  A fundamental challenge in physics-informed machine learning (PIML) is the
design of robust PIML methods for out-of-distribution (OOD) forecasting tasks.
These OOD tasks require learning-to-learn from observations of the same (ODE)
dynamical system with different unknown ODE parameters, and demand accurate
forecasts even under out-of-support initial conditions and out-of-support ODE
parameters. In this work we propose a solution for such tasks, which we define
as a meta-learning procedure for causal structure discovery (including
invariant risk minimization). Using three different OOD tasks, we empirically
observe that the proposed approach significantly outperforms existing
state-of-the-art PIML and deep learning methods.
