Results 21 to 30 of about 37,546 (263)

An Exploration Algorithm for Stochastic Simulators Driven by Energy Gradients

open access: yesEntropy, 2017
In recent work, we have illustrated the construction of an exploration geometry on free energy surfaces: the adaptive computer-assisted discovery of an approximate low-dimensional manifold on which the effective dynamics of the system evolves ...
Anastasia S. Georgiou   +5 more
doaj   +1 more source

Recursive Concurrent Stochastic Games

open access: yes, 2006
. We study Recursive Concurrent Stochastic Games (RCSGs), extending our recent analysis of recursive simple stochastic games [14, 15] to a concurrent setting where the two players choose moves simultaneously and independently at each state.
Mihalis Yannakakis   +3 more
core   +1 more source

Stochastic Geometry: Boolean model and random geometric graphs

open access: yesESAIM: Proceedings and Surveys, 2015
This paper collects the four contributions which were presented during the session devoted to Stochastic Geometry at the journées MAS 2014.
Calka Pierre   +4 more
doaj   +1 more source

Dimensional flow and fuzziness in quantum gravity: Emergence of stochastic spacetime

open access: yesNuclear Physics B, 2017
We show that the uncertainty in distance and time measurements found by the heuristic combination of quantum mechanics and general relativity is reproduced in a purely classical and flat multi-fractal spacetime whose geometry changes with the probed ...
Gianluca Calcagni, Michele Ronco
doaj   +1 more source

Three phosphatase families form a community: The phosphohydrolases that act upon inositol pyrophosphates

open access: yesFEBS Letters, EarlyView.
Inositol pyrophosphates are energy‐rich signaling molecules that perform critical functions in cells. Three different families of phosphatases hydrolyze the β phosphate of the inositol pyrophosphate molecules: two have narrow specificities and one is promiscuous.
Ronda J. Rolfes
wiley   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

open access: yesAdvanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt   +8 more
wiley   +1 more source

On muting mobile terminals for uplink interference mitigation in HetNets—system-level analysis via stochastic geometry

open access: yesEURASIP Journal on Wireless Communications and Networking, 2019
We investigate the performance of a scheduling algorithm where the mobile terminals (MTs) may be turned off if they cause a level of interference greater than a given threshold.
Francisco Javier Martín   +4 more
doaj   +1 more source

Foundations of stochastic geometry and theory of random sets

open access: yes, 2012
The first section of this chapter starts with the Buffon problem, which is one of the oldest in stochastic geometry, and then continues with the definition of measures on the space of lines.
Molchanov, Ilya, Ilya Molchanov
core   +1 more source

Prediction of Surface Topography Parameters in Direct Laser Interference Patterning of Stainless Steel Using Infrared Monitoring and Convolutional Neural Networks

open access: yesAdvanced Engineering Materials, EarlyView.
This study presents an infrared monitoring approach for direct laser interference patterning (DLIP) combined with a convolutional neural network (CNN). Thermal emission data captured during structuring are used to predict surface topography parameters.
Lukas Olawsky   +5 more
wiley   +1 more source

Uncertainty Quantification in Computational Electromagnetics: The stochastic approach [PDF]

open access: yes, 2013
Models in electromagnetism are more and more accurate. In some applications, the gap between the experience and the model comes from the deviation on input data of the model which are not perfectly known.
CLENET, Stephane
core  

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