Comment on "Machine learning conservation laws from differential equations"
The paper [1] by Liu, Madhavan, and Tegmark sought to use machine learning methods to elicit known conservation laws for several systems. However, in their example of a damped 1D harmonic oscillator they made seven serious errors, causing both their method and result to be incorrect. In this Comment, those errors are reviewed.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
AI Poincaré 2.0: Machine Learning Conservation Laws from Differential Equations
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-lin…
BIG-bench Machine LearningDiscovering New Interpretable Conservation Laws as Sparse Invariants
Discovering conservation laws for a given dynamical system is important but challenging. In a theorist setup (differential equations and basis functions are both known), we propose the Sparse Invariant Detector (SID), an…
Model-agnostic machine learning of conservation laws from data
We present a machine learning based method for learning first integrals of systems of ordinary differential equations from given trajectory data. The method is model-agnostic in that it does not require explicit knowledg…
Machine Learning of Linear Differential Equations using Gaussian Processes
This work leverages recent advances in probabilistic machine learning to discover conservation laws expressed by parametric linear equations. Such equations involve, but are not limited to, ordinary and partial different…
BIG-bench Machine LearningGaussian ProcessesSymmetry-regularized neural ordinary differential equations
Neural ordinary differential equations (Neural ODEs) is a class of machine learning models that approximate the time derivative of hidden states using a neural network. They are powerful tools for modeling continuous-tim…