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One connection I've seen is energy-based models. I suppose you could try applying ML to things like fluid dynamics? In general, it is thought that physics know

One connection I've seen is energy-based models. I suppose you could try applying ML to things like fluid dynamics? In general, it is thought that physics knowledge transfers better to ML than math because it's less abstract and physicists are more likely to be used to dealing with large datasets and software.

drtm 4y ago
Thanks for the insight! I have some friends that do computational fluid dynamics, with particle physics being similarly numerical, and was looking at physics-informed ML for my own particular area in quantum physics in a recent grant application in the hopes for funding to close the gap a bit myself. What is so powerful about ML and related statistical techniques is their versatility and genericity, so a project that can be benefited by that region of statistics tends not to be too far away. I will look into energy-based models, too.
- joxel 4y ago
  His comment is quite good, I do work with physics informed ML for cfd and other dynamical systems (temperature, hydrology etc.), there is just a ton of opportunity and funding for this type of work in research. Coming from a typical physics education, where you’re learning quantum and Astro, and realizing that 90% of the physics funding from government is in the earth sciences and the related physics was eye opening. I felt shortchanged by my physics education not even including fluids etc.
  - drtm 4y ago
    It was the same here - fluid dynamics was an elective at my university as well (one I took, but still not core syllabus). I guess amount of funding for a domain depends strongly on impact, and in the earth sciences output is much more immediately tangible than uncovering another supremely true but at-the-time inapplicable pattern of physical behaviour in the quantum, or context to humanity in the astronomical or cosmological domains.

drtm 4y ago

Thanks for the insight! I have some friends that do computational fluid dynamics, with particle physics being similarly numerical, and was looking at physics-informed ML for my own particular area in quantum physics in a recent grant application in the hopes for funding to close the gap a bit myself. What is so powerful about ML and related statistical techniques is their versatility and genericity, so a project that can be benefited by that region of statistics tends not to be too far away. I will look into energy-based models, too.

joxel 4y ago
His comment is quite good, I do work with physics informed ML for cfd and other dynamical systems (temperature, hydrology etc.), there is just a ton of opportunity and funding for this type of work in research. Coming from a typical physics education, where you’re learning quantum and Astro, and realizing that 90% of the physics funding from government is in the earth sciences and the related physics was eye opening. I felt shortchanged by my physics education not even including fluids etc.
- drtm 4y ago
  It was the same here - fluid dynamics was an elective at my university as well (one I took, but still not core syllabus). I guess amount of funding for a domain depends strongly on impact, and in the earth sciences output is much more immediately tangible than uncovering another supremely true but at-the-time inapplicable pattern of physical behaviour in the quantum, or context to humanity in the astronomical or cosmological domains.