Rapidly growing catalogs of compact binary mergers from advanced gravitational-wave detectors allow us to explore the astrophysics massive stellar binaries. Merger observations can constrain uncertain parameters that describe underlying processes in evolution stars and systems population models. In this paper, we demonstrate black hole populations - namely, detection rates, chirp masses, redshi...

Numerical modeling is increasingly used to analyze practical rock engineering problems. The geological strength index (GSI) a critical input for many However, no available method allows the quantification of GSI parameters, and engineers must consider range values. As projects progress, these ranges can be narrowed down. Machine learning (ML) algorithms have been coupled with numerical create s...

Model-based methods are popular in derivative-free optimization (DFO). In most of them, a single model function is built to approximate the objective function. This generally based on assumption that one blackbox. However, some real-life and theoretical problems show may consist several blackboxes. those problems, information provided by each blackbox not be equal. this situation, could build m...

A fast and accurate turbulence transport model based on quasilinear gyrokinetics is developed. The consists of a set neural networks trained bespoke GENE dataset, with saturation rule calibrated to dedicated nonlinear simulations. resultant network approximately eight orders magnitude faster than the original calculations. ITER predictions new project fusion gain in line targets. While dataset ...

Optimising a swarm of many robots can be computationally demanding, especially when accurate simulations are required to evaluate the proposed robot configurations. Consequentially, size instances and swarms must limited, reducing number problems that addressed. In this article, we study viability using surrogate models based on Gaussian processes artificial neural networks as predictors robots...

Abstract Surrogate models have several uses in engineering design, including speeding up design optimization, noise reduction, test measurement interpolation, gradient estimation, portability, and protection of intellectual property. Traditionally, surrogate require that all training data conform to the same parametrization (e.g., variables), limiting freedom prohibiting reuse historical data. ...