Variational Boosting for Physics-Informed Neural Networks
Physics-Informed Neural Networks (PINNs) solve differential equations by minimizing the residual of a nonlinear operator over a neural parameterization of the solution. However, monolithic PINNs often suffer from ill-conditioning, spectral bias, and optimization instability. We introduce a variational boosting framework in which solutions are constructed additively in function space. Each stage trains a weak learner whose converged correction satisfies a local orthogonality condition, equivalent to a projected functional gradient descent step onto the tangent space of the network's function manifold. Because each correction network is deliberately small, the restricted minimization admits...
| Authors | Kaylee Vo, Pavlos Protopapas |
|---|---|
| Primary category | cs.LG |
| Published | 27 July 2026 |
| Last updated at source | 28 September 2026 |
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