Metal additive manufacturing (AM) works on the principle of consolidating feedstock material
in layers towards the fabrication of complex objects through localized melting and …

This paper proposes a machine learning–based multifidelity modeling (MFM) and
information-theoretic Bayesian optimization approach where the associated models can …

A multi-fidelity (MF) surrogate involving Gaussian processes (GPs) is used for designing
temporal process maps in laser directed energy deposition (L-DED) additive manufacturing …

Data-driven prediction of spatiotemporal fields in fluid flow problems has received significant
interest lately. However, the scarcity of data often plagues the accuracy of the prevalent …

The multidisciplinary design optimization (MDO) of re-entry vehicles presents many
challenges associated with the plurality of the domains that characterize the design problem …

The exploration and trade-off analysis of different aerodynamic design configurations
requires solving optimization problems. The major bottleneck to assess the optimal design is …

In this article, we introduce a computational methodology for golf disc shape optimization
that employs a novel disc shape parameterization by cubic B-splines. Through application of …

This paper presents a multifidelity optimization strategy for efficient uncertainty quantification
and robust optimization applicable to turbomachinery blade design. The proposed strategy …

Abstract Machine learning models are becoming an increasingly popular way to exploit data
from fluid dynamics simulations. This project investigates how autoencoders and output …

United Technologies Corporation contributed extensively to the development of the CFD
Vision 2030, with Dr. E. Lurie of Pratt & Whitney being one of the co-authors of the 2014 …