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Found 8 papers, 0 papers with code
AI Enhanced Control Engineering Methods
In this paper, we explore how AI tools can be useful in control applications.
A Quantum Algorithm for Computing All Diagnoses of a Switching Circuit
In this paper we provide a novel approach for computing diagnosis of switching circuits with gate-based quantum computers.
System Resilience through Health Monitoring and Reconfiguration
The framework is based on a physics-based digital twin model and three modules tasked with real-time fault diagnosis, prognostics and reconfiguration.
Improving the Efficiency of Gradient Descent Algorithms Applied to Optimization Problems with Dynamical Constraints
In our second algorithm, we use an ODE solver to reset the ODE solution, but no direct are adjoint sensitivity analysis methods are used.
Interpretable machine learning models: a physics-based view
We describe how we can build models out of the p-H constructs and how we can train them.
Hybrid modeling: Applications in real-time diagnosis
In this paper, we outline a novel hybrid modeling approach that combines machine learning inspired models and physics-based models to generate reduced-order models from high fidelity models.
Design Space Exploration as Quantified Satisfaction
We apply our method to the design of Boolean systems and discover new and more optimal classical digital and quantum circuits for common arithmetic functions such as addition and multiplication.
Recognizing Abnormal Heart Sounds Using Deep Learning
The work presented here applies deep learning to the task of automated cardiac auscultation, i. e. recognizing abnormalities in heart sounds.
