AI-Aristotle: A physics-informed framework for systems biology gray-box identification
Fig 2
AI-Aristotle framework for gray-box identification: 1. The observed data and the partial knowledge of physics are used to train the selected neural network-based module. 2. The selection of the neural networks-based module needs to be done between (a) X-TFC, recommended for high-resolution data and missing terms discovery, and (b) PINN, recommended for sparse data and parameter estimation. The neural network outputs are the time-dependent representations of the missing terms of the dynamical systems, which are fed into the symbolic regression algorithm. 3. The selected Symbolic Regression module identifies the mathematical expressions of the missing terms. It is recommended to use both symbolic regressors for cross-validation. 4. The full knowledge of physics is now available, allowing forward modeling performance.