[2509.20349] Process-Informed Forecasting of Complex Thermal Dynamics in Pharmaceutical Manufacturing

Computer Science > Machine Learning arXiv:2509.20349 (cs) [Submitted on 24 Sep 2025 (v1), last revised 4 Apr 2026 (this version, v2)] Title:Process-Informed Forecasting of Complex Thermal Dynamics in Pharmaceutical Manufacturing Authors:Ramona Rubini, Siavash Khodakarami, Aniruddha Bora, George Em Karniadakis, Michele Dassisti View a PDF of the paper titled Process-Informed Forecasting of Complex Thermal Dynamics in Pharmaceutical Manufacturing, by Ramona Rubini and 4 other authors View PDF HTML (experimental) Abstract:Accurate time-series forecasting for complex physical systems is the backbone of modern industrial monitoring and control, yet deep learning models often lack the physical consistency required in regulated environments. To bridge this gap, we introduce Process-Informed Forecasting (PIF) models for temperature in pharmaceutical lyophilization, embedding deterministic production recipes as macro-structural priors. We investigate classical methods (e.g., Autoregressive Integrated Moving Average (ARIMA) model) and modern deep learning architectures, including Kolmogorov-Arnold Networks (KANs). We compare three different loss function formulations that integrate a process-informed trajectory prior: a fixed-weight loss, a dynamic uncertainty-based loss, and a Residual-Based Attention (RBA) mechanism. We evaluate all models not only for accuracy and physical consistency but also for robustness to sensor noise. Furthermore, we test the practical generalizability of ...