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Machine Learning Based Optimization Workflow for Tuning Numerical Settings of Differential Equation Solvers for Boundary Value Problems
April 17, 2024, 4:42 a.m. | Viny Saajan Victor, Manuel Ettm\"uller, Andre Schmei{\ss}er, Heike Leitte, Simone Gramsch
cs.LG updates on arXiv.org arxiv.org
Abstract: Several numerical differential equation solvers have been employed effectively over the years as an alternative to analytical solvers to quickly and conveniently solve differential equations. One category of these is boundary value solvers, which are used to solve real-world problems formulated as differential equations with boundary conditions. These solvers require certain numerical settings to solve the differential equations that affect their solvability and performance. A systematic fine-tuning of these settings is required to obtain the …
abstract arxiv cs.lg cs.na differential differential equation equation machine machine learning math.na numerical optimization solve type value workflow
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