Neural lumped parameter differential equations with application in friction-stir processing.
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| Title: | Neural lumped parameter differential equations with application in friction-stir processing. |
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| Authors: | Koch, James1 (AUTHOR) james.koch@pnnl.gov, Choi, WoongJo1 (AUTHOR), King, Ethan1 (AUTHOR), Garcia, David1 (AUTHOR), Das, Hrishikesh1 (AUTHOR), Wang, Tianhao1 (AUTHOR), Ross, Ken1 (AUTHOR), Kappagantula, Keerti1 (AUTHOR) |
| Source: | Journal of Intelligent Manufacturing. Feb2025, Vol. 36 Issue 2, p1111-1121. 11p. |
| Subjects: | Ordinary differential equations, Lumped elements, Differential equations, Machine learning, Power tools |
| Abstract: | Lumped parameter methods aim to simplify the evolution of spatially-extended or continuous physical systems to that of a "lumped" element representative of the physical scales of the modeled system. For systems where the definition of a lumped element or its associated physics may be unknown, modeling tasks may be restricted to full-fidelity physics simulations. In this work, we consider data-driven modeling tasks with limited point-wise measurements of otherwise continuous systems. We build upon the notion of the Universal Differential Equation (UDE) to construct data-driven models for reducing dynamics to that of a lumped parameter and inferring its properties. The flexibility of UDEs allow for composing various known physical priors suitable for application-specific modeling tasks, including lumped parameter methods. The motivating example for this work is the plunge and dwell stages for friction-stir welding; specifically, (i) mapping power input into the tool to a point-measurement of temperature and (ii) using this learned mapping for process control. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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