A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks
This work solves initial value PDEs for researchers and practitioners in computational science, offering a stable and scalable alternative to classical solvers, though it is incremental as it builds on existing ODE-based approaches.
The paper tackled the problem of solving initial value PDEs with neural networks by addressing catastrophic forgetting and scalability issues in existing methods, resulting in Neural IVP, which prevents ill-conditioning and scales linearly with parameters, enabling evolution of challenging PDE dynamics.
Unlike conventional grid and mesh based methods for solving partial differential equations (PDEs), neural networks have the potential to break the curse of dimensionality, providing approximate solutions to problems where using classical solvers is difficult or impossible. While global minimization of the PDE residual over the network parameters works well for boundary value problems, catastrophic forgetting impairs the applicability of this approach to initial value problems (IVPs). In an alternative local-in-time approach, the optimization problem can be converted into an ordinary differential equation (ODE) on the network parameters and the solution propagated forward in time; however, we demonstrate that current methods based on this approach suffer from two key issues. First, following the ODE produces an uncontrolled growth in the conditioning of the problem, ultimately leading to unacceptably large numerical errors. Second, as the ODE methods scale cubically with the number of model parameters, they are restricted to small neural networks, significantly limiting their ability to represent intricate PDE initial conditions and solutions. Building on these insights, we develop Neural IVP, an ODE based IVP solver which prevents the network from getting ill-conditioned and runs in time linear in the number of parameters, enabling us to evolve the dynamics of challenging PDEs with neural networks.