Dual-Network PINNs for Optimal Control: A Reproducible Benchmark on the Mass-Spring-Damper System
This work presents a transparent and reproducible benchmark study of a direct dual-network Physics-Informed Neural Network (PINN) formulation for the optimal control of a mass-spring-damper system. The classical linear-quadratic optimal control problem is solved by two independent classical methods -- Pontryagin's Minimum Principle with single shooting, and direct transcription through trapezoidal collocation -- and recast as a constrained optimization problem solved by two feedforward neural networks: a state network whose boundary conditions are enforced exactly through a composite cubic-and-mask ansatz, and an unconstrained control network. The composite loss combines the physics resid...
| Authors | Abdeladhim Tahimi, Rinaldo Vieira da Silva Junior |
|---|---|
| Primary category | math.OC |
| Published | 13 June 2026 |
| Last updated at source | 13 June 2026 |
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