Image inversion and uncertainty quantification for constitutive laws of pattern formation

Zhao, Hongbo; Braatz, Richard D.; Bazant, Martin Z.

doi:10.1016/j.jcp.2021.110279

Cited by 16 publications

(24 citation statements)

References 75 publications

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“…State-of-the-art ba ery parameterization most o en requires a complicated calculation of the parameter-output-gradient [18,24]. For example, Sethurajan et al [18] and Zhao et al [24] conduct microscopic imaging of electrolyte concentration profiles. They then apply gradient-based iterative optimization to di usion equations to extract electrolyte properties.…”

Section: Discussionmentioning

confidence: 99%

“…The Bayesian analysis of Sethurajan et al [18] and Zhao et al [24] is preceded by a gradient-based optimization. EP-BOLFI is stable enough not to require this step, drastically cu ing down on the implementation e ort while incorporating a more expansive parameter space into the uncertainty estimate.…”

Section: Discussionmentioning

confidence: 99%

“…There, a Neural Network or other stochastical classifier gets fi ed to a physics-based model and is then used instead in an optimizer [20][21][22][23]. Imaging techniques also give essential input parameters for spatially resolved simulations and have to concern themselves with the uncertainty propagation from imaging to simulation outcome [24][25][26][27][28]. The State-of-Health estimation of degradation modes may also benefit from a physical understanding of the degradation processes involved [29].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bayesian Parameterization of Continuum Battery Models from Featurized Electrochemical Measurements Considering Noise

Yannick¹,

Wolf²,

Latz³

et al. 2022

Preprint

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Physico-chemical continuum ba ery models can predict the performance of new materials and cell geometries. The physical model parameters are typically determined by manually fi ing to experimental data. However, this process relies on the individual expertise of researchers. In this article, we introduce a computer algorithm that directly utilizes the experience of ba ery researchers to extract information from experimental data reproducibly. We extend Bayesian Optimization (BOLFI) with Expectation Propagation (EP) to create a black-box optimizer suited for modular continuum ba ery models. Standard approaches compare the experimental data in its raw entirety to the model simulations. By dividing the data into physics-based features, our data-driven approach uses one to two orders of magnitude less evaluations of the simulations. This enables the fi ing of complex models that take up to a few minutes to simulate. For validation, we process full-cell GITT measurements to characterize the di usivities of both electrodes non-destructively. Our algorithm enables experimentators and theoreticians to investigate, verify, and record their insights. We intend this algorithm to be a tool for the accessible evaluation of experimental databases.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian Parameterization of Continuum Battery Models from Featurized Electrochemical Measurements Considering Noise

Yannick¹,

Wolf²,

Latz³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Alternatively, non-black box approaches ( Zhao et al, 2021 ) aim to identify the physical properties that can be accurately inferred from full images. Once parameters and physical properties are inferred, we can naturally wonder whether these 2D abstractions generalize, not only to 3D settings ( Sudhakaran et al, 2021 preprint), but also to systems reminiscent of cellular self-organization ( Gilpin, 2019 ).…”

Section: Going Further With Deep Learningmentioning

confidence: 99%

Deep learning for bioimage analysis in developmental biology

et al. 2021

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Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis. As the complexity and size of bioimage data continues to grow, this new analysis paradigm is becoming increasingly ubiquitous. In this Review, we begin by introducing the concepts needed for beginners to understand deep learning. We then review how deep learning has impacted bioimage analysis and explore the open-source resources available to integrate it into a research project. Finally, we discuss the future of deep learning applied to cell and developmental biology. We analyze how state-of-the-art methodologies have the potential to transform our understanding of biological systems through new image-based analysis and modelling that integrate multimodal inputs in space and time.

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“…81 In recent work, the inverse problem for the Cahn-Hilliard equation has been explored; this has enabled the evaluation of the thermodynamics of the system from a few snapshots of the pattern forming process. 82,83 This approach has particular utility for studying complex systems where there may be rich experimental data of the actual pattern-forming process, but formulating an accurate thermodynamic and/or transport model is non-trivial.…”

Section: Soft Matter Accepted Manuscriptmentioning

confidence: 99%

Continuum-scale modelling of polymer blends using the Cahn–Hilliard equation: transport and thermodynamics

et al. 2021

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Image inversion and uncertainty quantification for constitutive laws of pattern formation

Cited by 16 publications

References 75 publications

Bayesian Parameterization of Continuum Battery Models from Featurized Electrochemical Measurements Considering Noise

Bayesian Parameterization of Continuum Battery Models from Featurized Electrochemical Measurements Considering Noise

Deep learning for bioimage analysis in developmental biology

Continuum-scale modelling of polymer blends using the Cahn–Hilliard equation: transport and thermodynamics

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