Homogenization-Informed Convolutional Neural Networks for Estimation of Li-ion Battery Effective Properties

Weber, Ross M.; Korneev, Svyatoslav; Battiato, Ilenia

doi:10.1007/s11242-022-01862-9

Cited by 6 publications

(2 citation statements)

References 75 publications

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“…The first works in this perspective are found in the energy storage field. [14,15] In the production process of electrodes for batteries, researchers and technicians must follow many steps, each of them characterized by different operating conditions to be set. Knowing in advance the quantitative effect of operating conditions on a final desired property can decrease the trial-and-error phase and the cost of the experiments.…”

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confidence: 99%

Reconciling deep learning and first‐principle modelling for the investigation of transport phenomena in chemical engineering

2023

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The use of machine learning in chemical engineering has the potential to greatly improve the design and analysis of complex systems. However, there are also risks associated with its adoption, such as the potential for bias in algorithms and the need for careful oversight to ensure the safety and reliability of machine learningpowered systems. This paper explores the opportunities and risks of using machine learning in chemical engineering and provides a perspective on how it may be integrated into engineering practices in a responsible and effective manner. We generated the text of this abstract with GPT-3, OpenAI's large-scale languagegeneration model. Upon generating the draft, we ensured that the language was to our liking, and we take ultimate responsibility for the content of this publication.

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Section: Some Good Reasons Why You Should Give It a Trymentioning

confidence: 99%

Reconciling deep learning and first‐principle modelling for the investigation of transport phenomena in chemical engineering

2023

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“…The application type determines the choice of the method 8 . The fields of applications concern diverse domains such as assessment of the tortuosity of extracellular space in brain studies 5,9 , analysis of the diffusion in Li-ion batteries 10 , sandy sediments 2 and studies of hierarchical porous materials 11 .…”

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Deep learning for diffusion in porous media

Graczyk

Strzelczyk

Matyka

2023

Sci Rep

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We adopt convolutional neural networks (CNN) to predict the basic properties of the porous media. Two different media types are considered: one mimics the sand packings, and the other mimics the systems derived from the extracellular space of biological tissues. The Lattice Boltzmann Method is used to obtain the labeled data necessary for performing supervised learning. We distinguish two tasks. In the first, networks based on the analysis of the system’s geometry predict porosity and effective diffusion coefficient. In the second, networks reconstruct the concentration map. In the first task, we propose two types of CNN models: the C-Net and the encoder part of the U-Net. Both networks are modified by adding a self-normalization module [Graczyk et al. in Sci Rep 12, 10583 (2022)]. The models predict with reasonable accuracy but only within the data type, they are trained on. For instance, the model trained on sand packings-like samples overshoots or undershoots for biological-like samples. In the second task, we propose the usage of the U-Net architecture. It accurately reconstructs the concentration fields. In contrast to the first task, the network trained on one data type works well for the other. For instance, the model trained on sand packings-like samples works perfectly on biological-like samples. Eventually, for both types of the data, we fit exponents in the Archie’s law to find tortuosity that is used to describe the dependence of the effective diffusion on porosity.

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A deep learning upscaling framework: Reactive transport and mineral precipitation in fracture-matrix systems

Wang,

Battiato

2024

Advances in Water Resources

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Homogenization-Informed Convolutional Neural Networks for Estimation of Li-ion Battery Effective Properties

Cited by 6 publications

References 75 publications

Reconciling deep learning and first‐principle modelling for the investigation of transport phenomena in chemical engineering

Reconciling deep learning and first‐principle modelling for the investigation of transport phenomena in chemical engineering

Deep learning for diffusion in porous media

A deep learning upscaling framework: Reactive transport and mineral precipitation in fracture-matrix systems

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