A paradigm for data-driven predictive modeling using field inversion and machine learning

By E. Parish, Karthik Duraisamy. 2016

593 citations recorded by the source corpus.

Subfields: Environmental Engineering, Statistical and Nonlinear Physics, Statistics and Probability

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Citations in the corpus

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2016, background): “This class of modeling framework, which integrates machine learning terms in theory-based models, has been called field inversion andmachine learning (FIML) [73].”
Quantification of model uncertainty in RANS simulations: A review (2018, background): “, turbulent transport equations (for fields k, ω, or ) [43,70], and (2) those introducing uncertainties into the model outputs, e.”
Turbulence Modeling in the Age of Data (2018, background): “Duraisamy et al. (2015) and Parish and Duraisamy (2016) have explored this avenue.”
Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations (2017, background): “The closure parameters that necessarily arise in any such coarse-graining of nonlinear governing equations can then be learned from a broad range of observations and high-resolution simulations, as parametric or nonparametric functions of ESM state variables [cf. Parish and Duraisamy, 2016].”
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems (2020, background): “6) [224] [136] [35] [89] [256] [150] [123] [212] [117] [135] [257] [48] [45] [79] [246]”
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance (2016, methodology): “This inferred anisotropy tensor could then be used during the training phase to mitigate the effect of mismatched mean flow fields between the RANS and DNS, as suggested by Parish & Duraisamy (2016).”
Projection-based model reduction: Formulations for physics-based machine learning (2019, unknown): “The work in [51, 52, 53, 54] augments projection-based reduced models with correction terms learned from data.”
The challenge and opportunity of battery lifetime prediction from field data (2021, background): “To this end, promising frameworks exist, including the field inversion and machine learning paradigm [105], physics-informed neural networks [106–108], neural ordinary differential equations [109], universal differential equations [110], and neural differential equations [111].”
Machine learning methods for turbulence modeling in subsonic flows around airfoils (2018, unknown): “[12-14] combined the inversion model and machine learning to infer and reconstruct better functional forms in turbulence and transitions modeling; Xiao et al.”
Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: A data-driven, physics-informed Bayesian approach (2015, background): “[24]), with the prior corresponding to the initial guess.”
Unsupervised deep learning for super-resolution reconstruction of turbulence (2020, background): “Details are given in Perlman et al. (2007) and Li et al.”
Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework (2017, methodology): “Dow and Wang [5] chose the discrepancy field ∆νt in turbulent viscosity as the response, while Duraisamy and co-workers [7, 11, 12] introduced a full-field multiplicative discrepancy factor β into the production term of the transport equation as the learning target.”