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ASME Open Journal of Engineering
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A Data-Driven Framework for Computationally Efficient Integration of Chemical Kinetics Using Neural Ordinary Differential Equations
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: A data-driven methodology is introduced for computationally efficient integration of systems of stiff rate equations in chemical kinetics using neural ordinary differential equations (NODE). A systematic algorithm is ...