http://arxiv.org/abs/2203.12610
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.
Z. Liu, V. Madhavan and M. Tegmark
Thu, 24 Mar 22
21/56
Comments: 17 pages, 10 figures