Kavli Affiliate: Max Tegmark
| First 5 Authors: Ziming Liu, Varun Madhavan, Max Tegmark, ,
| Summary:
We present a machine learning algorithm that discovers conservation laws from
differential equations, both numerically (parametrized as neural networks) and
symbolically, ensuring their functional independence (a non-linear
generalization of linear independence). Our independence module can be viewed
as a nonlinear generalization of singular value decomposition. Our method can
readily handle inductive biases for conservation laws. We validate it with
examples including the 3-body problem, the KdV equation and nonlinear
Schr"odinger equation.
| Search Query: ArXiv Query: search_query=au:”Max Tegmark”&id_list=&start=0&max_results=10