Training Physics-Informed Neural Networks (PINNs) on stiff time-dependent PDEs remains highly unstable. Through rigorous ablation studies, we identify a surprisingly critical factor: the enforcement of initial conditions. We present the first systematic ablation of two core strategies, hard initial-condition constraints and adaptive loss weighting. Across challenging benchmarks (sharp transitions, higher-order derivatives, coupled systems, and high frequency modes), we find that exact enforcement of initial conditions (ICs) is not optional but essential. Our study demonstrates that stability and efficiency in PINN training fundamentally depend on ICs, paving the way toward more reliable PINN solvers in stiff regimes.

翻译：在刚性时间依赖偏微分方程上训练物理信息神经网络（PINNs）仍存在高度不稳定性。通过严格的消融研究，我们发现了一个至关重要的因素：初始条件的施加方式。我们首次系统性地对比分析了两种核心策略——硬初始条件约束与自适应损失加权。在多个具有挑战性的基准测试（急剧过渡、高阶导数、耦合系统及高频模态）中，我们发现精确施加初始条件并非可选项，而是必要条件。本研究表明，PINN训练的稳定性与效率根本上取决于初始条件的处理方式，这为在刚性区域构建更可靠的PINN求解器奠定了基础。