FireMark: A Benchmark Dataset for Data-Driven Forecasting of Laminar Flame Dynamics

High-fidelity computational fluid-dynamics (CFD) simulations of reacting flows are computationally expensive, limiting their usage in real-time applications. In this context, machine learning (ML) methods offer a promising approach for predicting dynamic flow evolution. However, evaluating them remains an open challenge due to the limited availability of benchmarks that combine high temporal resolution with a diverse range of exogenous inputs. In this work, we introduce FireMark, an open benchmark for evaluating data-driven methods to forecast reacting flows evolution. The benchmark contains multiple simulations of a non-premixed laminar methane/nitrogen coflow flame subjected to time-varying inlet velocities. FireMark includes both thermodynamic and chemical fields, obtained through CFD simulations with a detailed chemical kinetic mechanism. The benchmark is designed to assess the ability of forecasting models to reproduce the evolution of a laminar non-premixed flame and to generalize to unseen input signals. Training data consist of sine-sweep forcing signals at two amplitudes, whereas the test set comprises fixed-frequency sinusoids and step functions, with amplitudes both within and outside the training range. To establish reference performance and demonstrate the benchmark capabilities, we evaluate representative classical reduced-order and ML-based forecasting approaches using a set of quantitative metrics, including measures of physical consistency. A linear or bilinear autoregressive model on a reduced basis is more accurate than every neural forecaster on the same latent space and reproduces the heat-release response best; several neural forecasters fall below the accuracy of a constant initial state predictor; and the Fourier neural Operator (FNO), which has the lowest field error, reproduces the integrated heat release worse than that constant forecast. These results provide a baseline performance against which future forecasting methods can be compared. FireMark is publicly available and provides a standardized framework for the reproducible evaluation of data-driven models for reacting-flow dynamics.