This paper isolates admissibility conditions for trust-region radius updates that guarantee first-order stationarity and O(ε^{-2}) complexity, verifies them across five mechanism classes, and extends prior frameworks with new convergence results under linear Hessian growth.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Controlled benchmarks on Burgers, Darcy, Allen-Cahn and Navier-Stokes problems show grid unknowns favor discrete adjoint while neural representations favor PINNs, with PINNs cheaper for time-dependent cases and a hybrid strategy recovering adjoint accuracy at lower cost.
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Adjoint Method versus Physics-Informed Neural Networks in PDE-Constrained Inverse Problems
Controlled benchmarks on Burgers, Darcy, Allen-Cahn and Navier-Stokes problems show grid unknowns favor discrete adjoint while neural representations favor PINNs, with PINNs cheaper for time-dependent cases and a hybrid strategy recovering adjoint accuracy at lower cost.