Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that
arise in quantum physics and random matrix theory. In contrast to traditional structured …

Ever-increasing demands of uncertainty-based design, analysis, and optimization in
aerospace vehicles motivate the development of Monte Carlo methods with wide …

We present an intriguing discovery related to Random Fourier Features: replacing
multiplication by a random Gaussian matrix with multiplication by a properly scaled random …

This is a survey of the main achievements in the methodology and theory of stochastic
global optimization. It comprises two complimentary directions: global random search and …

This paper introduces a new way to compact a continuous probability distribution F into a set
of representative points called support points. These points are obtained by minimizing the …

the present invention relates generally to methods and systems for image rendering in and
by digital computing systems, such as computer graphics methods and systems for motion …

Uncertainty in aircraft trajectory planning and prediction generates major challenges for the
future air traffic management system. Therefore, understanding and managing uncertainty …

Surrogate modelling aims to reduce computational costs by avoiding the solution of rigorous
models for complex physicochemical systems. However, it requires extensive sampling to …

In this work we review a reduced basis method for the solution of uncertainty quantification
problems. Based on the basic setting of an elliptic partial differential equation with random …

The elastic wave propagation is investigated in a beam lattice material characterized by a
square periodic cell with anti-tetrachiral microstructure. With reference to the Floquet-Bloch …