In this paper we present a novel framework for better modeling uncertainty in complex systems and demonstrate the approach applied to regulated river systems. In this framework, only the stream inflows are assumed to be purely stochastic. Other uncertain quantities are correlated to the uncertainty of the stream inflows using the dynamics of the river as the physical description of the system. ...

The amount of cognitive effort required to process a word has been argued to depend on the word’s effect on the uncertainty about the incoming sentence, as quantified by the entropy over sentence probabilities. The current paper tests this hypothesis more thoroughly than has been done before by using recurrent neural networks for entropy-reduction estimation. A comparison between these estimate...

Using statistical emulators to guide sequential evaluations of complex computer experiments is now a well-established practice. When a model provides multiple outputs, a typical objective is to optimize one of the outputs with constraints (for instance, a threshold not to exceed) on the values of the other outputs. We propose here a new optimization strategy based on the stepwise uncertainty re...

Concurrent engineering, or overlapping development activities, is a widely discussed tool to reduce development leadtime. However, overlapping may not be beneficial if the overlapped activities are uncertain and not independent. This article combines recent results from the organization theory and concurrent engineering literature to develop a model of two overlapping development activities. Un...

This paper examines an index reduction method for linear time-invariant differential algebraic equations, with uncertainty in the equation coefficients. When the bottom block of a block upper triangular leading matrix contains no elements that can be distinguished from zero, the natural action to take is to replace all numbers in the block by exact zeros, and then proceed with index reduction b...

This paper analyses the decision to invest to reduce the emissions of a stock pollutant under environmental uncertainty. It shows that this decision depends on the type and level of uncertainty. When uncertainty is small, there is no simple irreversibility effect because of the tension between environmental irreversibility (the stock of pollutant causes costly long-term social damages), and inv...

In this paper, we investigate the problem of interpretat ion uncertainty caused by the conventional deterministic approaches to drawing image understanding from the three view points of homograph, heuristic knowledge and data ambiguity. To reduce these three factors of uncertaint ies, we propose new paradigm w i t h context-sensitive and hierarchical interpretation for homograph, multiple-inter...

The relationship between the amplitude of P300, the mean amplitude of the Slow Wave, and uncertainty reduction after (dis)confirmation of hypotheses was studied in a Concept-Identification task. The subjects had to categorize stimuli according to a conceptual mIe (joint denial or exclusion) and to rate the confidence that their classification was correct. Three types of feedback were distinguis...

The last few years have witnessed a tremendous development of the computational field of uncertainty quantification (UQ), which includes statistical, sensitivity and reliability analyses, stochastic or robust optimal control/design/optimization, parameter estimation, data assimilation, to name just a few. In all these problems, the solution of stochastic partial differential equations (PDEs) is...

This work surveys mathematical foundations of Model Order Reduction (MOR for short) techniques in accelerating computational forward and inverse UQ. Operator equations (comprising elliptic and parabolic Partial Differential Equations (PDEs for short) and Boundary Integral Equations (BIEs for short)) with distributed uncertain input, being an element of an infinitedimensional, separable Banach s...