Transport map Bayesian parameter estimation for dynamical systems

AbstractAccurate online state and parameter estimation of uncertain non‐linear dynamical systems is a demanding task that has been traditionally handled by adopting non‐linear Kalman Filters or particle filters. However, in case of Kalman filters the system needs to be linearised and for particle filters the computational demand can be high. Recent advances in optimal transport theory and the application to Bayesian model updating pave the way for other approaches to system and parameter identification. They also provide a way of formulating the problem in such a way that efficient online estimation for complex systems is possible. In this work, we investigate the properties of the transport map approach when compared to standard Markov Chain Monte Carlo in an off‐line setting as a first step towards on‐line parameter estimation. We apply both approaches to an analytical exponential model and a dynamical system with seven unknown parameters subjected to ground displacement. Details on the theory of transport maps and on the used MCMC algorithm are also given.</jats:p

DTYMK is essential for genome integrity and neuronal survival

Nucleotide metabolism is a complex pathway regulating crucial cellular processes such as nucleic acid synthesis, DNA repair and proliferation. This study shows that impairment of the biosynthesis of one of the building blocks of DNA, dTTP, causes a severe, early-onset neurodegenerative disease. Here, we describe two unrelated children with bi-allelic variants in DTYMK, encoding dTMPK, which catalyzes the penultimate step in dTTP biosynthesis. The affected children show severe microcephaly and growth retardation with minimal neurodevelopment. Brain imaging revealed severe cerebral atrophy and disappearance of the basal ganglia. In cells of affected individuals, dTMPK enzyme activity was minimal, along with impaired DNA replication. In addition, we generated dtymk mutant zebrafish that replicate this phenotype of microcephaly, neuronal cell death and early lethality. An increase of ribonucleotide incorporation in the genome as well as impaired responses to DNA damage were observed in dtymk mutant zebrafish, providing novel pathophysiological insights. It is highly remarkable that this deficiency is viable as an essential component for DNA cannot be generated, since the metabolic pathway for dTTP synthesis is completely blocked. In summary, by combining genetic and biochemical approaches in multiple models we identified loss-of-function of DTYMK as the cause of a severe postnatal neurodegenerative disease and highlight the essential nature of dTTP synthesis in the maintenance of genome stability and neuronal survival

Efficiency comparison of MCMC and Transport Map Bayesian posterior estimation for structural health monitoring

In this paper, an alternative to solving Bayesian inverse problems for structural health monitoring based on a variational formulation with so-called transport maps is examined. The Bayesian inverse formulation is a widely used tool in structural health monitoring applications. While Markov Chain Monte Carlo (MCMC) methods are often implemented in these settings, they come with the problem of using many model evaluations, which in turn can become quite costly. We focus here on recent developments in the field of transport theory, where the problem is formulated as finding a deterministic, invertible mapping between some easy to evaluate reference density and the posterior. The resulting variational formulation can be solved with integration and optimization methods. We develop a general formulation for the application of transport maps to vibration-based structural health monitoring. Further, we study influences of different integration approaches on the efficiency and accuracy of the transport map approach and compare it to the Transitional MCMC algorithm, a widely used method for structural identification. Both methods are applied to a lower-dimensional dynamic model with uni- and multi-modal properties, as well as to a higher-dimensional neural network surrogate system of an airplane structure. We find that transport maps have a significant increase in accuracy and efficiency, when used in the right circumstances

Efficient parameter estimation for dynamical systems with incomplete data

International audienc

Design and initial performance of the Askaryan Radio Array prototype EeV neutrino detector at the South Pole

We report on studies of the viability and sensitivity of the Askaryan Radio Array (ARA), a new initiative to develop a Teraton-scale ultra-high energy neutrino detector in deep, radio-transparent ice near Amundsen-Scott station at the South Pole. An initial prototype ARA detector system was installed in January 2011, and has been operating continuously since then. We describe measurements of the background radio noise levels, the radio clarity of the ice, and the estimated sensitivity of the planned ARA array given these results, based on the first five months of operation. Anthropogenic radio interference in the vicinity of the South Pole currently leads to a few-percent loss of data, but no overall effect on the background noise levels, which are dominated by the thermal noise floor of the cold polar ice, and galactic noise at lower frequencies. We have also successfully detected signals originating from a 2.5 km deep impulse generator at a distance of over 3 km from our prototype detector, confirming prior estimates of kilometer-scale attenuation lengths for cold polar ice. These are also the first such measurements for propagation over such large slant distances in ice. Based on these data, ARA-37, the ˜200 km2 array now in its initial construction phase, will achieve the highest sensitivity of any planned or existing neutrino detector in the 1016-1019 eV energy range.SCOPUS: ar.jinfo:eu-repo/semantics/publishe