Definable nonconvex parametric optimization problems admit an adjoint state formula under a qualification condition, selecting a conservative field for the value function without smoothness or uniqueness assumptions.
SIAM, 2008
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2representative citing papers
PINNs fail on spurious solutions admitted by the residual loss; adaptive pseudo-time stepping with Jacobian-based step selection improves accuracy and robustness on PDE benchmarks.
citing papers explorer
-
The adjoint state method for parametric definable optimization without smoothness or uniqueness
Definable nonconvex parametric optimization problems admit an adjoint state formula under a qualification condition, selecting a conservative field for the value function without smoothness or uniqueness assumptions.
-
When PINNs Go Wrong: Pseudo-Time Stepping Against Spurious Solutions
PINNs fail on spurious solutions admitted by the residual loss; adaptive pseudo-time stepping with Jacobian-based step selection improves accuracy and robustness on PDE benchmarks.