Interpretable machine learning for high-speed, high-fidelity GEOS-Chem model simulations with uncertainty quantification

For many full-physics Earth Science models—including the GEOS-Chem model—there are two main causes of their high computational cost: 1) the models represent phenomena that occur at disparate spatial and temporal scales, which result in stiff differential equations requiring small timesteps for integration; and 2) representing (part of) the Earth System requires a large number of state variables, resulting in a large memory footprint and computational burden for processes that operate on each state variable. The proposed project will leverage advanced machine learning techniques to alleviate these two computational constraints in the GEOS-Chem model. This has the potential to allow improved tradeoffs between accuracy and computational cost in geophysical modeling, resulting both in more accurate “digital twins” of Earth as well as in more computationally tractable simulations for decision-support analysis.

This project will advance the state of knowledge on how machine learning techniques, which leverage model structure, can be used in operational settings rather than small-scale experiments. This project has the potential to expand the Pareto frontier between accuracy and computational cost in geophysical modeling, resulting both in more accurate digital twins of Earth, as well as in computationally tractable simulations for decision-support analysis.

This is early/mid-stage (TRL 2) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

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