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Abstract Industrial ecosystems are coupled with natural systems, which causes the material flow dynamics in the network to be affected by the mechanistic dynamics of each node. However, current material flow dynamics studies do not capture these mechanistic and nonlinear dynamics to evaluate material flows in networks, thus missing its role in designing resilient industrial ecosystems. In this work, we present a methodology to overcome this limitation and model material flow dynamics in a coupled natural‐industrial network by accounting for underlying nonlinear dynamics at each node. We propose a three‐step methodology: first, creating accurate surrogate models using liquid time‐constant (LTC) neural networks to capture node‐specific behavior; second, coupling these individual node models to simulate material flow dynamics in the network; and third, evaluating resilience by measuring the system's ability to maintain production levels under climate stress. Applied to a soybean‐based biodiesel production network in Champaign County, Illinois (2006–2096), our analysis reveals significant vulnerability differences between climate scenarios, with the RCP 8.5 scenario triggering production failures approximately 10 years earlier than RCP 4.5 (2016 vs. 2026), exhibiting higher failure frequency and requiring longer recovery periods. Smaller farms (450 ha) demonstrated substantially higher import dependency, while medium farms (500 ha) reached a critical bifurcation point around 2050 under RCP 8.5, indicating a systemic tipping point. These findings provide insights for policymakers and industrial managers to implement targeted interventions, supply chain diversification, and adaptive management strategies, thereby enhancing system resilience while offering industrial ecology practitioners a methodology for modeling material flow dynamics in a coupled natural‐industrial network. 

Machine learning presents opportunities to improve the scale-specific accuracy of mechanistic models in a data-driven manner. Here we demonstrate the use of a machine learning technique called Sparse Identification of Nonlinear Dynamics (SINDy) to improve a simple mechanistic model of algal growth. Time-series measurements of the microalga Chlorella Vulgaris were generated under controlled photobioreactor conditions at the University of Technology Sydney. A simple mechanistic growth model based on intensity of light and temperature was integrated over time and compared to the time-series data. While the mechanistic model broadly captured the overall growth trend, discrepancies remained between the model and data due to the model's simplicity and non-ideal behavior of real-world measurement. SINDy was applied to model the residual error by identifying an error derivative correction term. Addition of this SINDy-informed error dynamics term shows improvement to model accuracy while maintaining interpretability of the underlying mechanistic framework. This work demonstrates the potential for machine learning techniques like SINDy to aid simple mechanistic models in scale-specific predictive accuracy.