Controlling Chaos in Van Der Pol Dynamics Using Signal-Encoded Deep Learning

Understand

Controlling nonlinear dynamics is a long-standing problem in engineering.

• Harnessing known physical information to accelerate or constrain stochastic learning pursues a new paradigm of scientific machine learning.
• By linearizing nonlinear systems, traditional control methods cannot learn nonlinear features from chaotic data for use in control.
• Here, we introduce Physics-Informed Deep Operator Control (PIDOC), and by encoding the control signal and initial position into the losses of a physics-informed neural network (PINN), the nonlinear system is forced to exhibit the desired trajectory given the control signal.