  We propose a novelty approach to connect machine learning to causal structure
learning by jacobian matrix of neural network w.r.t. input variables. In this
paper, we extend the jacobian-based approach to physical system which is the
method human explore and reason the world and it is the highest level of
causality. By functions fitting with Neural ODE, we can read out causal
structure from functions. This method also enforces a important acylicity
constraint on continuous adjacency matrix of graph nodes and significantly
reduce the computational complexity of search space of graph.
