We enhance machine learning algorithms for learning model parameters in complex systems represented by ordinary differential equations (ODEs) with domain decomposition methods. The study evaluates the performance of two approaches, namely (vanilla) Physics-Informed Neural Networks (PINNs) and Finite Basis Physics-Informed Neural Networks (FBPINNs), in learning the dynamics of test models with a quasi-stationary longtime behavior. We test the approaches for data sets in different dynamical regions and with varying noise level. As results, we find a better performance for the FBPINN approach compared to the vanilla PINN approach, even in cases with data from only a quasi-stationary time domain with few dynamics.

翻译：本研究通过域分解方法，增强了用于学习常微分方程所表征复杂系统中模型参数的机器学习算法。该研究评估了两种方法——即（基础型）物理信息神经网络与有限基物理信息神经网络——在学习具有准稳态长期行为的测试模型动力学时的性能。我们在不同动力学区域及不同噪声水平的数据集上对这两种方法进行了测试。结果表明，与基础型PINN方法相比，FBPINN方法表现出更优的性能，即使在数据仅来自动力学信息稀少的准稳态时间域的情况下亦是如此。