Results 61 to 70 of about 252,736 (238)

Efficient computation of updated lower expectations for imprecise continuous-time hidden Markov chains [PDF]

, 2017
We consider the problem of performing inference with imprecise continuous-time hidden Markov chains, that is, imprecise continuous-time Markov chains that are augmented with random output variables whose distribution depends on the hidden state of the ...
De Bock, Jasper, Krak, Thomas, Siebes, A
core   +1 more source

AI in chemical engineering: From promise to practice

AIChE Journal, EarlyView.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew, Ronnie Andersson, Thomas Bierweiler, Patrik Ryttestal, Torsten Wik   +4 more
wiley   +1 more source

Petri net analysis of a queueing inventory system with orbital search by the server

Demonstratio Mathematica, 2023
In this article, a queueing inventory system with finite sources of demands, retrial demands, service time, lead time, (s,S)\left(s,S) replenishment policy, and demands search from the orbit was studied.
Ikhlef Lyes, Hakmi Sedda, Lekadir Ouiza, Aïssani Djamil   +3 more
doaj   +1 more source

Risk-sensitive stopping problems for continuous-time Markov chains [PDF]

, 2016
In this paper we consider stopping problems for continuous-time Markov chains under a general risk-sensitive optimization criterion for problems with finite and infinite time horizon.
N. Bäuerle, Anton Popp
semanticscholar   +1 more source

Inverse Design of Alloys via Generative Algorithms: Optimization and Diffusion within Learned Latent Space

Advanced Intelligent Discovery, EarlyView.
This work presents a novel generative artificial intelligence (AI) framework for inverse alloy design through operations (optimization and diffusion) within learned compact latent space from variational autoencoder (VAE). The proposed work addresses challenges of limited data, nonuniqueness solutions, and high‐dimensional spaces.
Mohammad Abu‐Mualla, Ellis Crabtree, Fredrick Michael, Yayue Pan, Jida Huang   +4 more
wiley   +1 more source

Context-Aware Quantification for VANET Security: A Markov Chain-Based Scheme

IEEE Access, 2020
Recently, the quantification of VANET security has drawn significant attention due to the lack of standard computational metrics. The salient features of VANET, such as highly dynamic connections, sensitive information sharing, and unreliable fading ...
Jian Wang, Hongyang Chen, Zemin Sun
doaj   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Integration by Parts and Martingale Representation for a Markov Chain

Abstract and Applied Analysis, 2014
Integration-by-parts formulas for functions of fundamental jump processes relating to a continuous-time, finite-state Markov chain are derived using Bismut's change of measures approach to Malliavin calculus.
Tak Kuen Siu
doaj   +1 more source

Polymerase Chain Reaction. Perturbation Theory and Machine Learning Artificial Intelligence‐Experimental Microbiome Analysis: Applications to Ancient DNA and Tree Soil Metagenomics Cases of Study

Advanced Intelligent Systems, EarlyView.
The polymerase chain reaction (PCR).Perturbation Theory and Machine Learning framework integrates perturbation theory and machine learning to classify genetic sequences, distinguishing ancient DNA from modern controls and predicting tree health from soil metagenomic data.
Jose L. Rodriguez, Estefania Ascencio, Jose M. Ageitos, Lucia Feijoo, Shan He, Amirreza Daghighi, Gerardo M. Casanola‐Martin, Cristian R. Munteanu, Santiago Rodriguez Yañez, Alejandro Pazos, Harbil Bediaga, Brenda Durand Ordiales, Raquel Esteban, Ana‐Maria Heres, Jorge Curiel Yuste, Lur Epelde, Bakhtiyor Rasulev, Tomas Gonzalez, Sonia Arrasate, Humberto González‐Diaz   +19 more
wiley   +1 more source

An Efficient Finite Difference Method for Parameter Sensitivities of Continuous Time Markov Chains [PDF]

SIAM Journal on Numerical Analysis, 2011
We present an efficient finite difference method for the computation of parameter sensitivities that is applicable to a wide class of continuous time Markov chain models.
David F. Anderson
semanticscholar   +1 more source