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Abstract Prediction of the spatial‐temporal dynamics of the fluid flow in complex subsurface systems, such as geologic storage, is typically performed using advanced numerical simulation methods that solve the underlying governing physical equations. However, numerical simulation is computationally demanding and can limit the implementation of standard field management workflows, such as model calibration and optimization. Standard deep learning models, such as RUNET, have recently been proposed to alleviate the computational burden of physics‐based simulation models. Despite their powerful learning capabilities and computational appeal, deep learning models have important limitations, including lack of interpretability, extensive data needs, weak extrapolation capacity, and physical inconsistency that can affect their adoption in practical applications. We develop a Fluid Flow‐based Deep Learning (FFDL) architecture for spatial‐temporal prediction of important state variables in subsurface flow systems. The new architecture consists of a physics‐based encoder to construct physically meaningful latent variables, and a residual‐based processor to predict the evolution of the state variables. It uses physical operators that serve as nonlinear activation functions and imposes the general structure of the fluid flow equations to facilitate its training with data pertaining to the specific subsurface flow application of interest. A comprehensive investigation of FFDL, based on a field‐scale geologic storage model, is used to demonstrate the superior performance of FFDL compared to RUNET as a standard deep learning model. The results show that FFDL outperforms RUNET in terms of prediction accuracy, extrapolation power, and training data needs. 

Biomanufacturing with broad applications in various industries is projected to reach a market value of ~30 trillion USD by 2030, accounting for more than one third of the global manufacturing output. Future biomanufacturing of industrial products will use novel synthetic biology tools and advanced bioprocesses to convert abundant biomass and waste resources into value-added products with comparable or superior properties to replace current petroleum-based products, thus enabling circular bioeconomy with affordable energy, economic growth, and innovation in renewable energy and chemicals production. However, biomanufacturing faces many challenges in its development that requires fundamental research in synthetic biology and novel bioprocesses involving multidisciplinary teams and academic-industry partnerships. In particular, aging and lifespan of microbial cells have been largely overlooked in industrial fermentation. Only recently have microbiologists realized that many microorganisms including yeasts (e.g., Saccharomyces cerevisiae) and bacteria (e.g., Escherichia coli) have chronological and replicative life spans which dramatically impact cell viability and longevity. In this article, we will give our perspective on how synthetic biology may contribute to overcoming some challenges facing industrial biotechnology for fuels and chemicals production from renewable sources, highlighting the importance of understanding and regulating microorganism’s lifespan and aging.