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

arXiv:2608.26549 · math.NA, cs.AI, cs.LG, cs.NA, cs.SY, eess.SY · Submitted 2026-08-27 · Read on arXiv

math.NA, cs.AI, cs.LG, cs.NA, cs.SY, eess.SY

Submitted: 2026-08-27

Updated: 2026-08-27

Terminology

• Accuracy — 21× in this paper · explained in 1 episode
• Inverse Problem — 17× in this paper · explained in 2 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Activation Function — 5× in this paper · explained in 1 episode
• Core — 5× in this paper · explained in 2 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Computational overhead — 3× in this paper · explained in 2 episodes
• Physical Constraints — 3× in this paper · explained in 7 episodes
• Physics-Informed Stochastic Configuration Machine — 3× in this paper
• Objective Function — 2× in this paper · explained in 2 episodes
• Physics-Informed Neural Networks (PINNs) — 2× in this paper · explained in 6 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Adaptive Selection — 1× in this paper · explained in 1 episode
• Decoupling — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Multi-objective Optimization — 1× in this paper · explained in 4 episodes
• Ordinary Differential Equations (ODEs) — 1× in this paper · explained in 2 episodes
• Physics-Informed Stochastic Configuration Network — 1× in this paper
• Redundancy — 1× in this paper · explained in 2 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes

Sources

• An Expert's Guide to Training Physics-informed Neural Networks
• Least-Squares Multi-Step Koopman Operator Learning for Model Predictive Control

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