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The hype surrounding machine learning, a form of artificial intelligence, can make it seem like it is only a matter of time before such techniques are used to solve all scientific problems. While impressive claims are often made, those claims do not always hold up under scrutiny. Machine learning may be useful for solving some problems but falls short for others.

In a new paper in Nature Machine Intelligence, researchers at the U.S. Department of Energy's Princeton Plasma Physics Laboratory (PPPL) and Princeton University performed a systematic review of research comparing machine learning to traditional methods for solving fluid-related partial differential equations (PDEs). Such equations are important in many scientific fields, including the plasma research that supports the development of fusion power for the electricity grid.

The researchers found that comparisons between machine learning methods for solving fluid-related PDEs and traditional methods are often biased in favor of machine learning methods. They also found that negative results were consistently underreported. They suggest rules for performing fair comparisons but argue that cultural changes are also needed to fix what appear to be systemic problems.

"Our research suggests that, though machine learning has great potential, the present literature paints an overly optimistic picture of how machine learning works to solve these particular types of equations," said Ammar Hakim, PPPL's deputy head of computational science and the principal investigator on the research.

Comparing results to weak baselines

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