the fermionic statistics. Upon integrating out the auxiliary variables, we derive the Onsager-Machlup formulation, which agrees with the one recently obtained using a higher-order discretization scheme. In contrast to the latter, the construction proposed here is formulated directly in continuous time.

cond-mat.stat-mech↗

arXiv2024

Identifying phase transitions in physical systems with neural networks: a neural architecture search perspective

The use of machine learning algorithms to investigate phase transitions in physical systems is a valuable way to better understand the characteristics of these systems. Neural networks have been used to extract information of phases and phase transitions directly from many-body configurations. However, one limitation of neural networks is that they require the definition of the model architecture and parameters previous to their application, and such determination is itself a difficult problem. In this paper, we investigate for the first time the relationship between the accuracy of neural networks for information of phases and the network configuration (that comprises the architecture and hyperparameters). We formulate the phase analysis as a regression task, address the question of generating data that reflects the different states of the physical system, and evaluate the performance of neural architecture search for this task. After obtaining the optimized architectures, we further implement smart data processing and analytics by means of neuron coverage metrics, assessing the capability of these metrics to estimate phase transitions. Our results identify the neuron coverage metric as promising for detecting phase transitions in physical systems.

cs.NE↗

arXiv2020

State dependent diffusion in a bistable potential: conditional probabilities and escape rates

We consider a simple model of a bistable system under the influence of multiplicative noise. We provide a path integral representation of the overdamped Langevin dynamics and compute conditional probabilities and escape rates in the weak noise approximation. The saddle-point solution of the functional integral is given by a diluted gas of instantons and anti-instantons, similarly to the additive noise problem. However, in this case, the integration over fluctuations is more involved. We introduce a local time reparametrization that allows its computation in the form of usual Gaussian integrals. We found corrections to the Kramers' escape rate produced by the diffusion function which governs the state dependent diffusion for arbitrary values of the stochastic prescription parameter. Theoretical results are confirmed through numerical simulations.

cond-mat.stat-mech↗

arXiv2019

Conditional probabilities in multiplicative noise processes

We address the calculation of transition probabilities in multiplicative noise stochastic differential equations using a path integral approach. We show the equivalence between the conditional probability and the propagator of a quantum particle with variable mass. Introducing a {\em time reparametrization}, we are able to transform the problem of multiplicative noise fluctuations into an equivalent additive one. We illustrate the method by showing the explicit analytic computation of the conditional probability of a harmonic oscillator in a nonlinear multiplicative environment.

cond-mat.stat-mech↗

arXiv2018

Stochastic dynamics of planar magnetic moments in a three-dimensional environment

We study the stochastic dynamics of a two-dimensional magnetic moment embedded in a three-dimensional environment, described by means of the stochastic Landau-Lifshitz-Gilbert (sLLG) equation. We define a covariant generalization of this equation, valid in the "generalized Stratonovich discretization prescription". We present a path integral formulation that allows to compute any $n-$point correlation function, independently of the stochastic calculus used. Using this formalism, we show the equivalence between the cartesian formulation with vectorial noise, with the polar formulation with just one scalar fluctuation term. In particular, we show that, for isotropic fluctuations, the system is represented by an {\em additive stochastic process}, despite of the multiplicative terms appearing in the original formulation of the sLLG equation, but, for anisotropic fluctuations the noise turns out to be truly multiplicative.

cond-mat.stat-mech↗

arXiv2018

An estimation of distribution algorithm for the computation of innovation estimators of diffusion processes

Estimation of Distribution Algorithms (EDAs) and Innovation Method are recognized methods for solving global optimization problems and for the estimation of parameters in diffusion processes, respectively. Well known is also that the quality of the Innovation Estimator strongly depends on an adequate selection of the initial value for the parameters when a local optimization algorithm is used in its computation. Alternatively, in this paper, we study the feasibility of a specific EDA - a continuous version of the Univariate Marginal Distribution Algorithm (UMDAc) - for the computation of the Innovation Estimators. Numerical experiments are performed for two different models with a high level of complexity. The numerical simulations show that the considered global optimization algorithms substantially improves the effectiveness of the Innovation Estimators for different types of diffusion processes with complex nonlinear and stochastic dynamics.

math.NA↗

arXiv2016

Path Integral approach to nonequilibrium potentials in multiplicative Langevin dynamics

We present a path integral formalism to compute potentials for nonequilibrium steady states, reached by a multiplicative stochastic dynamics. We develop a weak-noise expansion, which allows the explicit evaluation of the potential in arbitrary dimensions and for any stochastic prescription. We apply this general formalism to study noise-induced phase transitions. We focus on a class of multiplicative stochastic lattice models and compute the steady state phase diagram in terms of the noise intensity and the lattice coupling. We obtain, under appropriate conditions, an ordered phase induced by noise. By computing entropy production, we show that microscopic irreversibility is a necessary condition to develop noise-induced phase transitions. This property of the nonequilibrium stationary state has no relation with the initial stages of the dynamical evolution, in contrast with previous interpretations, based on the short-time evolution of the order parameter.

cond-mat.stat-mech↗

arXiv2016

Bosonic binary mixtures with Josephson-type interactions

Motivated by experiments in bosonic mixtures composed of a single element in two different hyperfine states, we study bosonic binary mixtures in the presence of Josephson interactions between species. We focus on a particular model with $O(2)$ isospin symmetry, lifted by an imbalanced population parametrized by a Rabi frequency, $Ω_{R}$, and a detuning, $ν$, which couples the phases of both species. We have studied the model at mean-field approximation plus Gaussian fluctuations. We have found that both species simultaneously condensate below a critical temperature $T_{c}$ and the relative phases are locked by the applied laser phase, $α$. Moreover, the condensate fractions are strongly dependent on the ratio $Ω_{R}/|ν|$ that is not affected by thermal fluctuations.

cond-mat.quant-gas↗

arXiv2015

Langevin dynamics for vector variables driven by multiplicative white noise: a functional formalism

We discuss general multi-dimensional stochastic processes driven by a system of Langevin equations with multiplicative white noise. In particular, we address the problem of how time reversal diffusion processes are affected by the variety of conventions available to deal with stochastic integrals. We present a functional formalism to built up the generating functional of correlation functions without any type of discretization of the Langevin equations at any intermediate step. The generating functional is characterized by a functional integration over two sets of commuting variables as well as Grassmann variables. In this representation, time reversal transformation became a linear transformation in the extended variables, simplifying in this way the complexity introduced by the mixture of prescriptions and the associated calculus rules. The stochastic calculus is codified in our formalism in the structure of the Grassmann algebra. We study some examples such as higher order derivatives Langevin equations and the functional representation of the micromagnetic stochastic Landau-Lifshitz-Gilbert equation.

cond-mat.stat-mech↗

arXiv2014

Nonlinear inhomogeneous Fokker-Planck equation within a generalized Stratonovich prescription

We deduce a nonlinear and inhomogeneous Fokker-Planck equation within a generalized Stratonovich, or stochastic $α$-, prescription ($α=0$, $1/2$ and $1$ respectively correspond to the Itô, Stratonovich and anti-Itô prescriptions). We obtain its stationary state $p_{st}(x)$ for a class of constitutive relations between drift and diffusion and show that it has a $q$-exponential form, $p_{st}(x) = N_q[1 - (1-q)βV(x)]^{1/(1-q)}$, with an index $q$ which does not depend on $α$ in the presence of any nonvanishing nonlinearity. This is in contrast with the linear case, for which the index $q$ is $α$-dependent.

cond-mat.stat-mech↗

arXiv2012

Hidden symmetries and equilibrium properties of multiplicative white-noise stochastic processes

Multiplicative white-noise stochastic processes continuously attract the attention of a wide area of scientific research. The variety of prescriptions available to define it difficults the development of general tools for its characterization. In this work, we study equilibrium properties of Markovian multiplicative white-noise processes. For this, we define the time reversal transformation for this kind of processes, taking into account that the asymptotic stationary probability distribution depends on the prescription. Representing the stochastic process in a functional Grassman formalism, we avoid the necessity of fixing a particular prescription. In this framework, we analyze equilibrium properties and study hidden symmetries of the process. We show that, using a careful definition of equilibrium distribution and taken into account the appropriate time reversal transformation, usual equilibrium properties are satisfied for any prescription. Finally, we present a detailed deduction of a covariant supersymmetric formulation of a multiplicative Markovian white-noise process and study some of the constraints it imposes on correlation functions using Ward-Takahashi identities.

cond-mat.stat-mech↗

arXiv2012

Supersymmetric formulation of multiplicative white--noise stochastic processes

We present a supersymmetric formulation of Markov processes, represented by a family of Langevin equations with multiplicative white-noise. The hidden symmetry encodes equilibrium properties such as fluctuation-dissipation relations. The formulation does not depend on the particular prescription to define the Wiener integral. In this way, different equilibrium distributions, reached at long times for each prescription, can be formally treated on the same footing.

cond-mat.stat-mech↗

arXiv2010

Functional integral approach for multiplicative stochastic processes

We present a functional formalism to derive a generating functional for correlation functions of a multiplicative stochastic process represented by a Langevin equation. We deduce a path integral over a set of fermionic and bosonic variables without performing any time discretization. The usual prescriptions to define the Wiener integral appear in our formalism in the definition of Green functions in the Grassman sector of the theory. We also study non-perturbative constraints imposed by BRS symmetry and supersymmetry on correlation functions. We show that the specific prescription to define the stochastic process is wholly contained in tadpole diagrams. Therefore, in a supersymmetric theory the stochastic process is uniquely defined since tadpole contributions cancels at all order of perturbation theory.

cond-mat.stat-mech↗

arXiv2008

Metastable anisotropy orientation of nematic quantum Hall fluids

We analyze the experimental observation of metastable anisotropy resistance orientation at half filled quantum Hall fluids by means of a model of a quantum nematic liquid in an explicit symmetry breaking potential. We interpret the observed ``rotation'' of the anisotropy axis as a process of nucleation of nematic domains and compute the nucleation rate within this model. By comparing with experiment, we are able to predict the critical radius of nematic bubbles, $R_c\sim 2.6 μm $. Each domain contains about $10^4$ electrons.

cond-mat.mes-hall↗