Data-Driven Discovery of Governing Equations

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dc.contributor.authorShikhrakar, Rojesh Man
dc.date.accessioned2022-02-01T10:47:56Z
dc.date.available2022-02-01T10:47:56Z
dc.date.issued2020-07
dc.descriptionTheoretical equations are the basis of scientific progress. Many scientific domains still lack the appropriate theoretical model to reason about the phenomena.en_US
dc.description.abstractTheoretical equations are the basis of scientific progress. Many scientific domains still lack the appropriate theoretical model to reason about the phenomena. With the rise of data, there is an increasing need for methodology in data-driven science and engineering for understanding the physical phenomena. This thesis on Data-Driven Discovery of Governing Equations aims to provide a method for model discovery and find governing partial differential equations from data by training physics-informed neural networks. Given data, our method generalizes a neural network to compute a matrix of candidate terms for Partial Differential Equation(PDE). Minimizing the residuals from the candidate matrix allows us to find the coefficients for the governing equation. We present a framework to discover PDE not restricted to first-order time derivative equations.en_US
dc.identifier.citationMASTER IN MECHANICAL SYSTEMS DESIGN AND ENGINEERINGen_US
dc.identifier.urihttps://hdl.handle.net/20.500.14540/8014
dc.language.isoenen_US
dc.publisherPulchowk Campusen_US
dc.subjectData-Driven Discovery,en_US
dc.subjectPartial Differential Equation,en_US
dc.subjectParameter Estimation,en_US
dc.subjectSparse Optimization,en_US
dc.subjectMachine Learning,en_US
dc.subjectDiscrete Inverse Problemen_US
dc.titleData-Driven Discovery of Governing Equationsen_US
dc.typeThesisen_US
local.academic.levelMastersen_US
local.affiliatedinstitute.titlePulchowk Campusen_US
local.institute.titleInstitute of Engineeringen_US
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