Regional importance measures based on failure probability in the presence of epistemic and aleatory uncertainties

2013 ◽  
Vol 228 (6) ◽  
pp. 1029-1047 ◽  
Author(s):  
Lei Cheng ◽  
Zhenzhou Lu ◽  
Luyi Li
Keyword(s):  
Failure Probability ◽  
Computational Cost ◽  
Variance Decomposition ◽  
The State ◽  
Structural Systems ◽  
Conditional Mean ◽  
First Order ◽  
State Dependent ◽  
Main Effect ◽  
Dependent Parameter

For the structural systems with both epistemic and aleatory uncertainties, in order to analyze the effects of different regions of epistemic parameters on failure probability, two regional importance measures (RIMs) are firstly proposed, i.e. contribution to mean of failure probability (CMFP) and contribution to variance of failure probability (CVFP), and their properties are analyzed and verified. Then, to analyze the effect of different regions of the epistemic parameters on their corresponding first-order variance (i.e. main effect) in the Sobol’s variance decomposition, another RIM is proposed which is named as contribution to variance of conditional mean of failure probability (CVCFP). The proposed CVCFP is then extended to define another RIM named as contribution to mean of conditional mean of failure probability, i.e. CMCFP, to measure the contribution of regions of epistemic parameters to mean of conditional mean of failure probability. For the problem that the computational cost for calculating the conditional mean of failure probability may be too large to be accepted, the state dependent parameter (SDP) method is introduced to estimate CVCFP and CMCFP. Several examples are used to demonstrate the effectiveness of the proposed RIMs and the efficiency and accuracy of the SDP-based method are also demonstrated by the examples.

EXPRESS: Pain-dependent learning in healthy volunteers.

2021 ◽  
pp. 174702182110010
Author(s):  
Giovanna Carosena Del Sordo ◽  
Dominic Simon
Keyword(s):  
Memory Task ◽  
Cold Pressor ◽  
The State ◽  
State Dependent Learning ◽  
Research Areas ◽  
State Dependent ◽  
Main Effect ◽  
Context Of Learning ◽  
Dependent Learning ◽  
Adult Volunteers

A state-dependent learning paradigm was studied in which healthy adult volunteers studied/encoded and recalled information from short passages, neutral in their content, in one of the following conditions: (1) Pain during study-Pain during both recall sessions; (2) Pain during study-No Pain during both recall sessions; (3) No Pain during study-Pain during both recall sessions; and (4) No Pain during study-No Pain during both recall sessions. Pain was experimentally induced using the cold pressor technique. In this study we looked at evidence for state-dependent learning when the context of learning is not emotionally driven, but neutral. The memory task consisted of encoding detailed information about short stories, then recalling as many details as possible 20 minutes and 48 hours later. The results indicated no occurrence of a state-dependent learning and retrieval effect in this sample: participants in the pain-no pain and no pain-pain conditions did not significantly perform differently than participants in the pain-pain and no pain-no pain conditions. However, a main effect of the state during study/encoding was significant, suggesting that being in pain during study had a detrimental effect on performance on the memory tests regardless of the state at retrieval. These results oppose previous studies’ findings and shed new light on possible implications in various research areas.

A global sensitivity analysis method based on ANOVA with convex set model and its state-dependent parameter solution

2018 ◽  
Vol 233 (11) ◽  
pp. 4039-4051
Author(s):  
Qing Guo ◽  
Yongshou Liu ◽  
Xiangyu Chen
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Convex Set ◽  
The State ◽  
Analysis Method ◽  
Global Sensitivity ◽  
State Dependent ◽  
Sensitivity Analysis Method ◽  
Dependent Parameter ◽  
Input Variables

Convex set model is most widely applied around nonprobabilistic uncertainty description. This paper combines the convex model with global sensitivity analysis theory of variance, and then proposes an index based on convex set model and variance-based global sensitivity analysis method to illustrate the effect of the nonprobability variables on the dangerous degree. The proposed index consists of two parts, including the main and total indices. The main index can quantitatively reflect the effect of uncertainties of input variables on the variance of output response, and the total index reflects the influence of interaction with other variables in addition to the individual influence of input variables. Furthermore, an efficient state-dependent parameter solution for solving the variance-based global sensitivity analysis of nonprobabilistic convex uncertainty is given in this paper. The state-dependent parameter solution not only greatly improves the efficiency but also guarantees the computational accuracy, and the times of performance functions evaluation decrease from [Formula: see text] in single-loop Monte Carlo solution to 2048 in the state-dependent parameter method. Finally, three numerical examples and a finite element example are used to verify the feasibility and rationality of the proposed method.

A modified variance-based importance measure and its solution by state dependent parameter

2012 ◽  
Vol 227 (1) ◽  
pp. 3-15
Author(s):  
Wenbin Ruan ◽  
Zhenzhou Lu ◽  
Longfei Tian
Keyword(s):  
The State ◽  
Importance Measure ◽  
Parameter Method ◽  
Analysis Method ◽  
Output Response ◽  
State Dependent ◽  
Importance Analysis ◽  
Dependent Parameter ◽  
Input Variables ◽  
State Dependent Parameter Method

To overcome the disadvantage of traditional variance-based importance measures, i.e. the effects of different realizations of input variables on output response may mutually counteract each other, a modified variance-based importance measure is presented for importance analysis of the input variables. The proposed measure analyses the importance of the input variables comprehensively in terms of the expectation and variance of the output response. Compared with the traditional variance-based importance analysis method, the modified importance measure indices not only reflect the old one, but also provide a very useful supplement for it. Furthermore, combined with the advantages of the state dependent parameter model, a solution to the proposed measure indices is provided. Several examples are introduced to show that the modified importance measure is more comprehensive and reasonable, and the solution based on the state dependent parameter method can improve computational efficiency considerably with acceptable precision.

Accurate and Transferable Multitask Prediction of Chemical Properties with an Atoms-in-Molecule Neural Network

2018 ◽  
Author(s):  
Roman Zubatyuk ◽  
Justin S. Smith ◽  
Jerzy Leszczynski ◽  
Olexandr Isayev
Keyword(s):  
Neural Network ◽  
Molecular System ◽  
Computational Cost ◽  
Chemical Properties ◽  
The State ◽  
Molecular Properties ◽  
Training Data ◽  
Dft Methods ◽  
Benchmark Datasets ◽  
Quantum Phenomena

<p>Atomic and molecular properties could be evaluated from the fundamental Schrodinger’s equation and therefore represent different modalities of the same quantum phenomena. Here we present AIMNet, a modular and chemically inspired deep neural network potential. We used AIMNet with multitarget training to learn multiple modalities of the state of the atom in a molecular system. The resulting model shows on several benchmark datasets the state-of-the-art accuracy, comparable to the results of orders of magnitude more expensive DFT methods. It can simultaneously predict several atomic and molecular properties without an increase in computational cost. With AIMNet we show a new dimension of transferability: the ability to learn new targets utilizing multimodal information from previous training. The model can learn implicit solvation energy (like SMD) utilizing only a fraction of original training data, and archive MAD error of 1.1 kcal/mol compared to experimental solvation free energies in MNSol database.</p>

First passage time for the state of exact chemical equilibrium

1980 ◽  
Vol 45 (3) ◽  
pp. 777-782 ◽  
Author(s):  
Milan Šolc
Keyword(s):  
Chemical Equilibrium ◽  
First Passage Time ◽  
Passage Time ◽  
The State ◽  
Recurrence Time ◽  
First Passage ◽  
First Order ◽  
The Mean ◽  
Passage Times ◽  
Number Of Particles

The establishment of chemical equilibrium in a system with a reversible first order reaction is characterized in terms of the distribution of first passage times for the state of exact chemical equilibrium. The mean first passage time of this state is a linear function of the logarithm of the total number of particles in the system. The equilibrium fluctuations of composition in the system are characterized by the distribution of the recurrence times for the state of exact chemical equilibrium. The mean recurrence time is inversely proportional to the square root of the total number of particles in the system.

Reliability and reliability-based sensitivity analysis of self-centering buckling restrained braces using meta-models

2021 ◽  
pp. 1045389X2110263
Author(s):  
Seyede Vahide Hashemi ◽  
Mahmoud Miri ◽  
Mohsen Rashki ◽  
Sadegh Etedali
Keyword(s):  
Failure Probability ◽  
Limit State ◽  
Computational Cost ◽  
Sensitivity Analyses ◽  
State Function ◽  
Reliability Indices ◽  
Buckling Restrained Brace ◽  
Polynomial Response Surface ◽  
Nonlinear Dynamic Analyses ◽  
High Computational Cost

This paper aims to carry out sensitivity analyses to study how the effect of each design variable on the performance of self-centering buckling restrained brace (SC-BRB) and the corresponding buckling restrained brace (BRB) without shape memory alloy (SMA) rods. Furthermore, the reliability analyses of BRB and SC-BRB are performed in this study. Considering the high computational cost of the simulation methods, three Meta-models including the Kriging, radial basis function (RBF), and polynomial response surface (PRSM) are utilized to construct the surrogate models. For this aim, the nonlinear dynamic analyses are conducted on both BRB and SC-BRB by using OpenSees software. The results showed that the SMA area, SMA length ratio, and BRB core area have the most effect on the failure probability of SC-BRB. It is concluded that Kriging-based Monte Carlo Simulation (MCS) gives the best performance to estimate the limit state function (LSF) of BRB and SC-BRB in the reliability analysis procedures. Considering the effects of changing the maximum cyclic loading on the failure probability computation and comparison of the failure probability for different LSFs, it is also found that the reliability indices of SC-BRB were always higher than the corresponding reliability indices determined for BRB which confirms the performance superiority of SC-BRB than BRB.

scholarly journals Uncertainty increases curiosity, but decreases happiness

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lieke L. F. van Lieshout ◽  
Floris P. de Lange ◽  
Roshan Cools
Keyword(s):  
The State ◽  
Main Effect ◽  
Future Work ◽  
Monetary Gain ◽  
Strong Main Effect

AbstractYou probably know what kind of things you are curious about, but can you also explain what it feels like to be curious? Previous studies have demonstrated that we are particularly curious when uncertainty is high and when information provides us with a substantial update of what we know. It is unclear, however, whether this drive to seek information (curiosity) is appetitive or aversive. Curiosity might correspond to an appetitive drive elicited by the state of uncertainty, because we like that state, or rather it might correspond to an aversive drive to reduce the state of uncertainty, because we don’t like it. To investigate this, we obtained both subjective valence (happiness) and curiosity ratings from subjects who performed a lottery task that elicits uncertainty-dependent curiosity. We replicated a strong main effect of outcome uncertainty on curiosity: Curiosity increased with outcome uncertainty, irrespective of whether the outcome represented a monetary gain or loss. By contrast, happiness decreased with higher outcome uncertainty. This indicates that people were more curious, but less happy about lotteries with higher outcome uncertainty. These findings raise the hypothesis, to be tested in future work, that curiosity reflects an aversive drive to reduce the unpleasant state of uncertainty.

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