(298b) Enabling Interpolation of Sparse Data Via Neural ODEs
AIChE Annual Meeting
Tuesday, November 15, 2022 - 12:49pm to 1:08pm
Key to accurate estimation of system derivatives is accurate interpolation of measured data, which may be spread across multiple experiments. Recently, we proposed Neural ODEs as the data-driven means to interpolate state data for parameter estimation, offering evidence that Neural ODEs could better infer state derivatives than algebraic data-driven models . However, yet to be convincingly demonstrated is whether Neural ODEs can (and under what circumstances) interpolate data better than all standard interpolation techniques. This presentation intends to address this gap. Through a series of case studies, we show how the ability of Neural ODEs to transfer learning across experiments gives it a global interpolation property, allowing it to interpolate datasets outside the reach of standard interpolation techniques, which rely on local interpolation. To demonstrate the frameworkâs generalizability, this presentation will clearly map out the criteria necessary for robust interpolation of sparse datasets via Neural ODEs. Finally, as derivative estimation is generally not an end in itself, we conclude by demonstrating how the accurate estimation of derivatives under sparse data conditions enables automated kinetic model identification. Thus, in addition to highlighting the flexible nature of Neural ODEs, the presentation aims to present a vision of the expansive set of problems these universal interpolators are well-positioned to address.
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