Alternating minimization for data-driven computational elasticity from experimental data: kernel method for learning constitutive manifold

For many industries, an understanding of the fatigue behavior of cast iron is important but this topic is still under extensive research in materials science. This paper offers fuzzy logic as a data-driven approach to address the challenge of predicting casting performance. However, data scarcity is an issue when applying a data-driven approach in this field; the presented study tackled this problem. Four fuzzy logic systems were constructed and compared in the study, two based solely upon experimental data and the others combining the same experimental data with data drawn from relevant literature. The study showed that the latter demonstrated a higher accuracy for the prediction of the ultimate tensile strength for cast iron.

Download Full-text

Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies

10.1101/344226 ◽

2018 ◽

Author(s):

Jukka Intosalmi ◽

Adrian C. Scott ◽

Michelle Hays ◽

Nicholas Flann ◽

Olli Yli-Harja ◽

...

Keyword(s):

Experimental Data ◽

Time Course ◽

Model Development ◽

Growth Dynamics ◽

Colony Growth ◽

Data Driven ◽

Coarse Grained ◽

Intercellular Signaling ◽

Growth Data ◽

Metabolic Coupling

AbstractMotivationMulticellular entities, such as mammalian tissues or microbial biofilms, typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding on how cell–cell and metabolic coupling produce functionally optimized structures is still limited.ResultsHere, we present a data-driven spatial framework to computationally investigate the development of one multicellular structure, yeast colonies. Using experimental growth data from homogeneous liquid media conditions, we develop and parameterize a dynamic cell state and growth model. We then use the resulting model in a coarse-grained spatial model, which we calibrate using experimental time-course data of colony growth. Throughout the model development process, we use state-of-the-art statistical techniques to handle the uncertainty of model structure and parameterization. Further, we validate the model predictions against independent experimental data and illustrate how metabolic coupling plays a central role in colony formation.AvailabilityExperimental data and a computational implementation to reproduce the results are available athttp://research.cs.aalto.fi/csb/software/multiscale/[email protected],[email protected]

Download Full-text

Data-Driven Methods in Fluid Dynamics: Sparse Classification from Experimental Data

Whither Turbulence and Big Data in the 21st Century? ◽

10.1007/978-3-319-41217-7_17 ◽

2016 ◽

pp. 323-342 ◽

Cited By ~ 19

Author(s):

Zhe Bai ◽

Steven L. Brunton ◽

Bingni W. Brunton ◽

J. Nathan Kutz ◽

Eurika Kaiser ◽

...

Keyword(s):

Fluid Dynamics ◽

Experimental Data ◽

Data Driven ◽

Sparse Classification

Download Full-text

Data-driven seismic response prediction of structural components

Earthquake Spectra ◽

10.1177/87552930211053345 ◽

2021 ◽

pp. 875529302110533

Author(s):

Huan Luo ◽

Stephanie German Paal

Keyword(s):

Experimental Data ◽

Seismic Response ◽

Computational Cost ◽

Data Driven ◽

Computational Time ◽

Lateral Stiffness ◽

Hysteretic Model ◽

Structural Components ◽

Rc Columns ◽

Detailed Model

Lateral stiffness of structural components, such as reinforced concrete (RC) columns, plays an important role in resisting the lateral earthquake loads. The lateral stiffness relates the lateral force to the lateral deformation, having a critical effect on the accuracy of the lateral seismic response predictions. The classical methods (e.g. fiber beam–column model) to estimate the lateral stiffness require calculations from section, element, and structural levels, which is time-consuming. Moreover, the shear deformation and bond-slip effect may also need to be included to more accurately calculate the lateral stiffness, which further increases the modeling difficulties and the computational cost. To reduce the computational time and enhance the accuracy of the predictions, this article proposes a novel data-driven method to predict the laterally seismic response based on the estimated lateral stiffness. The proposed method integrates the machine learning (ML) approach with the hysteretic model, where ML is used to compute the parameters that govern the nonlinear properties of the lateral response of target structural components directly from a training set composed of experimental data (i.e. data-driven procedure) and the hysteretic model is used to directly output the lateral stiffness based on the computed parameters and then to perform the seismic analysis. We apply the proposed method to predict the lateral seismic response of various types of RC columns subjected to cyclic loading and ground motions. We present the detailed model formulation for the application, including the developments of a modified hysteretic model, a hybrid optimization algorithm, and two data-driven seismic response solvers. The results predicted by the proposed method are compared with those obtained by classical methods with the experimental data serving as the ground truth, showing that the proposed method significantly outperforms the classical methods in both generalized prediction capabilities and computational efficiency.

Download Full-text

Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies

BMC Molecular and Cell Biology ◽

10.1186/s12860-019-0234-z ◽

2019 ◽

Vol 20 (1) ◽

Author(s):

Jukka Intosalmi ◽

Adrian C. Scott ◽

Michelle Hays ◽

Nicholas Flann ◽

Olli Yli-Harja ◽

...

Keyword(s):

Experimental Data ◽

Time Course ◽

Growth Dynamics ◽

Colony Growth ◽

Data Driven ◽

Coarse Grained ◽

Colony Development ◽

Intercellular Signaling ◽

Metabolic Coupling ◽

The Impact

Abstract Background Multicellular entities like mammalian tissues or microbial biofilms typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding how this cell–cell and metabolic coupling lead to functionally optimized structures is still limited. Results Here, we present a data-driven spatial framework to computationally investigate the development of yeast colonies as such a multicellular structure in dependence on metabolic capacity. For this purpose, we first developed and parameterized a dynamic cell state and growth model for yeast based on on experimental data from homogeneous liquid media conditions. The inferred model is subsequently used in a spatially coarse-grained model for colony development to investigate the effect of metabolic coupling by calibrating spatial parameters from experimental time-course data of colony growth using state-of-the-art statistical techniques for model uncertainty and parameter estimations. The model is finally validated by independent experimental data of an alternative yeast strain with distinct metabolic characteristics and illustrates the impact of metabolic coupling for structure formation. Conclusions We introduce a novel model for yeast colony formation, present a statistical methodology for model calibration in a data-driven manner, and demonstrate how the established model can be used to generate predictions across scales by validation against independent measurements of genetically distinct yeast strains.

Download Full-text