Machine learning conservation laws from differential equations
Ziming Liu, Varun Madhavan, Max Tegmark
Abstract
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 nonlinear 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 three-body problem, the KdV equation, and nonlinear Schrödinger equation.
Topics & Concepts
Conservation lawGeneralizationIndependence (probability theory)Nonlinear systemArtificial neural networkMathematicsDifferential equationApplied mathematicsComputer scienceMathematical analysisArtificial intelligencePhysicsQuantum mechanicsStatisticsModel Reduction and Neural NetworksMeteorological Phenomena and SimulationsComputational Physics and Python Applications