Density Estimates for Solutions of Stochastic Functional Differential Equations

Dũng, Nguyễn Tiến; Son, Ta Cong; Cuong, Tran Manh; Tan, Nguyen Van; Quynh, Trinh Nhu

doi:10.1007/s10473-019-0404-2

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Cited by 2 publications

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“…In fact, in the last decade, the tail distribution and density estimates for various random variables have been investigated by many authors. We can find more results about tail distribution estimates in [1,9] about density estimates in [10][11][12] and references therein. In the present paper, we will focus on providing explicit estimates for the probability distribution () t P X x  , where t X is the solution of Eq.…”

Section: Introduction *mentioning

confidence: 99%

Tail Distribution Estimates of Fractional CIR Model

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Nguyen²

2022

MaP

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Section: Introduction *mentioning

confidence: 99%

Tail Distribution Estimates of Fractional CIR Model

Hang

Nguyen²

2022

MaP

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Machine-Learning-Assisted the Design of Resin Matrix Composites Coating with Ablation Resistance

et al. 2019

IOP Conf. Ser.: Mater. Sci. Eng.

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Traditional experimental methods always cost a lot but produce little when designing and developing new kinds of materials, especially for coating materials. However, with the assistance of machine learning, it is possible to predict the performance of a specific coating without preparation or simulation, which makes the design of material more efficient. In this study, machine learning was introduced to assist the design of resin matrix composites coating with ablation resistance. A structured method for engineering data in the material field was approved. Based on this method, the data from laboratory records and Lange’s Chemistry Handbook were collated into one operational database. All the 190 sets of data were used to train the artificial neural network (ANN) regression model to predict the back-surface temperature of the substrate for specific coating under given ablation condition. The mean absolutely percentage error (MAPE) of the model is 7%. Concerning the characteristics of the material database, a feature engineering method, which combines the Pearson correlation coefficient and random forest (RF) algorithm was performed to identify the main controlling factors of the service performance of the coatings.

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Density Estimates for Solutions of Stochastic Functional Differential Equations

Cited by 2 publications

References 17 publications

Tail Distribution Estimates of Fractional CIR Model

Tail Distribution Estimates of Fractional CIR Model

Machine-Learning-Assisted the Design of Resin Matrix Composites Coating with Ablation Resistance

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