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Flamelet models are widely used in computational fluid dynamics to simulatethermochemical processes in turbulent combustion. These models typically employmemory-expensive lookup tables that are predetermined and represent thecombustion process to be simulated. Artificial neural networks (ANNs) offer adeep learning approach that can store this tabular data using a small number ofnetwork weights, potentially reducing the memory demands of complex simulationsby orders of magnitude. However, ANNs with standard training losses oftenstruggle with underrepresented targets in multivariate regression tasks, e.g.,when learning minor species mass fractions as part of lookup tables. This paperseeks to improve the accuracy of an ANN when learning multiple species massfractions of a hydrogen (\ce{H2}) combustion lookup table. We assess a simple,yet effective loss weight adjustment that outperforms the standard mean-squarederror optimization and enables accurate learning of all species mass fractions,even of minor species where the standard optimization completely fails.Furthermore, we find that the loss weight adjustment leads to more balancedgradients in the network training, which explains its effectiveness.

Bringing Chemistry to Scale: Loss Weight Adjustment for Multivariate Regression in Deep Learning of Thermochemical Processes