92 research outputs found
Gaussian process surrogates for failure detection: a Bayesian experimental design approach
An important task of uncertainty quantification is to identify {the
probability of} undesired events, in particular, system failures, caused by
various sources of uncertainties. In this work we consider the construction of
Gaussian {process} surrogates for failure detection and failure probability
estimation. In particular, we consider the situation that the underlying
computer models are extremely expensive, and in this setting, determining the
sampling points in the state space is of essential importance. We formulate the
problem as an optimal experimental design for Bayesian inferences of the limit
state (i.e., the failure boundary) and propose an efficient numerical scheme to
solve the resulting optimization problem. In particular, the proposed
limit-state inference method is capable of determining multiple sampling points
at a time, and thus it is well suited for problems where multiple computer
simulations can be performed in parallel. The accuracy and performance of the
proposed method is demonstrated by both academic and practical examples
Application of Computer Technology in Mechanical Design and Manufacturing and Automation
The popularization and application of computer information technology has promoted the progress of mankind, from the era of mechanization into the era of information technology. With the development of economic construction, the demand for mechanical equipment and mechanical products continues to increase, computer technology plays an inestimable role in various fields, the machinery industry is no exception, the application of computer technology in the machinery industry to improve the efficiency of production, reduce production costs, and promote the realization of mechanical automation production. This paper analyzes the application of computer technology in mechanical design and manufacturing and its automation
On Computer Communication Network Security Maintenance Measures
With the rapid development of China’s science and technology, today’s Internet communication technology has also followed the ever-changing, but there is still a certain network communication information security problems, and this problem is not to be underestimated. Network communication security has a close relationship with national important documents and information protection confidentiality, social security and stability, national development, social and economic development. The use of Internet technology to commit crimes generally does not leave traces of the crime, which increases the chances of using the network to commit crimes
Active Learning for Saddle Point Calculation
The saddle point (SP) calculation is a grand challenge for computationally
intensive energy function in computational chemistry area, where the saddle
point may represent the transition state (TS). The traditional methods need to
evaluate the gradients of the energy function at a very large number of
locations. To reduce the number of expensive computations of the true
gradients, we propose an active learning framework consisting of a statistical
surrogate model, Gaussian process regression (GPR) for the energy function, and
a single-walker dynamics method, gentle accent dynamics (GAD), for the
saddle-type transition states. SP is detected by the GAD applied to the GPR
surrogate for the gradient vector and the Hessian matrix. Our key ingredient
for efficiency improvements is an active learning method which sequentially
designs the most informative locations and takes evaluations of the original
model at these locations to train GPR. We formulate this active learning task
as the optimal experimental design problem and propose a very efficient
sample-based sub-optimal criterion to construct the optimal locations. We show
that the new method significantly decreases the required number of energy or
force evaluations of the original model.Comment: 27 page
Adaptive design of experiment via normalizing flows for failure probability estimation
Failure probability estimation problem is an crucial task in engineering. In
this work we consider this problem in the situation that the underlying
computer models are extremely expensive, which often arises in the practice,
and in this setting, reducing the calls of computer model is of essential
importance. We formulate the problem of estimating the failure probability with
expensive computer models as an sequential experimental design for the limit
state (i.e., the failure boundary) and propose a series of efficient adaptive
design criteria to solve the design of experiment (DOE). In particular, the
proposed method employs the deep neural network (DNN) as the surrogate of limit
state function for efficiently reducing the calls of expensive computer
experiment. A map from the Gaussian distribution to the posterior approximation
of the limit state is learned by the normalizing flows for the ease of
experimental design. Three normalizing-flows-based design criteria are proposed
in this work for deciding the design locations based on the different
assumption of generalization error. The accuracy and performance of the
proposed method is demonstrated by both theory and practical examples.Comment: failure probability, normalizing flows, adaptive design of
experiment. arXiv admin note: text overlap with arXiv:1509.0461
Maximum conditional entropy Hamiltonian Monte Carlo sampler
The performance of Hamiltonian Monte Carlo (HMC) sampler depends critically
on some algorithm parameters such as the total integration time and the
numerical integration stepsize. The parameter tuning is particularly
challenging when the mass matrix of the HMC sampler is adapted. We propose in
this work a Kolmogorov-Sinai entropy (KSE) based design criterion to optimize
these algorithm parameters, which can avoid some potential issues in the often
used jumping-distance based measures. For near-Gaussian distributions, we are
able to derive the optimal algorithm parameters with respect to the KSE
criterion analytically. As a byproduct the KSE criterion also provides a
theoretical justification for the need to adapt the mass matrix in HMC sampler.
Based on the results, we propose an adaptive HMC algorithm, and we then
demonstrate the performance of the proposed algorithm with numerical examples
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