Mathematical modeling and simulation are moving from being powerful development and
analysis tools towards having increased roles in operational monitoring, control and …

Uncertainty analysis in building energy assessment has become an active research field
because a number of factors influencing energy use in buildings are inherently uncertain …

Digital twin can be defined as a virtual representation of a physical asset enabled through
data and simulators for real-time prediction, optimization, monitoring, controlling, and …

Regularization methods are a key tool in the solution of inverse problems. They are used to
introduce prior knowledge and allow a robust approximation of ill-posed (pseudo-) inverses …

The adjoint method is used for high-fidelity aerodynamic shape optimization and is an
efficient approach for computing the derivatives of a function of interest with respect to a …

Recent breakthroughs in computing power have made it feasible to use machine learning
and deep learning to advance scientific computing in many fields, including fluid mechanics …

Since the seminal work of Venkatakrishnan, Bouman, and Wohlberg [Proceedings of the
Global Conference on Signal and Information Processing, IEEE, 2013, pp. 945--948] in …

Controller design faces a trade-off between robustness and performance, and the reliability
of linear controllers has caused many practitioners to focus on the former. However, there is …

Abstract The ensemble Kalman filter (EnKF) was introduced by Evensen in 1994 (Evensen
1994 J. Geophys. Res. 99 10143–62) as a novel method for data assimilation: state …

To best learn from data about large-scale complex systems, physics-based models
representing the laws of nature must be integrated into the learning process. Inverse theory …