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We develop a novel method based on machine learning principles to achieveoptimal initiation of CPU-intensive computations for forward asteroseismicmodeling in a multi-D parameter space. A deep neural network is trained on aprecomputed asteroseismology grid containing about 62 million coherentoscillation-mode frequencies derived from stellar evolution models. Thesemodels are representative of the core-hydrogen burning stage ofintermediate-mass and high-mass stars. The evolution models constitute a 6Dparameter space and their predicted low-degree pressure- and gravity-modeoscillations are scanned, using a genetic algorithm. A software pipeline iscreated to find the best fitting stellar parameters for a given set of observedoscillation frequencies. The proposed method finds the optimal regions in the6D parameters space in less than a minute, hence providing the optimal startingpoint for further and more detailed forward asteroseismic modeling in ahigh-dimensional context. We test and apply the method to seven pulsating starsthat were previously modeled asteroseismically by classical grid-based forwardmodeling based on a $\chi^2$ statistic and obtain good agreement with pastresults. Our deep learning methodology opens up the application ofasteroseismic modeling in +6D parameter space for thousands of stars pulsatingin coherent modes with long lifetimes observed by the $Kepler$ space telescopeand to be discovered with the TESS and PLATO space missions, while applicationsso far were done star-by-star for only a handful of cases. Our method is opensource and can be used by anyone freely.

Deep Learning Applied to the Asteroseismic Modeling of Stars with Coherent Oscillation Modes