Dual-Network PINNs for Optimal Control: A Reproducible Benchmark on the Mass-Spring-Damper System
Researchers have developed a dual-network Physics-Informed Neural Network (PINN) approach to solve optimal control problems, demonstrated on a mass-spring-damper system. This method achieves accuracy comparable to classical techniques, reproducing optimal costs to four significant digits and satisfying constraints exactly. While the PINN formulation is slower to train than traditional methods, it aims to simplify the process for practitioners by providing a clear, reproducible benchmark and implementation. AI