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A reliable and continuous assessment of the degradation state of industrial water electrolyzers is crucial for maintenance planning and dispatch optimization, thus facilitating risk management for both suppliers and operators. Although voltage is a widely used and easily measurable degradation indicator, its effectiveness is compromised in industrial settings due to the impact of arbitrary operating conditions. Existing methods to correct the impact of operating conditions often rely on measuring characteristic curves, which typically only provide a single‐dimensional correction and do not allow varying corrections over time. We propose a data‐driven method for degradation state quantification that adjusts the measured voltage under arbitrary operating conditions to a reference condition, using an empirical voltage model and degradation history from a fleet of electrolyzers. This method involves fitting the empirical voltage model for each time series segment and calculating the voltage under the reference condition. To assist model fitting under limited data coverage, the method utilizes a Bayesian approach to incorporate fleet knowledge–an aggregation of the degradation trajectories of the electrolyzer fleet. This method was validated using both synthetic data and operation data from 12 industrial electrolyzers with 1–3 years of operation history, including in‐depth sensitivity analyses on the data coverage, fleet–target discrepancy, and fleet size. Results proved the superiority of the proposed fleet‐based method over the benchmark method without using fleet knowledge.

Numerical weather prediction models require ever‐growing computing time and resources but, still, have sometimes difficulties with predicting weather extremes. We introduce a data‐driven framework that is based on analog forecasting (prediction using past similar patterns) and employs a novel deep learning pattern‐recognition technique (capsule neural networks, CapsNets) and an impact‐based autolabeling strategy. Using data from a large‐ensemble fully coupled Earth system model, CapsNets are trained on midtropospheric large‐scale circulation patterns (Z500) labeled 0–4 depending on the existence and geographical region of surface temperature extremes over North America several days ahead. The trained networks predict the occurrence/region of cold or heat waves, only using Z500, with accuracies (recalls) of 69–45% (77–48%) or 62–41% (73–47%) 1–5 days ahead. Using both surface temperature and Z500, accuracies (recalls) with CapsNets increase to ∼80% (88%). In both cases, CapsNets outperform simpler techniques such as convolutional neural networks and logistic regression, and their accuracy is least affected as the size of the training set is reduced. The results show the promises of multivariate data‐driven frameworks for accurate and fast extreme weather predictions, which can potentially augment numerical weather prediction efforts in providing early warnings. Key Points A data‐driven extreme weather prediction framework based on analog forecasting and deep learning pattern‐recognition methods is proposed Extreme surface temperature events over North America are skillfully predicted using only midtropospheric large‐scale circulation patterns More advanced deep learning methods are found to yield better forecasts, encouraging novel methods tailored for climate/weather data

This article proposes a data‐driven model predictive control (MPC) method for multirotor collision avoidance, considering uncertainties and the unknown dynamics caused by a payload. To address this challenge, sparse identification of nonlinear dynamics (SINDy) is employed to derive the governing equations of the multirotor system. SINDy is capable of discovering the equations of target systems from limited data, under the assumption that a few dominant functions primarily characterize the system's behavior. In addition, a data collection framework that combines a baseline controller with MPC is proposed to generate diverse trajectories for model identification. A candidate function library, informed by prior knowledge of multirotor dynamics, along with a normalization technique, is utilized to enhance the accuracy of the SINDy‐based model. Using data‐driven model from SINDy, MPC is used to achieve accurate trajectory tracking while satisfying state and input constraints, including those for obstacle avoidance. Simulation results demonstrate that SINDy can successfully identify the governing equations of the multirotor system, accounting for mass parameter uncertainties and aerodynamic effects. Furthermore, the results confirm that the proposed method outperforms conventional MPC, which suffers from parameter uncertainty and an unknown aerodynamic model, in both obstacle avoidance and trajectory tracking performance. Sparse identification of nonlinear dynamics‐based model predictive control is proposed for multirotor collision avoidance.

Bioreactor scale‐up and scale‐down have always been a topical issue for the biopharmaceutical industry and despite considerable effort, the identification of a fail‐safe strategy for bioprocess development across scales remains a challenge. With the ubiquitous growth of digital transformation technologies, new scaling methods based on computer models may enable more effective scaling. This study aimed to evaluate the potential application of machine learning (ML) algorithms for bioreactor scale‐up, with a specific focus on the prediction of scaling parameters. Factors critical to the development of such models were identified and data for bioreactor scale‐up studies involving CHO cell‐generated mAb products collated from the literature and public sources for the development of unsupervised and supervised ML models. Comparison of bioreactor performance across scales identified similarities between the different processes and primary differences between small‐ and large‐scale bioreactors. A series of three case studies were developed to assess the relationship between cell growth and scale‐sensitive bioreactor features. An embedding layer improved the capability of artificial neural network models to predict cell growth at a large‐scale, as this approach captured similarities between the processes. Further models constructed to predict scaling parameters demonstrated how ML models may be applied to assist the scaling process. The development of data sets that include more characterization data with greater variability under different gassing and agitation regimes will also assist the future development of ML tools for bioreactor scaling. Lay summary: This study examined the potential of machine learning to assist in bioreactor scale‐up. The findings demonstrated the capability of these algorithms to uncover complex non‐linear relationships among scale‐sensitive features, transfer knowledge, and predict process performance across scales. A method for predicting scaling factors for equivalent performance across scales was also developed and the characteristics of ideal datasets for future application of machine learning to scaling described.

Complex industrial processes usually exhibit multimode characteristics, meaning that statistical features of process data, such as mean, variance, and correlation, vary across different modes. Extracting critical information from these distinct modes can significantly enhance the accuracy and robustness of data‐driven models in process monitoring, condition evaluation, and quality improvement. Consequently, the multimode identification of industrial data becomes a paramount concern in data‐driven modelling. However, existing methods for multimode identification require prior knowledge to predetermine the number of modes and struggle to describe the similarity between high‐dimensional samples effectively. To address this issue, this study introduces an automatic multimode identification method based on complex network community detection. In this approach, each data sample is considered as a node, and manifold similarity is calculated to construct the complex network model. The method leverages weighted geodesic distances to capture the data's manifold structure and potential density, enabling better distinction between high‐dimensional samples in different modes. The greedy search algorithm with modularity maximisation is employed to partition nodes into modes without manual selection of the number of modes. Furthermore, a node degree‐based indicator is developed for online mode monitoring. Experimental studies on two examples demonstrate the effectiveness of the proposed method in uncovering multimode characteristics of complex industrial processes, highlighting its promising application potential. Extracting critical information from different modes can significantly improve the accuracy and robustness of data‐driven models in process monitoring, condition evaluation, and quality improvement. However, the existing multimode identification methods rely on prior knowledge to determine the number of modes in advance and cannot describe the similarity between high‐dimensional samples well. Therefore, a novel mode identification method is proposed based on a complex network to overcome mode number selection's difficulty.

With the recent advances in machine learning (ML) and artificial intelligence (AI), data‐driven modeling approaches for pharmacokinetics (PK) and pharmacodynamics (PD) have gained popularity due to their versatility in diverse settings and reduced reliance on prior assumptions. However, most of the ML methods ignore the hidden dynamics behind the data, lacking interpretability. This study investigated the applicability of neural controlled differential equation (NCDE), a novel ML method that is suitable for data‐driven modeling of PK and PD profiles, especially in the setting of multiple dosing. We demonstrated that NCDE was capable of combining differential‐equation‐based dynamics with data‐driven characteristics, flexibly incorporating various types of inputs, and embedding discontinuous dynamics. Moreover, a direct correspondence was identified between the learned dynamics of NCDE and the dynamics behind the data, which highlights the intrinsic interpretability of NCDE. Additionally, the influence of important hyperparameters was systematically investigated, and it was found that L1 regularization and the AdaMax optimizer were useful for stabilizing the training process and leading to a generalizable NCDE model. Together, these findings demonstrate the accuracy, generalizability, and interpretability of NCDE, indicating that NCDE is a reliable method for further application. In the future, NCDE may further facilitate PK and PD prediction in general. Neural controlled differential equations (NCDE), driven by control variables, are capable to learn the discontinuous dynamics in the PK and PD datasets.

We present a comprehensive inter‐comparison of linear regression (LR), stochastic, and deep‐learning approaches for reduced‐order statistical emulation of ocean circulation. The reference data set is provided by an idealized, eddy‐resolving, double‐gyre ocean circulation model. Our goal is to conduct a systematic and comprehensive assessment and comparison of skill, cost, and complexity of statistical models from the three methodological classes. The model based on LR is considered as a baseline. Additionally, we investigate its additive white noise augmentation and a multi‐level stochastic approach, deep‐learning methods, hybrid frameworks (LR plus deep‐learning), and simple stochastic extensions of deep‐learning and hybrid methods. The assessment metrics considered are: root mean squared error, anomaly cross‐correlation, climatology, variance, frequency map, forecast horizon, and computational cost. We found that the multi‐level linear stochastic approach performs the best for both short‐ and long‐timescale forecasts. The deep‐learning hybrid models augmented by additive state‐dependent white noise came second, while their deterministic counterparts failed to reproduce the characteristic frequencies in climate‐range forecasts. Pure deep learning implementations performed worse than LR and its simple white noise augmentation. Skills of LR and its white noise extension were similar on short timescales, but the latter performed better on long timescales, while LR‐only outputs decay to zero for long simulations. Overall, our analysis promotes multi‐level LR stochastic models with memory effects, and hybrid models with linear dynamical core augmented by additive stochastic terms learned via deep learning, as a more practical, accurate, and cost‐effective option for ocean emulation than pure deep‐learning solutions. Plain Language Summary In weather and climate predictions, scientists use comprehensive ocean circulation models for representing the effects of the oceans on the atmosphere. These models simulate the three‐dimensional ocean dynamics using millions of variables and, thus, require significant computational resources and running time. Therefore, there is a need for low‐cost, data‐driven ocean models with fewer variables that can reproduce essential oceanic circulations with reasonable accuracy. There are several popular data‐driven approaches to build these models, but singling out the best one is difficult and significantly understudied. We have systematically assessed and compared the accuracy, stability, and computational cost of various data‐driven models against the linear regression—a fundamental and easy‐to‐implement deterministic model, that is, it provides a fixed output for a fixed input. We considered several stochastic and deep‐learning models for comparison; stochastic models combine a deterministic model with customized noise, whereas deep‐learning models train a complex network of neurons similar to the human brain. We found that the stochastic models that properly include the core dynamics, time‐delay effects, and model errors perform the best. The core dynamics provides the essential changes, time‐delay effects are the changes due to correlation between successive ocean states, and model errors provide other possible causes of changes. Key Points The multi‐level stochastic approach produces the most stable, accurate, and low‐cost emulator of a double‐gyre ocean model solution Artificial neural networks and long short term memory work better in a hybrid form with linear regression, providing the core dynamics, than in their standalone application Emulators incorporating memory effects and state‐dependent noise show enhanced performance and deep learning can learn these effects

Generative Artificial Intelligence (AI) frameworks, such as Variational Autoencoders (VAEs), have proven powerful in learning structured representations from complex, high‐dimensional data. In pharmacometrics (PMX), nonlinear mixed effects (NLME) modeling is widely used to capture inter‐individual variability and link covariates to characterize parameters with the goal of informing key decisions in drug research and development. This research combines the strengths of both approaches by introducing a VAE framework specifically designed for NLME modeling. The proposed method integrates the flexibility of generative AI with the interpretability and robustness of mechanism‐based PMX modeling. To advance covariate selection in PMX, we replace the Evidence Lower Bound objective in VAEs with an objective function based on the corrected Bayesian information criterion. This enables the simultaneous evaluation of all potential covariate‐parameter combinations, thereby allowing for automated and joint estimation of population parameters and covariate selection within a single run. Manual selection and repeated model fitting across covariate combinations are no longer required. We demonstrate the effectiveness of this combined AI‐PMX approach with two representative cases. As the first generative AI‐based optimization method for NLME modeling, the VAE achieves high‐quality results in a single run, outperforming traditional stepwise procedures in terms of efficiency. As such, the presented approach facilitates automated model development, advancing PMX and its applications in model‐informed drug development.

Lyophilization, aka freeze drying, is a key process used in the production of biotherapeutic products. The optimization of lyophilization formulations and operations is a slow process that could be accelerated by on‐line analytics. In recent years, hyperspectral imaging (HSI) has garnered increasing attention from both academia and industry in biopharmaceutical and food engineering fields. As a non‐invasive, rapid, non‐destructive, accurate, and automated tool that combines advantages from both spectroscopy and imaging techniques, HSI holds significant potential for analyzing and optimizing lyophilization processes and products. However, the huge and information‐rich datasets generated from HSI are difficult to be modeled and interpreted properly. This article reviews and discusses the literature on the application of HSI on lyophilization, and the strategies that use the resulting data to build models. Such strategies include preprocessing, spectral unmixing, classification and regression, and data fusion. From the data modeling and application perspectives, the current challenges and future prospects regarding HSI techniques for lyophilization are addressed. This article is intended to provide guidance and insights for non‐specialist researchers and engineers into leveraging HSI and the data‐driven modeling strategies for addressing a wide range of lyophilization‐related challenges. Lyophilization is a key process used in the production of biotherapeutic products. This article reviews and discusses the application of HSI on lyophilization, and the strategies that use the resulting data to build models. It is intended to provide guidance and insights for non‐specialist researchers and engineers into leveraging HSI and the data‐driven modeling strategies for addressing lyophilization‐related challenges.

Data‐driven modeling in material science rose to prominence in the last decade, and various supervised and unsupervised machine learning techniques have been employed for material development and deriving insights for decision‐making purposes. In this context, machine learning can have prominent importance in the field of nanostructured aerogels for accelerated materials design and material properties prediction. Current attempts rely only on experimental approach, which have inherent shortcomings, including inefficiency due to the prolonged synthesis process, and necessity of analyzing microstructure and properties. In order to address the challenges associated with the traditional experimental approach, in this study, an artificial neural network (ANN) is employed to predict the material properties of nanostructured aerogels. Polyimide (PI) organic aerogels are selected for this purpose. Through understanding the contributing material and processing factors in PI aerogel synthesis, a dataset is prepared. Data preprocessing is performed, and through hyperparameter tuning, ANN is constructed and trained for a given dataset. Various material properties are predicted, including compressive modulus, density, and porosity. Results show that ANN is trained with high accuracy, which demonstrates the versatility and accuracy of model in materials properties prediction. This study can therefore pave the way for establishing a platform for data‐driven materials innovation. This study focused on the development of a machine learning predictive model as a new toolbox to mitigate costs, risks, and time associated with the traditional experimental approach in aerogel materials fabrication. To achieve this objective, an artificial neural network model was constructed with optimized topology. Various properties of aerogels including compressive modulus, density, and porosity were accurately predicted and a procedure for the use of model for novel aerogel materials development is recommended.