论文标题
端口 - 分离神经网络：复杂物理系统的热力学知识的机器学习
Port-metriplectic neural networks: thermodynamics-informed machine learning of complex physical systems
论文作者
论文摘要
我们为基于哈米尔顿港形式主义的复杂物理系统的机器学习发展了归纳偏见。为了满足构造在学识渊博的物理学中的热力学原理（能量保护，非负熵产生），我们相应地修改了哈米尔顿港形式主义，以实现港口超齐式的形式。我们表明，构造的网络能够按部分学习复杂系统的物理学，从而减轻了与这种系统的实验表征和后验学习过程相关的负担。但是，可以根据完整系统的规模进行预测。示例显示了所提出的技术的性能。
We develop inductive biases for the machine learning of complex physical systems based on the port-Hamiltonian formalism. To satisfy by construction the principles of thermodynamics in the learned physics (conservation of energy, non-negative entropy production), we modify accordingly the port-Hamiltonian formalism so as to achieve a port-metriplectic one. We show that the constructed networks are able to learn the physics of complex systems by parts, thus alleviating the burden associated to the experimental characterization and posterior learning process of this kind of systems. Predictions can be done, however, at the scale of the complete system. Examples are shown on the performance of the proposed technique.
