The article discusses the use of second-order solvers in scientific machine learning (SciML) for regression tasks. It introduces a software framework based on PETSc to bridge deep-learning software with conventional solvers. Empirical evidence shows the efficacy of trust region methods based on Gauss-Newton approximation in improving generalization errors. The content is structured into sections discussing the introduction, background, related work, contributions, and numerical results of various test cases.
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arxiv.org
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