227,264 research outputs found
Ensemble Kalman Methods: A Mean Field Perspective
This paper provides a unifying mean field based framework for the derivation
and analysis of ensemble Kalman methods. Both state estimation and parameter
estimation problems are considered, and formulations in both discrete and
continuous time are employed. For state estimation problems both the control
and filtering approaches are studied; analogously, for parameter estimation
(inverse) problems the optimization and Bayesian perspectives are both studied.
The approach taken unifies a wide-ranging literature in the field, provides a
framework for analysis of ensemble Kalman methods, and suggests open problems
A new approach to optimal designs for correlated observations
This paper presents a new and efficient method for the construction of
optimal designs for regression models with dependent error processes. In
contrast to most of the work in this field, which starts with a model for a
finite number of observations and considers the asymptotic properties of
estimators and designs as the sample size converges to infinity, our approach
is based on a continuous time model. We use results from stochastic anal- ysis
to identify the best linear unbiased estimator (BLUE) in this model. Based on
the BLUE, we construct an efficient linear estimator and corresponding optimal
designs in the model for finite sample size by minimizing the mean squared
error between the opti- mal solution in the continuous time model and its
discrete approximation with respect to the weights (of the linear estimator)
and the optimal design points, in particular in the multi-parameter case. In
contrast to previous work on the subject the resulting estimators and
corresponding optimal designs are very efficient and easy to implement. This
means that they are practi- cally not distinguishable from the weighted least
squares estimator and the corresponding optimal designs, which have to be found
numerically by non-convex discrete optimization. The advantages of the new
approach are illustrated in several numerical examples.Comment: Keywords and Phrases: linear regression, correlated observations,
optimal design, Gaussian white mouse model, Doob representation, quadrature
formulas AMS Subject classification: Primary 62K05; Secondary: 62M0
A Comparison of Discrete and Continuous Neural Network Approaches to Solve the Class/Teacher Timetabling Problem
This study explores the application of neural network-based heuristics to the class/teacher timetabling problem (CTTP). The paper begins by presenting the basic CTTP characteristics in terms of hard and soft constraints and proposing a formulation for the energy function required to map the problem within the artificial neural network model. There follow two distinct approaches to simulating neural network evolution. The first uses a Potts mean-field annealing simulation based on continuous Potts neurons, which has obtained favorable results in various combi¬natorial optimization problems. Afterwards, a discrete neural network simulation, based on discrete winner-take-all neurons, is proposed. The paper concludes with a comparison of the computational results taken from the application of both heuris¬tics to hard hypothetical and real CTTP instances. This experiment demonstrates that the discrete approach performs better, in terms of solution quality as well as execution time
Fresh Multiple Access: A Unified Framework Based on Large Models and Mean-Field Approximations
Information freshness has attracted increasingly attention in the past decade
as it plays a critical role in the emerging real-time applications. Age of
information (AoI) holds the promise of effectively characterizing the
information freshness, hence widely considered as a fundamental performance
metric. However, in multiple-device scenarios, most existing works focus on the
analysis and optimization of AoI based on queueing systems. The study for a
unified approach for general multiple access control scheme in
freshness-oriented scenarios remains open. In this paper, we take into
consideration the combination of the fundamental freshness metric AoI and
multiple access control schemes to achieve efficient cross-layer analysis and
optimization in freshness-oriented scenarios, which is referred to as fresh
multiple access. To this end, we build a unified framework with a discrete-time
tandem queue model for fresh multiple access. The unified framework enables the
analysis and optimization for general multiple access protocols in fresh
multiple access. To handle the high dimension framework embedded in fresh
multiple access, we introduce large model approaches for the Markov chain
formulation in AoI oriented scenarios. Two typical AoI-based metric are studied
including age of incorrect information (AoII) and peak AoII. Moreover, to
address the computational complexity of the large model, we present mean-field
approximations which significantly reduces the dimension of the Markov chain
model by approximating the integral affect of massive devices in fresh multiple
access.Comment: accepted by Journal of Communications and Network
Variational assimilation of sparse time-averaged data for efficient adjoint-based optimization of unsteady RANS simulations
Data assimilation (DA) plays a crucial role in extracting valuable
information from flow measurements in fluid dynamics problems. Often only
time-averaged data is available, which poses challenges for DA in the context
of unsteady flow problems. Recent works have shown promising results in
optimizing Reynolds-averaged Navier-Stokes (RANS) simulations of stationary
flows using sparse data through variational data assimilation, enabling the
reconstruction of mean flow profiles.
In this study we perform three-dimensional variational data assimilation of
sparse time-averaged data into an unsteady RANS (URANS) simulation by means of
a stationary divergence-free forcing term in the URANS equations. Efficiency
and speed of our method are enhanced by employing coarse URANS simulations and
leveraging the stationary discrete adjoint method for the time-averaged URANS
equations. The data assimilation codes were developed in-house using OpenFOAM
for the URANS simulations as well as for the solution of the adjoint problem,
and Python for the gradient-based optimization.
Our results demonstrate that data assimilation of sparse time-averaged
velocity measurements not only enables accurate mean flow reconstruction, but
also improves the flow dynamics, specifically the vortex shedding frequency. To
validate the efficacy of our approach, we applied it to turbulent flows around
cylinders of various shapes at Reynolds numbers ranging from 3000 to 22000. Our
findings indicate that data points near the cylinder play a crucial role in
improving the vortex shedding frequency, while additional data points further
downstream are necessary to also reconstruct the time-averaged velocity field
in the wake region
Continuous time mean-variance portfolio optimization through the mean field approach
A simple mean-variance portfolio optimization problem in continuous time is solved using the mean field approach. In this approach, the original optimal control problem, which is time inconsistent, is viewed as the McKean\u2013Vlasov limit of a family of controlled many-component weakly interacting systems. The prelimit problems are solved by dynamic programming, and the solution to the original problem is obtained by passage to the limit
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