Nature's Cost Function: Simulating Physics by Minimizing the Action
T Strang, I Caruso, S Greydanus - arXiv preprint arXiv:2303.02115, 2023 - arxiv.org
In physics, there is a scalar function called the action which behaves like a cost function.
When minimized, it yields the" path of least action" which represents the path a physical
system will take through space and time. This function is crucial in theoretical physics and is
usually minimized analytically to obtain equations of motion for various problems. In this
paper, we propose a different approach: instead of minimizing the action analytically, we
discretize it and then minimize it directly with gradient descent. We use this approach to …
When minimized, it yields the" path of least action" which represents the path a physical
system will take through space and time. This function is crucial in theoretical physics and is
usually minimized analytically to obtain equations of motion for various problems. In this
paper, we propose a different approach: instead of minimizing the action analytically, we
discretize it and then minimize it directly with gradient descent. We use this approach to …
Nature's Cost Function: Simulating Physics by Minimizing the Action
SJ Greydanus, TD Strang, I Caruso - ICLR 2023 Workshop on …, 2023 - openreview.net
In physics, there is a scalar function called the action which behaves like a cost function.
When minimized, it yields the" path of least action" which represents the path a physical
system will take through space and time. This function is crucial in theoretical physics and is
usually minimized analytically to obtain equations of motion for various problems. In this
paper, we propose a different approach: instead of minimizing the action analytically, we
discretize it and then minimize it directly with gradient descent. We use this approach to …
When minimized, it yields the" path of least action" which represents the path a physical
system will take through space and time. This function is crucial in theoretical physics and is
usually minimized analytically to obtain equations of motion for various problems. In this
paper, we propose a different approach: instead of minimizing the action analytically, we
discretize it and then minimize it directly with gradient descent. We use this approach to …
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