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Many astrophysical applications require efficient yet reliable forecasts ofstellar evolution tracks. One example is population synthesis, which generatesforward predictions of models for comparison with observations. The majority ofstate-of-the-art rapid population synthesis methods are based on analyticfitting formulae to stellar evolution tracks that are computationally cheap tosample statistically over a continuous parameter range. The computational costsof running detailed stellar evolution codes, such as MESA, over wide anddensely sampled parameter grids are prohibitive, while stellar-age basedinterpolation in-between sparsely sampled grid points leads to intolerablylarge systematic prediction errors. In this work, we provide two solutions forautomated interpolation methods that offer satisfactory trade-off pointsbetween cost-efficiency and accuracy. We construct a timescale-adaptedevolutionary coordinate and use it in a two-step interpolation scheme thattraces the evolution of stars from ZAMS all the way to the end of core heliumburning while covering a mass range from ${0.65}$ to $300 \, \mathrm{M_\odot}$.The feedforward neural network regression model (first solution) that we trainto predict stellar surface variables can make millions of predictions,sufficiently accurate over the entire parameter space, within tens of secondson a 4-core CPU. The hierarchical nearest-neighbor interpolation algorithm(second solution) that we hard-code to the same end achieves even higherpredictive accuracy, the same algorithm remains applicable to all stellarvariables evolved over time, but it is two orders of magnitude slower. Ourmethodological framework is demonstrated to work on the MIST (Choi et al. 2016)data set. Finally, we discuss the prospective applications of these methods andprovide guidelines for generalizing them to higher dimensional parameterspaces.

Scalable stellar evolution forecasting: Deep learning emulation vs. hierarchical nearest neighbor interpolation