Exactly conservative physics-informed neural networks and deep operator networks for dynamical systems

We introduce a method for training exactly conservative physics-informed neural networks and physics-informed deep operator networks for dynamical systems, that is, for ordinary differential equations. The method employs a projection-based technique that maps a candidate solution learned by the neural network solver for any given dynamical system possessing at least one first integral onto an invariant manifold. We illustrate that exactly conservative physics-informed neural network solvers and physics-informed deep operator networks for dynamical systems vastly outperform their non-conservative counterparts for several real-world problems from the mathematical sciences.