Nature

Physics-informed attention-based neural network for hyperbolic partial differential equations: application to the Buckley–Leverett problem

Physics-informed neural networks (PINNs) have enabled significant improvements in modelling physical processes described by partial differential equations (PDEs) and are in principle capable of ...
Nature

Sparse inference and active learning of stochastic differential equations from data

Throughout the natural sciences, mathematical models are frequently formulated as differential equations. For example, with stochastic, ordinary, and partial differential equations (SDEs, ODEs, and ...