Results 91 to 100 of about 11,938,701 (296)

Monte Carlo filtering of piecewise deterministic processes [PDF]

, 2011
We present efficient Monte Carlo algorithms for performing Bayesian inference in a broad class of models: those in which the distributions of interest may be represented by time marginals of continuous-time jump processes conditional on a realisation of
Godsill, S, Whiteley, NP, Johansen, Adam M., Whiteley, Nick, Godsill, Simon J., Johansen, AM   +5 more
core   +1 more source

When Poor Exciton Dissociation Limits Photocurrents in Organic Solar Cells: Why Low Offset Non‐Fullerene Acceptor Blends Can't Be Efficient

Advanced Materials, EarlyView.
The energetic offset between the donor and the acceptor components in organic photoactive layers is central to the tradeoff between photovoltage and photocurrent losses. This Perspective covers the most important issues surrounding this topic in non‐fullerene acceptor blends, from the difficulty of accurately determining state energies and driving ...
Dieter Neher, Manasi Pranav
wiley   +1 more source

Resistance to Overdoping Allows Over 2000 S cm−1 Conductivity in P(g3BTTT) With Anion‐Exchange Doping

Advanced Materials, EarlyView.
Anion‐exchange doping of conjugated polymers is an effective way to achieve high conductivities. Here, we report over 2000 S cm−1 electrical conductivity for doped P(g3BTTT). In addition, we show that P(g3BTTT) sustains exceptionally high doping levels without any drop in the charge mobility.
Basil Hunger, Maximilian M. Horn, Eva Röck, Diego Rosas Villalva, Lize Bynens, Jochen Vanderspikken, Christina Kousseff, Silène Gobeil, Olivier Bardagot, Nesibe Akmanşen‐Kalayci, Sarah H. Tolbert, Iain McCulloch, Wouter Maes, Demetra Tsokkou, Natalie Banerji   +14 more
wiley   +1 more source

Quasi Monte Carlo method for linear combination unitaries via classical postprocessing

Physical Review Research
We propose the quasi Monte Carlo method for linear combination of unitaries via classical postprocessing (LCU-CPP) on quantum applications. The LCU-CPP framework has been proposed as an approach to reduce hardware resources, expressing a general target ...
Yuya Kawamata, Kosuke Mitarai, Keisuke Fujii   +2 more
doaj   +1 more source

How Monte Carlo heuristics aid to identify the physical processes of drug release kinetics

MethodsX, 2018
We implement a Monte Carlo heuristic algorithm to model drug release from a solid dosage form. We show that with Monte Carlo simulations it is possible to identify and explain the causes of the unsatisfactory predictive power of current drug release ...
Paola Lecca
doaj   +1 more source

Markov chain Monte Carlo for integrated face image analysis [PDF]

, 2014
This PhD thesis is about the integration of different methods to fit a statistical model of human faces to a single image. I propose to take a probabilistic view on the problem and implement and evaluate an integrative framework for face image ...
Schönborn, Sandro
core   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

A new method to improve the accuracy of radioactivity of sample

四川大学学报. 自然科学版, 2019
Detection efficiency of gamma rays is an important factor to affect the measurement accuracy of radioactivity. There are two methods to acquire detection efficiency currently, the standard sample method and Monte Carlo method. Both methods have their own
ZHONG Wan-Bing, XU Jia-Yun, BAI Li-Xin
doaj  

The moment‐guided Monte Carlo method

International Journal for Numerical Methods in Fluids, 2011
AbstractIn this work we propose a new approach for the numerical simulation of kinetic equations through Monte Carlo schemes. We introduce a new technique that permits to reduce the variance of particle methods through a matching with a set of suitable macroscopic moment equations.
Degond P., DIMARCO, Giacomo, PARESCHI, Lorenzo   +2 more
openaire   +5 more sources

SMCTC: Sequential Monte Carlo in C++ [PDF]

Sequential Monte Carlo methods are a very general class of Monte Carlo methods for sampling from sequences of distributions. Simple examples of these algorithms are used very widely in the tracking and signal processing literature.
Adam M. Johansen
core