id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_seu3plr3nfhaxfaujqqxfctmky	Daniel J.A. Hills	An algorithm for discovering Lagrangians automatically from data	2015	17	.pdf	application/pdf	7863	835	53	Early attempts to automatically model physical systems searched for simple mathematical regularities in observed quantities. The Lagrangian is well-suited to be the output of an automated modelling algorithm. search over the possible Lagrangians, working to improve the score. Code for the score function, search algorithms and the datasets we use below can be Figure 2 Result of running the algorithm on simulated data from the five test systems. generating a model in this form the algorithm gives insight into the system directly from We have shown that the algorithm can find models which successfully predict the future Figure 4 Predictions for different initial conditions of the learned simple pendulum model (red tree-based expression search is able to converge on this model. We have shown that the algorithm generates models By capturing the idea of searching for least action models in an algorithm An algorithm for discovering Lagrangians automatically from data	./cache/work_seu3plr3nfhaxfaujqqxfctmky.pdf	./txt/work_seu3plr3nfhaxfaujqqxfctmky.txt