Accurate modeling of drug release is essential for designing and developing controlled-release systems. Classical models (Fick, Higuchi, Peppas) rely on simplifying assumptions that limit their accuracy in complex geometries and release mechanisms. Here, we propose a novel approach using Physics-Informed Neural Networks (PINNs) and Bayesian PINNs (BPINNs) for predicting release from planar, 1D-wrinkled, and 2D-crumpled films. This approach uniquely integrates Fick's diffusion law with limited experimental data to enable accurate long-term predictions from short-term measurements, and is systematically benchmarked against classical drug release models. We embedded Fick's second law into PINN as loss with 10,000 Latin-hypercube collocation points and utilized previously published experimental datasets to assess drug release performance through mean absolute error (MAE) and root mean square error (RMSE), considering noisy conditions and limited-data scenarios. Our approach reduced mean error by up to 40% relative to classical baselines across all film types. The PINN formulation achieved RMSE <0.05 utilizing only the first 6% of the release time data (reducing 94% of release time required for the experiments) for the planar film. For wrinkled and crumpled films, the PINN reached RMSE <0.05 in 33% of the release time data. BPINNs provide tighter and more reliable uncertainty quantification under noise. By combining physical laws with experimental data, the proposed framework yields highly accurate long-term release predictions from short-term measurements, offering a practical route for accelerated characterization and more efficient early-stage drug release system formulation.

翻译：药物释放的精确建模对于设计和开发控释系统至关重要。经典模型（Fick、Higuchi、Peppas）依赖于简化假设，这限制了其在复杂几何形状和释放机制中的准确性。本文提出了一种利用物理信息神经网络（PINNs）和贝叶斯物理信息神经网络（BPINNs）预测平面、一维褶皱和二维褶皱薄膜释放的新方法。该方法独特地将菲克扩散定律与有限的实验数据相结合，从而能够基于短期测量进行准确的长期预测，并与经典药物释放模型进行了系统性基准测试。我们将菲克第二定律作为损失函数嵌入PINN，使用了10,000个拉丁超立方配置点，并利用先前发表的实验数据集，通过平均绝对误差（MAE）和均方根误差（RMSE）来评估药物释放性能，同时考虑了噪声条件和有限数据场景。在所有薄膜类型中，我们的方法相对于经典基线模型将平均误差降低了高达40%。对于平面薄膜，PINN模型仅利用释放时间数据的前6%（减少了实验所需释放时间的94%）就实现了RMSE <0.05。对于褶皱和褶皱薄膜，PINN在33%的释放时间数据内达到了RMSE <0.05。BPINNs在噪声条件下提供了更紧密和更可靠的不确定性量化。通过将物理定律与实验数据相结合，所提出的框架能够从短期测量中获得高度准确的长期释放预测，为加速表征和更高效的早期药物释放系统配方提供了一条实用途径。