Hamiltonian mechanics is an effective tool to represent many physical
processes with concise yet well-generalized mathematical expressions. A
well-modeled Hamiltonian makes it easy for researchers to analyze and forecast
many related phenomena that are governed by the same physical law. However, in
general, identifying a functional or shared expression of the Hamiltonian is
very difficult. It requires carefully designed experiments and the researcher's
insight that comes from years of experience. We propose that meta-learning
algorithms can be potentially powerful data-driven tools for identifying the
physical law governing Hamiltonian systems without any mathematical assumptions
on the representation, but with observations from a set of systems governed by
the same physical law. We show that a well meta-trained learner can identify
the shared representation of the Hamiltonian by evaluating our method on
several types of physical systems with various experimental settings.

使用基于数据的元学习算法可以识别控制由相同物理定律支配的哈密顿体系的物理规律，无需进行数学假设。在多种物理系统上验证我们的方法可以识别哈密顿共享表达式。