Learning Large Scale Ordinary Differential Equation Systems

Understand

Learning large scale nonlinear ordinary differential equation (ODE) systems from data is known to be computationally and statistically challenging.

• We present a framework together with the adaptive integral matching (AIM) algorithm for learning polynomial or rational ODE systems with a sparse network structure.
• The framework allows for time course data sampled from multiple environments representing e.g.
• different interventions or perturbations of the system.