  In this work, we propose an end-to-end graph network that learns forward and
inverse models of particle-based physics using interpretable inductive biases.
Physics-informed neural networks are often engineered to solve specific
problems through problem-specific regularization and loss functions. Such
explicit learning biases the network to learn data specific patterns and may
require a change in the loss function or neural network architecture hereby
limiting their generalizabiliy. Our graph network is implicitly biased by
learning to solve several tasks, thereby sharing representations between tasks
in order to learn the forward dynamics as well as infer the probability
distribution of unknown particle specific properties. We evaluate our approach
on one-step next state prediction tasks across diverse datasets. Our comparison
against related data-driven physics learning approaches reveals that our model
is able to predict the forward dynamics with at least an order of magnitude
higher accuracy. We also show that our approach is able to recover multi-modal
probability distributions of unknown physical parameters.
