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    • 21st Annual Phi Kappa Phi Student Research and Fine Arts Conference (2020)
    • 21st Annual PKP Student Research and Fine Arts Conference: Oral Symposia VI
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    Data Driven Machine Learning Discovery of Fundamental Physical Laws

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    Authors
    Brady, Alexander
    Datta, Trinanjan
    Issue Date
    1/30/2020
    URI
    http://hdl.handle.net/10675.2/623079
    
    Metadata
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    Abstract
    Machine learning, which is part of artificial intelligence, has become an invaluable tool to manipulate, analyze, predict, and reveal trends and associations hidden within big data. Machine learning algorithms build a mathematical model of sample data in order to make predictions or decisions, whether simply filtering emails and recommending products in a search bar or discovering the fundamental laws governing highly sensitive chaotic systems. In this research investigation we apply the "Least Absolute Shrinkage and Selection Operator" (LASSO) method of data analysis that determines the relationship, or lack thereof, between variables, allowing for the removal of irrelevant features. The method is first applied to a generic system of differential equations, to demonstrate its applicability, before showcasing its application within the context of a chaotic Lorenz oscillator system. The generic coupled system is solved using the LASSO module available in Python's sci-kit-learn. A similar computational approach for the chaotic system with synthetic Gaussian noisy data successfully reproduces the original Lorenz attractor solution.
    Affiliation
    Chemistry and Physics
    Description
    Presentation given at the 21th Annual Phi Kappa Phi Student Research and Fine Arts Conference
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    21st Annual PKP Student Research and Fine Arts Conference: Oral Symposia VI

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