Physics-Informed Stochastic Configuration Machine: A Backpropagation-Free Neural Network with Fast Training involving Nonlinear Differential Equations

While Physics-Informed Neural Networks (PINNs) have emerged as a transformative paradigm for solving complex differential equations, their reliance on backpropagation-based gradient descent and automatic differentiation (AD) imposes significant computational bottlenecks and severe non-convex optimization challenges. To overcome these fundamental limitations, we propose the Physics-Informed Stochastic Configuration Machine (PI-SCM), a novel backpropagation-free framework for both forward and inverse problems in differential equations. The core mathematical contribution lies in the analytical evaluation of local Jacobians for nonlinear differential operators, which facilitates a linearized representation of the physical loss and projects it into a unified, linearized algebraic subspace. This reformulation allows for the explicit determination of network weights via a sequence of linearized least-squares problems, effectively bypassing the iterative traps of traditional nonlinear optimizers. We develop a progressive algorithmic suite comprising localized construction (PI-SC-I), sliding-window correction (PI-SC-II), and global correction (PI-SC-III), and establish their universal approximation properties. Extensive experiments demonstrate that PI-SCM achieves robust parameter identification and substantially higher predictive accuracy than the PIELM baselines while accelerating training by orders of magnitude compared with standard PINNs. Our work provides a highly efficient and scalable foundation for next-generation, real-time Scientific Machine Learning applications.