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SPECIAL ISSUE: ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TECHNOLOGIES
Towards discovery of the differential equations
A. A. Hvatov, R. V. Titov NSS Lab, ITMO University, Saint-Petersburg, Russian Federation
Abstract:
Differential equation discovery, a machine learning subfield, is used to develop interpretable models, particularly in nature-related applications. By expertly incorporating the general parametric form of the equation of motion and appropriate differential terms, algorithms can autonomously uncover equations from data. This paper explores the prerequisites and tools for independent equation discovery without expert input, eliminating the need for equation form assumptions. We focus on addressing the challenge of assessing the adequacy of discovered equations when the correct equation is unknown, with the aim of providing insights for reliable equation discovery without prior knowledge of the equation form.
Keywords:
differential equation discovery, evolutionary optimization, multi-objective optimization, physics-informed neural network.
Citation:
A. A. Hvatov, R. V. Titov, “Towards discovery of the differential equations”, Dokl. RAN. Math. Inf. Proc. Upr., 514:2 (2023), 109–117; Dokl. Math., 108:suppl. 2 (2023), S257–S264
Linking options:
https://www.mathnet.ru/eng/danma456 https://www.mathnet.ru/eng/danma/v514/i2/p109
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Abstract page: | 77 | References: | 12 |
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