Stochastic Nonlinear System

Stochastic nonlinear systems research focuses on modeling and controlling systems exhibiting unpredictable behavior due to both inherent nonlinearities and random disturbances. Current efforts concentrate on developing robust model architectures, such as normalizing flows and physics-informed machine learning, and advanced algorithms like nonlinear stochastic gradient descent and spectral dynamic embedding, to improve prediction accuracy and control performance under noisy conditions. This field is crucial for advancing control strategies in various applications, from chemical processes and robotics to machine learning, where handling uncertainty and nonlinearity is paramount for reliable and safe operation.