Nonlinear functionals of master equation unravelings
- URL: http://arxiv.org/abs/2402.05352v1
- Date: Thu, 8 Feb 2024 02:21:23 GMT
- Title: Nonlinear functionals of master equation unravelings
- Authors: Dustin Keys, Jan Wehr
- Abstract summary: The trajectories generated by unravelings may also be treated as real -- as in the collapse models.
Two types of nonlinear functionals are considered here: variance, and entropy.
In the case of entropy, these corrections are shown to be negative, expressing the localization introduced by the Lindblad operators.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Unravelings provide a probabilistic representation of solutions of master
equations and a method of computation of the density operator dynamics. The
trajectories generated by unravelings may also be treated as real -- as in the
stochastic collapse models. While averages of linear functionals of the
unraveling trajectories can be calculated from the master equation, the
situation is different for nonlinear functionals, thanks to the corrections
with nonzero expected values, coming from the It\^o formula. Two types of
nonlinear functionals are considered here: variance, and entropy. The
corrections are calculated explicitly for two types of unravelings, based on
Poisson and Wiener processes. In the case of entropy, these corrections are
shown to be negative, expressing the localization introduced by the Lindblad
operators.
Related papers
- Two-Point Deterministic Equivalence for Stochastic Gradient Dynamics in Linear Models [76.52307406752556]
We derive a novel deterministic equivalence for the two-point function of a random resolvent.
We give a unified derivation of the performance of a wide variety of high-dimensional trained linear models with gradient descent.
arXiv Detail & Related papers (2025-02-07T16:45:40Z) - Kernel Operator-Theoretic Bayesian Filter for Nonlinear Dynamical Systems [25.922732994397485]
We propose a machine-learning alternative based on a functional Bayesian perspective for operator-theoretic modeling.
This formulation is directly done in an infinite-dimensional space of linear operators or Hilbert space with universal approximation property.
We demonstrate that this practical approach can obtain accurate results and outperform finite-dimensional Koopman decomposition.
arXiv Detail & Related papers (2024-10-31T20:31:31Z) - Generalized and new solutions of the NRT nonlinear Schrödinger equation [0.0]
We present new solutions of the non-linear Schr"oodinger equation proposed by Nobre, Rego-Monteiro and Tsallis for the free particle.
Analytical expressions for the wave function, the auxiliary field and the probability density are derived using a variety of approaches.
arXiv Detail & Related papers (2024-10-26T17:02:33Z) - BrowNNe: Brownian Nonlocal Neurons & Activation Functions [0.0]
We show that Brownian neural activation functions in low-training data beats the ReLU counterpart.
Our experiments indicate the superior capabilities of Brownian neural activation functions in low-training data.
arXiv Detail & Related papers (2024-06-21T19:40:30Z) - Dynamical chaos in nonlinear Schr\"odinger models with subquadratic
power nonlinearity [137.6408511310322]
We deal with a class of nonlinear Schr"odinger lattices with random potential and subquadratic power nonlinearity.
We show that the spreading process is subdiffusive and has complex microscopic organization.
The limit of quadratic power nonlinearity is also discussed and shown to result in a delocalization border.
arXiv Detail & Related papers (2023-01-20T16:45:36Z) - Stochastic Langevin Differential Inclusions with Applications to Machine Learning [5.274477003588407]
We show some foundational results regarding the flow and properties of Langevin-type Differential Inclusions.
In particular, we show strong existence of the solution, as well as an canonical- minimization of the free-energy functional.
arXiv Detail & Related papers (2022-06-23T08:29:17Z) - Experimental Design for Linear Functionals in Reproducing Kernel Hilbert
Spaces [102.08678737900541]
We provide algorithms for constructing bias-aware designs for linear functionals.
We derive non-asymptotic confidence sets for fixed and adaptive designs under sub-Gaussian noise.
arXiv Detail & Related papers (2022-05-26T20:56:25Z) - The Connection between Discrete- and Continuous-Time Descriptions of
Gaussian Continuous Processes [60.35125735474386]
We show that discretizations yielding consistent estimators have the property of invariance under coarse-graining'
This result explains why combining differencing schemes for derivatives reconstruction and local-in-time inference approaches does not work for time series analysis of second or higher order differential equations.
arXiv Detail & Related papers (2021-01-16T17:11:02Z) - Non-parametric Models for Non-negative Functions [48.7576911714538]
We provide the first model for non-negative functions from the same good linear models.
We prove that it admits a representer theorem and provide an efficient dual formulation for convex problems.
arXiv Detail & Related papers (2020-07-08T07:17:28Z) - Exponentially Weighted l_2 Regularization Strategy in Constructing
Reinforced Second-order Fuzzy Rule-based Model [72.57056258027336]
In the conventional Takagi-Sugeno-Kang (TSK)-type fuzzy models, constant or linear functions are usually utilized as the consequent parts of the fuzzy rules.
We introduce an exponential weight approach inspired by the weight function theory encountered in harmonic analysis.
arXiv Detail & Related papers (2020-07-02T15:42:15Z) - Bayesian Hidden Physics Models: Uncertainty Quantification for Discovery
of Nonlinear Partial Differential Operators from Data [0.0]
There has been a surge of interest in using machine learning models to discover physical laws such as differential equations from data.
We introduce a novel model comprising "leaf modules" that learn to govern functional data as neural networks.
Our approach quantifies the reliability of the learned physics in terms of a posterior distribution over operators and propagates this uncertainty to solutions of novel initial-boundary value problem instances.
arXiv Detail & Related papers (2020-06-07T18:48:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.