ML models suffer from overoptimism in computational physics PDEs

A key application of machine learning in computational physics is accelerating the solution of partial differential equations (PDEs). Machine-learning-based PDE solvers aim to produce accurate solutions more quickly than standard numerical methods, used as the baseline for comparison.
​
BuzzRobot guest, Nick McGreivy, reviewed the ML-for-PDE-solving literature and found that 79% (60/76) of articles claiming ML outperforms standard methods use weak baselines. He also noted widespread reporting biases, including outcome and publication biases.

Conclusion: ML-for-PDE-solving research is overoptimistic due to weak baselines and under-reported negative results.

Join the BuzzRobot Slack to connect with the community