Abstract: In this paper, we present a neurodynamic approach to model predictive control ... systems based on two recurrent neural networks (RNNs). The echo state network (ESN) and simplified dual ...

To ensure robust system identification against noisy observations, we devise an algorithm to assess the confidence of our estimated parameters using numerical analysis of the dynamic equations. To ...

During this task, which requires predictive sensorimotor control, the activity of most neurons ... First, we performed PCA (Python, PCA from sklearn.decomposition) on actual and model data to get the ...

Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.

Neurodegenerative diseases like Alzheimer's are a growing concern in the U.S., with over 7 million Americans living with ...

Multi-omics analysis of human kidney PDGFRβ+ mesenchymal cells quantified over 14,000 biomolecules across 8 time points, ...

We are at a turning point where artificial intelligence systems are beginning to operate beyond human control. These systems ...

Air travel is safe everywhere in the country, including at Newark. Confident words from United Airlines CEO Scott Kirby today that belie his frustration about the state of aviation safety in America.

Endogenous intracellular allosteric modulators of GPCRs remain largely unexplored, with limited binding and phenotype data available. This gap arises from the lack of robust computational methods for ...