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.

翻译：我们根据港-港-安密尔顿正规主义,为机器学习复杂的物理系统,发展了感应偏差。为了通过在学物理(节能、非阴性酶生产)中构建热力学原理,我们相应修改港-安密尔顿正规学,以达到港口对流学。我们表明,已建成的网络能够按部件学习复杂系统的物理学,从而减轻与这类系统的实验性定性和后代学习过程相关的负担。但是,可以在整个系统中进行预测,并举例说明了拟议技术的性能。