Positive Feedback, Memory and the Predictability of Earthquakes

We review the "critical point" concept for large earthquakes and enlarge it in the framework of so-called "finite-time singularities". The singular behavior associated with accelerated seismic release is shown to result from a positive feedback of the seismic activity on its release rate. The most important mechanisms for such positive feedback are presented. We introduce and solve analytically a novel simple model of geometrical positive feedback in which the stress shadow cast by the last large earthquake is progressively fragmented by the increasing tectonic stress. Finally, we present a somewhat speculative figure that tends to support a mechanism based on the decay of stress shadows. This figure suggests that a large earthquake in Southern California of size similar to the 1812 great event is maturing.Comment: PostScript document of 18 pages + 2 eps figure

Techniques for the Fast Simulation of Models of Highly dependable Systems

With the ever-increasing complexity and requirements of highly dependable systems, their evaluation during design and operation is becoming more crucial. Realistic models of such systems are often not amenable to analysis using conventional analytic or numerical methods. Therefore, analysts and designers turn to simulation to evaluate these models. However, accurate estimation of dependability measures of these models requires that the simulation frequently observes system failures, which are rare events in highly dependable systems. This renders ordinary Simulation impractical for evaluating such systems. To overcome this problem, simulation techniques based on importance sampling have been developed, and are very effective in certain settings. When importance sampling works well, simulation run lengths can be reduced by several orders of magnitude when estimating transient as well as steady-state dependability measures. This paper reviews some of the importance-sampling techniques that have been developed in recent years to estimate dependability measures efficiently in Markov and nonMarkov models of highly dependable system

Realistic time-scale fully atomistic simulations of surface nucleation of dislocations in pristine nanopillars

We use our recently proposed accelerated dynamics algorithm (Tiwary and van de Walle, 2011) to calculate temperature and stress dependence of activation free energy for surface nucleation of dislocations in pristine Gold nanopillars under realistic loads. While maintaining fully atomistic resolution, we achieve the fraction of a second time-scale regime. We find that the activation free energy depends significantly and non-linearly on the driving force (stress or strain) and temperature, leading to very high activation entropies. We also perform compression tests on Gold nanopillars for strain-rates varying between 7 orders of magnitudes, reaching as low as 10^3/s. Our calculations bring out the perils of high strain-rate Molecular Dynamics calculations: we find that while the failure mechanism for compression of Gold nanopillars remains the same across the entire strain-rate range, the elastic limit (defined as stress for nucleation of the first dislocation) depends significantly on the strain-rate. We also propose a new methodology that overcomes some of the limits in our original accelerated dynamics scheme (and accelerated dynamics methods in general). We lay out our methods in sufficient details so as to be used for understanding and predicting deformation mechanism under realistic driving forces for various problems

A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection

It is important to identify the change point of a system's health status, which usually signifies an incipient fault under development. The One-Class Support Vector Machine (OC-SVM) is a popular machine learning model for anomaly detection and hence could be used for identifying change points; however, it is sometimes difficult to obtain a good OC-SVM model that can be used on sensor measurement time series to identify the change points in system health status. In this paper, we propose a novel approach for calibrating OC-SVM models. The approach uses a heuristic search method to find a good set of input data and hyperparameters that yield a well-performing model. Our results on the C-MAPSS dataset demonstrate that OC-SVM can also achieve satisfactory accuracy in detecting change point in time series with fewer training data, compared to state-of-the-art deep learning approaches. In our case study, the OC-SVM calibrated by the proposed model is shown to be useful especially in scenarios with limited amount of training data

Three-dimensional molecular dynamics simulations of void coalescence during dynamic fracture of ductile metals

Void coalescence and interaction in dynamic fracture of ductile metals have been investigated using three-dimensional strain-controlled multi-million atom molecular dynamics simulations of copper. The correlated growth of two voids during the coalescence process leading to fracture is investigated, both in terms of its onset and the ensuing dynamical interactions. Void interactions are quantified through the rate of reduction of the distance between the voids, through the correlated directional growth of the voids, and through correlated shape evolution of the voids. The critical inter-void ligament distance marking the onset of coalescence is shown to be approximately one void radius based on the quantification measurements used, independent of the initial separation distance between the voids and the strain-rate of the expansion of the system. The interaction of the voids is not reflected in the volumetric asymptotic growth rate of the voids, as demonstrated here. Finally, the practice of using a single void and periodic boundary conditions to study coalescence is examined critically and shown to produce results markedly different than the coalescence of a pair of isolated voids.Comment: Accepted for publication in Physical Review

Fault modelling and accelerated simulation of integrated circuits manufacturing defects under process variation

As silicon manufacturing process scales to and beyond the 65-nm node, process variation can no longer be ignored. The impact of process variation on integrated circuit performance and power has received significant research input. Variation-aware test, on the other hand, is a relatively new research area that is currently receiving attention worldwide.Research has shown that test without considering process variation may lead to loss of test quality. Fault modelling and simulation serve as a backbone of manufacturing test. This thesis is concerned with developing efficient fault modelling techniques and simulation methodologies that take into account the effect of process variation on manufacturing defects with particular emphasis on resistive bridges and resistive opens.The first contribution of this thesis addresses the problem of long computation time required to generate logic fault of resistive bridges under process variation by developing a fast and accurate modelling technique to model logic fault behaviour of resistive bridges.The new technique is implemented by employing two efficient voltage calculation algorithms to calculate the logic threshold voltage of driven gates and critical resistance of a fault-site to enable the computation of bridge logic faults without using SPICE. Simulation results show that the technique is fast (on average 53 times faster) and accurate (worst case is 2.64% error) when compared with HSPICE. The second contribution analyses the complexity of delay fault simulation of resistive bridges to reduce the computation time of delay fault when considering process variation. An accelerated delay fault simulation methodology of resistive bridges is developed by employing a three-step strategy to speed up the calculation of transient gate output voltage which is needed to accurately compute delay faults. Simulation results show that the methodology is on average 17.4 times faster, with 5.2% error in accuracy, when compared with HSPICE. The final contribution presents an accelerated simulation methodology of resistive opens to address the problem of long simulation time of delay fault when considering process variation. The methodology is implemented by using two efficient algorithms to accelerate the computation of transient gate output voltage and timing critical resistance of an open fault-site. Simulation results show that the methodology is on average up to 52 times faster than HSPICE, with 4.2% error in accuracy

Metallic nanoparticles meet Metadynamics

We show how standard Metadynamics coupled with classical Molecular Dynamics can be successfully ap- plied to sample the configurational and free energy space of metallic and bimetallic nanopclusters via the implementation of collective variables related to the pair distance distribution function of the nanoparticle itself. As paradigmatic examples we show an application of our methodology to Ag147, Pt147 and their alloy AgshellPtcore at 1:1 and 2:1 chemical compositions. The proposed scheme is not only able to reproduce known structural transformation pathways, as the five and the six square-diamond mechanisms both in pure and core-shell nanoparticles but also to predict a new route connecting icosahedron to anti-cuboctahedron.Comment: 7 pages, 8 figure