Machine-learning based error prediction approach for coarse-grid Computational Fluid Dynamics (CG-CFD)

Hanna, Botros N.; Dinh, Truc-Nam; Youngblood, Robert; Bolotnov, Igor A.

doi:10.1016/j.pnucene.2019.103140

Cited by 72 publications

(26 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reducing computational costs and time is thus crucial for making continuum models useful in experimental or clinical settings (1). Rapid developments in machine learning tools combined with supercomputing provide promising avenues to predict complex flow solvers (44,45) that could significantly reduce computational power and allow elaborate parameter exploration.…”

Section: Resultsmentioning

confidence: 99%

High-fidelity continuum modeling predicts avian voiced sound production

Jiang

Rasmussen

Xue

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Voiced sound production is the primary form of acoustic communication in terrestrial vertebrates, particularly birds and mammals, including humans. Developing a causal physics-based model that ultimately links descending vocal motor control to tissue vibration and sound requires embodied approaches that include realistic representations of voice physiology. Here, we first implement and then experimentally test a high-fidelity three-dimensional (3D) continuum model for voiced sound production in birds. Driven by individual-based physiologically quantifiable inputs, combined with noninvasive inverse methods for tissue material parameterization, our model accurately predicts observed key vibratory and acoustic performance traits. These results demonstrate that realistic models lead to accurate predictions and support the continuum model approach as a critical tool toward a causal model of voiced sound production.

show abstract

Section: Resultsmentioning

confidence: 99%

High-fidelity continuum modeling predicts avian voiced sound production

Jiang

Rasmussen

Xue

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

show abstract

“…Artificial neural networks (ANNs) consist of simple artificial neurons that connect with each other by passing information through links between them [118]. Any ANN model could consist of one linear layer or a complex architecture of input layers (deep hidden layers) and finally output layer, as can be seen in Figure 9.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…These emphasize only the difficulties to achieve such outcomes but also the requirements of such state-of-the-art instruments and expertise which are not readily available [ 134 ]. In that respect, multi-scale modeling techniques that involve machine learning and deep learning are foreseen to play an important role [ 94 , 118 , 135 ]. Figure 10 represents the general overview of different length-scale investigation as reported in Reference [ 127 ].…”

Section: Different Modelling and Simulation Techniquesmentioning

confidence: 99%

Practice of Simulation and Life Cycle Assessment in Tribology—A Review

Kurdi

Alhazmi

et al. 2020

Materials

View full text Add to dashboard Cite

To simulate today’s complex tribo-contact scenarios, a methodological breakdown of a complex design problem into simpler sub-problems is essential to achieve acceptable simulation outcomes. This also helps to manage iterative, hierarchical systems within given computational power. In this paper, the authors reviewed recent trends of simulation practices in tribology to model tribo-contact scenario and life cycle assessment (LCA) with the help of simulation. With the advancement of modern computers and computing power, increasing effort has been given towards simulation, which not only saves time and resources but also provides meaningful results. Having said that, like every other technique, simulation has some inherent limitations which need to be considered during practice. Keeping this in mind, the pros and cons of both physical experiments and simulation approaches are reviewed together with their interdependency and how one approach can benefit the other. Various simulation techniques are outlined with a focus on machine learning which will dominate simulation approaches in the future. In addition, simulation of tribo-contacts across different length scales and lubrication conditions is discussed in detail. An extension of the simulation approach, together with experimental data, can lead towards LCA of components which will provide us with a better understanding of the efficient usage of limited resources and conservation of both energy and resources.

show abstract

“…Each CART is trained on a bootstrapped sample of the original training data by selecting many bootstrap observations from the original data (Tao et al 2020). The RF uses a random subset of predictive variables in the division of every node, which reduces the generalization error (Chen et al 2020), and after a large number of regression trees have been generated, they are used to predict the class of new data, the best split at each node of the tree is searched only amongst a randomly selected subset of predictors, using the so-called out-of-bag (OOB) data (Hanna et al 2020). Building a RF model needs three parameters: the number of trees in the forest, the minimum number of data points in each terminal node and the number of features tried at each node (Breiman 2001).…”

Section: Modelling Approachesmentioning

confidence: 99%

Intelligent Data Analytics Approaches for Predicting Dissolved Oxygen Concentration in River: Extremely Randomized Tree Versus Random Forest, MLPNN and MLR

Heddam

2020

Springer Transactions in Civil and Environmental Engineering

View full text Add to dashboard Cite

Control of water quality by monitoring water variables is still of major importance for the protection of human life (El Najjar et al. 2019). Rivers and streams are the major's component of the freshwater ecosystems and constitute the source of life for both humans and animals and they become the most "endangered ecosystems" in the world (Kumar and Jayakumar 2020; Kebede et al. 2020;Emenike et al. 2020). From year to year, a considerable work was carried out to maintain a good water quality status (Jerves-Cobo et al. 2020). Dissolved oxygen concentration (DO) in rivers, lakes and streams freshwater is a key variable in the assessment and control of the aquatic life and freshwater health (Banerjee et al. 2019). It also plays tremendous role in the control of water quality, and has many important effects on aquatic hydrochemistry and aquatic toxicology, and used as an "early warning" and classifying the eutrophication status of lakes (Hoang et al. 2019).Dissolved oxygen should be sufficiently and accurately estimated and monitored, and the variability of DO in freshwaters ecosystems can stem from various reasons and the amount of variability is particularly great when considering the interaction between several other water quality variables (Hutchins and Hitt 2019;Yaseen et al. 2018a). Over the years, it was demonstrated that there is substantial evidence that DO concentration is highly related to water quality variables (e.g. water temperature, water pH, specific conductance), which can be linked together. For example, it was demonstrated that decrease of DO concentration was mainly related to the increase S. Heddam (B) University 20 Août 1955 Skikda,

show abstract

Machine-learning based error prediction approach for coarse-grid Computational Fluid Dynamics (CG-CFD)

Cited by 72 publications

References 25 publications

High-fidelity continuum modeling predicts avian voiced sound production

High-fidelity continuum modeling predicts avian voiced sound production

Practice of Simulation and Life Cycle Assessment in Tribology—A Review

Intelligent Data Analytics Approaches for Predicting Dissolved Oxygen Concentration in River: Extremely Randomized Tree Versus Random Forest, MLPNN and MLR

Contact Info

Product

Resources

About