  Global warming accelerates permafrost degradation, impacting the reliability
of critical infrastructure used by more than five million people daily.
Furthermore, permafrost thaw produces substantial methane emissions, further
accelerating global warming and climate change and putting more than eight
billion people at additional risk. To mitigate the upcoming risk, policymakers
and stakeholders must be given an accurate prediction of the thaw development.
Unfortunately, comprehensive physics-based permafrost models require
location-specific fine-tuning that is challenging in practice. Models of
intermediate complexity require few input parameters but have relatively low
accuracy. The performance of pure data-driven models is low as well as the
observational data is sparse and limited. In this work, we designed a
physics-informed machine-learning approach for permafrost thaw prediction. The
method uses a heat equation to regularize data-driven approach trained over
permafrost monitoring data and climate projections. The latter leads to higher
precision and better numerical stability allowing for reliable decision-making
or construction and maintenance in the areas endangered by permafrost thaw with
a time horizon of decades.
