Surrogate-Assisted Model Predictive Control for Multiphase Processes

Auto-generated stub. Edit this file to add more details.

A control framework where a learned neural operator acts as a fast, differentiable surrogate model inside an iterative Model Predictive Control optimization loop.

Why It Matters

This framework explicitly integrates a learned, fast surrogate (the FNO) into a standard MPC loop to overcome the computational bottleneck of using high-fidelity numerical simulators for real-time control.

Evidence

proposes a surrogate-assisted model predictive control (MPC) framework for regulating multiphase processes using learned operators.

Related Papers

• openalex-2603.25308-real-time-control-of-multiphase-processes-with-learned-opera