Plant Breeding Biomolecular Classification in Quantum Bayesianism (QBism) Physics-Informed Neural Network Architecture

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.31223/X53P9N. This is version 2 of this Preprint.

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Authors

Karriem A.J. Perry, Barbara S Keary

Abstract

In this brief communication, biomolecular plant breeding multi-classification inference is discussed when leveraging the advantages of Physics-informed Neural Network (PiNN) architecture. Albeit, the expected utility of Partial Differential Equation (PDE) inspired neural networks resides in its performance under limited data availability; a variety of neural network configurations result from PDE inspired machine learning models. Having less emphasis on the theoretical outcomes of the PiNN while paying particular attention to the performance of the model prior to convergence. Biotechnology stands to experience myriad benefits as both plant science and machine learning evolve and cross-pollinate.

DOI

https://doi.org/10.31223/X53P9N

Subjects

Artificial Intelligence and Robotics, Climate, Other Statistics and Probability, Plant Sciences, Probability, Quantum Physics, Research Methods in Life Sciences, Soil Science, Statistical, Nonlinear, and Soft Matter Physics, Sustainability, Systems Biology

Keywords

Plant science | quantum machine learning | partial differential equations | Qbism | Biomolecular, plant science, biomolecular, quantum bayesianism, Partial differential equations

Dates

Published: 2022-08-31 11:56

Last Updated: 2022-09-01 07:49

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License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
None

Data Availability (Reason not available):
This is a brief communication absent analysis in this current form.

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