Mesoscale informed parameter estimation through machine learning: A case-study in fracture modelingUncertainty quantificationReduced order modelMachine learningData driven upscalingProbabilistic emulatorFracture
You can useSimulink Design Optimization™to interactively preprocess test data, automatically estimate model parameters, and validate estimation results. To create a digital twin of a current hardware asset: Model the machine’s dynamics inSimulinkorSimscape™ Automatically update parameters of a model...
Machine Learning for Nanohertz Gravitational Wave Detection and Parameter Estimation with Pulsar Timing Arraymachine learningneural networkPTAGW-induced time residualsStudies have shown that the use of pulsar timing arrays (PTAs) is among the\napproaches with the highest potential to detect very low-...
Extreme learning machinereservoir parameter estimationsandstone reservoirThis study focuses on reservoir parameter estimation using extreme learning machine in heterogeneous sandstone reservoir. The specific aim of work is to obtain accurate porosity and permeability which has proven to be difficult by ...
In subject area: Earth and Planetary Sciences Parameter estimation refers to the process of determining the values of certain properties of a reservoir system by using mathematical models and comparing them to measured data. It involves constructing a mathematical model, defining an objective function ...
Otherwise, the function sets the estimation statuses of the remaining parameter estimates toestimable. Bootstrap Confidence Interval Calculation Thebootci(Statistics and Machine Learning Toolbox)function from Statistics and Machine Learning Toolbox™ is used to compute the bootstrap confidence intervals. ...
A normalizing flow (NF) is a mapping that transforms a chosen probability distribution to a normal distribution. Such flows are a common technique used for data generation and density estimation in machine learning and data science. The density estimate obtained with a NF requires a change of var...
Otherwise, the function sets the estimation statuses of the remaining parameter estimates toestimable. Bootstrap Confidence Interval Calculation Thebootci(Statistics and Machine Learning Toolbox)function from Statistics and Machine Learning Toolbox™ is used to compute the bootstrap confidence intervals. ...
This paper introduces a novel parameter estimation method for the probability tables of Bayesian network classifiers (BNCs), using hierarchical Dirichlet p
Reinforcement learningVariable neighborhood searchIn this paper, reinforcement learning (RL) with a Q-factor algorithm is used to enhance performance of the scheduling method proposed for dynamic job shop scheduling (DJSS) problem which considers random job arrivals and machine breakdowns. In fact, ...