Uncertainty propagation in nonlinear dynamical systems is a field of study that explores how measurement inaccuracies, initial condition errors, and model approximations evolve within inherently ...

Many frequently observed real-world phenomena are nonlinear in nature. This means that their output does not change in a manner that is proportional to their input. These models have a degree of ...

Complex network theory has become a key analytical framework in modern physics for studying structure, dynamics, and emergent behaviour in complex systems.

Engineers have demonstrated a simple computational approach for supporting the classification performance of neural networks operating on sensor time series. The proposed technique involves feeding ...

Anyone with a chronic illness understands the struggle of living with a disease that is deeply unpredictable. Many such illnesses are characterized by long periods of remission broken up by sudden, ...

In the relentless drive to build more cost-efficient, less resource-intensive structures, their operating envelopes are expanding resulting in larger deflections and geometric nonlinearities being ...

Efficient Large Displacement/Large Rotation Dynamic Simulations Using Nonlinear Dynamic Substructures Utilizing reduced-order dynamic math models (DMM) in linear system-level dynamic analyses is a ...

We have considerable expertise in MPC as a powerful tool for providing optimal control in dynamic environments, ensuring real-time performance and adaptability. Our work includes developing predictive ...