Sea-ice thickness measurement based on the dispersion of ice swell

The dispersion of flexural waves propagating in the Arctic sea ice cover is exploited in order to locally measure the ice thickness. The observed dispersion, for waves filtered in the 4-20 s period interval, at up to 4 broad-band seismometers deployed in Spring 2007 near the North Pole, is compared to a parameterized model that accounts for a complex wavefield made of a superposition of independent plane waves with different amplitudes and back-azimuth angles. The parameterization, that includes finding the best modeled ice thickness, is performed by using the cross-correlation functions between the seismometers. The ice thickness is estimated to 2.5 ± 0.2 m for the ~1 km-large floe the seismic stations were deployed on, which is coherent with other, independent measurements at this site. This study thus demonstrates the feasibility of using broad-band seismometers deployed on the sea-ice in order to passively measure the ice thickness, without requiring active sources nor human intervention.     

A mode matching approach for modeling two dimensional porous grating with infinitely rigid or soft inclusions

This work investigates the acoustical properties of a multilayer porous material in which periodic inclusions are embedded. The material is assumed to be backed by a rigid wall. Most of the studies performed in this field used the multipole method and are limited to circular shape inclusions. Here, a mode matching approach, more convenient for a layered system, is adopted. The inclusions can be in the form of rigid scatterers of an arbitrary shape, in the form of an air-filled cavity or in the form of a porous medium with contrasting properties. The computational approach is validated on simple geometries against other numerical schemes and with experimental results obtained in an anechoic room on a rigid grating embedded in a porous material made of 2 mm glass beads. The method is used to study the acoustic absorption behavior of this class of materials in the low frequency range and at a range of angles of incidence.     

Analysis of the impact degree distribution in metabolic networks using branching process approximation

Theoretical frameworks to estimate the tolerance of metabolic networks to various failures are important to evaluate the robustness of biological complex systems in systems biology. In this paper, we focus on a measure for robustness in metabolic networks, namely, the impact degree, and propose an approximation method to predict the probability distribution of impact degrees from metabolic network structures using the theory of branching process. We demonstrate the relevance of this method by testing it on real-world metabolic networks. Although the approximation method possesses a few limitations, it may be a powerful tool for evaluating metabolic robustness.     

Peeling of Dirac and Maxwell fields on a Schwarzschild background

We study the peeling of Dirac and Maxwell fields on a Schwarzschild background following the approach developed by the authors in Mason and Nicolas (2009) [12] for the wave equation. The method combines a conformal compactification with vector field techniques in order to work out the optimal space of initial data for a given transverse regularity of the rescaled field across null infinity. The results show that analogous decay and regularity assumptions in Minkowski and Schwarzschild produce the same regularity across null infinity. The results are valid also for the classes of asymptotically simple spacetimes constructed by Corvino–Schoen/Chru?ciel–Delay.     

Learning PDE to Model Self-Organization of Matter

A self-organization hydrodynamic process has recently been proposed to partially explain the formation of femtosecond laser-induced nanopatterns on Nickel, which have important applications in optics, microbiology, medicine, etc. Exploring laser pattern space is difficult, however, which simultaneously (i) motivates using machine learning (ML) to search for novel patterns and (ii) hinders it, because of the few data available from costly and time-consuming experiments. In this paper, we use ML to predict novel patterns by integrating partial physical knowledge in the form of the Swift-Hohenberg (SH) partial differential equation (PDE). To do so, we propose a framework to learn with few data, in the absence of initial conditions, by benefiting from background knowledge in the form of a PDE solver. We show that in the case of a self-organization process, a feature mapping exists in which initial conditions can safely be ignored and patterns can be described in terms of PDE parameters alone, which drastically simplifies the problem. In order to apply this framework, we develop a second-order pseudospectral solver of the SH equation which offers a good compromise between accuracy and speed. Our method allows us to predict new nanopatterns in good agreement with experimental data. Moreover, we show that pattern features are related, which imposes constraints on novel pattern design, and suggest an efficient procedure of acquiring experimental data iteratively to improve the generalization of the learned model. It also allows us to identify the limitations of the SH equation as a partial model and suggests an improvement to the physical model itself.     

Die L?slichkeit der Salicyls?ure und Benzo?s?ure in Wasser

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