OGCVL integrates symbolic and numerical techniques to learn effective nonlinear controlled variables for scalable self-optimizing control in chemical processes.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative 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.
citing papers explorer
-
Generalized Global Self-Optimizing Control for Chemical Processes: Part II Objective-Guided Controlled Variable Learning Approach
OGCVL integrates symbolic and numerical techniques to learn effective nonlinear controlled variables for scalable self-optimizing control in chemical processes.