人工神经网络
计算机科学
开源
常微分方程
外推法
混合动力系统
人工智能
非线性系统
JSON文件
微分方程
机器学习
数据挖掘
软件
数学
数据库
量子力学
物理
数学分析
程序设计语言
作者
Kilian Merkelbach,Artur M. Schweidtmann,Younes Müller,Patrick Schwoebel,Adel Mhamdi,Alexander Mitsos,Andreas Schuppert,Thomas Mrziglod,Sebastian Schneckener
标识
DOI:10.1016/j.compchemeng.2022.107736
摘要
Hybrid modelling, i.e., the combination of data-driven modelling with mechanistic model components, reduces the data demand and enables extrapolation of data-driven models. However, building, training and evaluation of hybrid models is cumbersome with current frameworks. We developed HybridML, an open-source modeling platform, in which hybrid models can be trained, i.e., combinations of artificial neural networks, arithmetic expressions, and differential equations. We employ TensorFlow for artificial neural network training and Casadi to integrate ordinary differential equations and provide gradients of differential model equations enabling continuous time representations. HybridML provides also a JSON interface for the model development. We apply HybridML to an industrial case study, in which the trained model is used to predict drug concentrations over time, based on physiological information about the patients. To demonstrate its versatility, we also present a nonlinear application, where HybridML is used to model the spread of the COVID-19 pandemic in German federal states based on the state’s socio-economic attributes.
科研通智能强力驱动
Strongly Powered by AbleSci AI