Abstract Model building and parameter estimation are traditional concepts widely used in
chemical, biological, metallurgical, and manufacturing industries. Early modeling …

In the context of science, the well-known adage" a picture is worth a thousand words" might
well be" a model is worth a thousand datasets." In this manuscript we introduce the SciML …

Fluid–structure interaction simulations can be performed in a partitioned way, by coupling a
flow solver with a structural solver. However, Gauss–Seidel iterations between these solvers …

Integrating physics models within machine learning models holds considerable promise
toward learning robust models with improved interpretability and abilities to extrapolate. In …

This manuscript proposes a physics-guided Bayesian neural network, which combines
Approximate-Bayesian-Computation training with physics-based models. This hybrid …

A new physics-guided Bayesian recurrent neural network is proposed in this manuscript.
This hybrid algorithm benefits from the knowledge in physics-based models, the capability of …

Nanomedicine research recently started exploring the combination of therapy and
diagnostics, so-called theranostics, as an approach to offer a more flexible, personal, and …

We propose an algorithmic pipeline enabling deep reinforcement learning controllers to
detect when a significant change in system characteristics has occurred and update the …

The value of unknown parameters of multibody systems is crucial for prediction, monitoring,
and control, sometimes estimated using a biased physics-based model leading to incorrect …

A novel hybrid neural network based learning control method is proposed to improve
trajectory tracking accuracy for complex robot manipulators in this paper. Firstly, a hybrid …