Self-supervised optimization of random material microstructures in the small-data regime

@article{Rixner2021SelfsupervisedOO,
  title={Self-supervised optimization of random material microstructures in the small-data regime},
  author={Maximilian Rixner and Phaedon-Stelios Koutsourelakis},
  journal={npj Computational Materials},
  year={2021},
  volume={8},
  pages={1-11}
}
While the forward and backward modeling of the process-structure-property chain has received a lot of attention from the materials’ community, fewer efforts have taken into consideration uncertainties. Those arise from a multitude of sources and their quantification and integration in the inversion process are essential in meeting the materials design objectives. The first contribution of this paper is a flexible, fully probabilistic formulation of materials’ optimization problems that accounts… 
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2 Citations

2 Citations

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References

SHOWING 1-10 OF 57 REFERENCES

Material structure-property linkages using three-dimensional convolutional neural networks

A predictive machine learning approach for microstructure optimization and materials design

A systematic framework consisting of random data generation, feature selection and classification algorithms is developed and experiments show that this framework outperforms traditional optimization methods with the average running time reduced by as much as 80% and with optimality that would not be achieved otherwise.

Solving Stochastic Inverse Problems for Property–Structure Linkages Using Data-Consistent Inversion and Machine Learning

This work seeks to learn a distribution of microstructure parameters that are consistent in the sense that the forward propagation of this distribution through a crystal plasticity finite element model matches a target distribution on materials properties.

Microstructure optimization with constrained design objectives using machine learning-based feedback-aware data-generation

Stochastic microstructure characterization and reconstruction via supervised learning

Fast inverse design of microstructures via generative invariance networks

This work reformulate the microstructure design process as a constrained Generative Adversarial Network (GAN) to generate two-phase morphologies for photovoltaic applications obeying design specifications: specifically, various short circuit current density and fill-factor combinations.

Microstructure Representation and Reconstruction of Heterogeneous Materials via Deep Belief Network for Computational Material Design

A feature learning mechanism based on convolutional deep belief network is developed to automate a two-way conversion between microstructures and their lower-dimensional feature representations, and to achieves a 1000-fold dimension reduction from the microstructure space.

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

It is shown that experiment- and simulation-based data mining in combination with machine leaning tools provide exceptional opportunities to enable highly reliant identification of fundamental interrelations within materials for characterization and optimization in a scale-bridging manner.

STOCHASTIC DESIGN AND CONTROL IN RANDOM HETEROGENEOUS MATERIALS

The present paper discusses a sampling framework that enables the optimization of complex systems characterized by high-dimensional uncertainties and design variables. We are especially concerned…

Microstructure design using machine learning generated low dimensional and continuous design space

...