Researchers at Kobe University and Osaka University have successfully developed artificial intelligence technology that can extract hidden equations of motion from regular observational data and create a model that is faithful to the laws of physics.

This technology could enable us to discover the hidden equations of motion behind phenomena for which the laws were considered unexplainable. For example, it may be possible to use physics-based knowledge and simulations to examine ecosystem sustainability.

The research group consisted of Associate Professor YAGUCHI Takaharu and PhD. student CHEN Yuhan (Graduate School of System Informatics, Kobe University), and Associate Professor MATSUBARA Takashi (Graduate School of Engineering Science, Osaka University).

Main points:
- Being able to model (formularize) physical phenomena using artificial intelligence could result in extremely precise, high-speed simulations.
- In current methods using artificial intelligence, it is necessary to use transformed data that fits the equation of motion. Therefore it is difficult to apply artificial intelligence to actual observational data for which the equations of motion are unknown.
- The research group used geometry to develop artificial intelligence that can find the hidden equation of motion in the supplied observational data (regardless of its format) and model it accordingly.
- In the future, it may be possible to discover the hidden physical laws behind phenomena that that had previously been considered to be incompatible with Newton's Laws, such as ecosystem changes.
- This will enable us to carry out investigations and simulations related to these phenomena using the laws of physics, which could reveal previously unknown properties.

These research achievements were made public on December 6, 2021, and were presented at the Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS2021), a prestigious meeting on artificial intelligence technologies. This research was among the top 3% selected for the spotlight category.

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