A Bayesian approach to blood rheological uncertainties in aortic hemodynamics.

Computational hemodynamics has received increasing attention recently. Patient-specific simulations require questionable model assumptions, for example, for geometry, boundary conditions, and material parameters. Consequently, the credibility of these simulations is much doubted, and rightly so.

Cross-Entropy Learning for Aortic Pathology Classification of Artificial Multi-Sensor Impedance Cardiography Signals.

An aortic dissection, a particular aortic pathology, occurs when blood pushes through a tear between the layers of the aorta and forms a so-called false lumen. Aortic dissection has a low incidence compared to other diseases, but a relatively high mortality that increases with disease progression. An early identification and treatment increases patients' chances of survival.

Bayesian Uncertainty Quantification with Multi-Fidelity Data and Gaussian Processes for Impedance Cardiography of Aortic Dissection.

In 2000, Kennedy and O'Hagan proposed a model for uncertainty quantification that combines data of several levels of sophistication, fidelity, quality, or accuracy, e.g., a coarse and a fine mesh in finite-element simulations.

Auxiliary master equation approach within stochastic wave functions: Application to the interacting resonant level model.

We present further developments of the auxiliary master equation approach (AMEA), a numerical method to simulate many-body quantum systems in as well as out of equilibrium and apply it to the interacting resonant level model to benchmark the new developments. In particular, our results are obtained by employing the stochastic wave functions method to solve the auxiliary open quantum system arising within AMEA. This development allows us to reach extremely low wall times for the calculation of correlation functions with respect to previous implementations of AMEA.

Bayesian Analysis of Femtosecond Pump-Probe Photoelectron-Photoion Coincidence Spectra with Fluctuating Laser Intensities.

This paper employs Bayesian probability theory for analyzing data generated in femtosecond pump-probe photoelectron-photoion coincidence (PEPICO) experiments. These experiments allow investigating ultrafast dynamical processes in photoexcited molecules. Bayesian probability theory is consistently applied to data analysis problems occurring in these types of experiments such as background subtraction and false coincidences.

Nonequilibrium dynamical mean-field theory: an auxiliary quantum master equation approach.

We introduce a versatile method to compute electronic steady-state properties of strongly correlated extended quantum systems out of equilibrium. The approach is based on dynamical mean-field theory (DMFT), in which the original system is mapped onto an auxiliary nonequilibrium impurity problem imbedded in a Markovian environment. The steady-state Green's function of the auxiliary system is solved by full diagonalization of the corresponding Lindblad equation.

Strong coupling expansion for the Bose-Hubbard and Jaynes-Cummings lattice models.

A strong coupling expansion based on the Kato-Bloch perturbation theory, which has recently been proposed by Eckardt et al (2009 Phys. Rev. B 79 195131) and Teichmann et al (2009 Phys.

A numerical projection technique for large-scale eigenvalue problems.

We present a new numerical technique to solve large-scale eigenvalue problems. It is based on the projection technique, used in strongly correlated quantum many-body systems, where first an effective approximate model of smaller complexity is constructed by projecting out high energy degrees of freedom and in turn solving the resulting model by some standard eigenvalue solver.Here we introduce a generalization of this idea, where both steps are performed numerically and which in contrast to the standard projection technique converges in principle to the exact eigenvalues.

Reconstruction of surfaces from phase-shifting speckle interferometry: Bayesian approach.

The reconstruction of surfaces from speckle interferometry data is a demanding data-analysis task that involves edge detection, edge completion, and image reconstruction from noisy data. We present an approach that makes optimal use of the experimental information to minimize the hampering influence of the noise. The experimental data are then analyzed with a combination of wavelet transform and Bayesian probability theory.

Signal and background separation.

In many areas of research the measured spectra consist of a collection of "peaks"--the sought-for signals--which sit on top of an unknown background. The subtraction of the background in a spectrum has been the subject of many investigations and different techniques, varying from filtering to fitting polynomial functions, have been developed. These techniques yield results that are not always satisfactory and often even misleading.

Analysis of multicomponent mass spectra applying Bayesian probability theory.

In this paper we develop a method for the decomposition of mass spectra of gas mixtures, together with the relevant calibration measurements. The method is based on Bayesian probability theory. Given a set of spectra, the algorithm returns the relative concentrations and the associated margin of confidence for each component of the mixture.

Simulations on infinite-size lattices.

We introduce a Monte Carlo method, as a modification of existing cluster algorithms, which allows simulations directly on systems of infinite size, and for quantum models also at beta = infinity. All two-point functions can be obtained, including dynamical information. When the number of iterations is increased, correlation functions at larger distances become available.

Background estimation in experimental spectra.

A general probabilistic technique for estimating background contributions to measured spectra is presented. A Bayesian model is used to capture the defining characteristics of the problem, namely, that the background is smoother than the signal. The signal is allowed to have positive and/or negative components.