Using M.L. to Learn Good Parameters for Physics Simulation

AI Thread Summary
The discussion centers on the application of machine learning, particularly neural networks, for parameter identification in physics simulations. Participants highlight the relevance of this research area and suggest that neural networks can effectively learn parameters for simulations. Resources and references related to "neural network parameter identification" are recommended for further exploration. A specific example, Boxcar2D, is mentioned as a potential illustration of this concept in practice. Overall, the conversation emphasizes the innovative intersection of machine learning and physics simulation.
Jarvis323
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I was wondering if anyone has any knowledge of any work that has been done, to use machine learning methods to try to learn parameters good for a physics simulation. Or if anyone has any insight about how this can be done.
 
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This is the subject of parameter identification. Neural networks have been used for that. It is a very interesting subject that is on the leading edge of current research. Google "neural network parameter identification" to see a set of references.
 
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Do you mean something like http://boxcar2d.com/ ?
 
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