Hard-Constrained Invariant-Preserving Neural Networks for Conservative Dynamical Systems: A Statistically Validated Approach
Year: 2026
Type: software
Abstract: This version (v2.0) introduces hard structural constraints via multiplicative manifold projection, achieving 72.2% improvement in invariant preservation (p < 1e-11, Cohen's d = 5.03) compared to baseline, and 66.9% improvement over soft penalty methods. Includes three-way statistical comparison: Baseline PINN vs. Soft IP-PINN vs. Hard IP-PINN.
Author: OUAAR, Fatima
Institution: University of Biskra
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7127614129