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Toward Learning Latent-Variable Representations of Microstructures by Optimizing in Spatial Statistics Space
Machine Learning · Submitted 16 Feb 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Our experiments indicate that it is possible to train a VAE that minimizes the distance in spatial statistics space between the original and the reconstruction of synthetic images.
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The abstract, sentence by sentence
In Materials Science, material development involves evaluating and optimizing the internal structures of the material, generically referred to as microstructures. Microstructures structure is stochastic, analogously to image textures. A particular microstructure can be well characterized by its spatial statistics, analogously to image texture being characterized by the response to a Fourier-like filter bank. Material design would benefit from low-dimensional representation of microstructures Paulson et al. (2017). In this work, we train a Variational Autoencoders (VAE) to produce reconstructions of textures that preserve the spatial statistics of the original texture, while not necessarily reconstructing the same image in data space. We accomplish this by adding a differentiable term to the cost function in order to minimize the distance between the original and the reconstruction in spatial statistics space. Our experiments indicate that it is possible to train a VAE that minimizes the distance in spatial statistics space between the original and the reconstruction of synthetic images. In future work, we will apply the same techniques to microstructures, with the goal of obtaining low-dimensional representations of material microstructures.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | In Materials Science, material development involves evaluating and optimizing the internal structures of the material, generically referred to as microstructures. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | Microstructures structure is stochastic, analogously to image textures. | background 1.00 | 0.09 | 0.02 | asserted 1.00 |
| s2 | A particular microstructure can be well characterized by its spatial statistics, analogously to image texture being characterized by the response to a Fourier-like filter bank. | background 1.00 | 0.08 | 0.03 | asserted 0.98 |
| s3 | Material design would benefit from low-dimensional representation of microstructures Paulson et al. (2017). | background 0.91 | 0.05 | 0.05 | hedged possibility 0.59 |
| s4 | In this work, we train a Variational Autoencoders (VAE) to produce reconstructions of textures that preserve the spatial statistics of the original texture, while not necessarily reconstructing the same image in data space. | method 0.62 | 0.92 | 0.11 | asserted 0.99 |
| s5 | We accomplish this by adding a differentiable term to the cost function in order to minimize the distance between the original and the reconstruction in spatial statistics space. | method 1.00 | 0.88 | 0.08 | asserted 0.99 |
| s6 | Our experiments indicate that it is possible to train a VAE that minimizes the distance in spatial statistics space between the original and the reconstruction of synthetic images. | result 1.00 | 0.96 | 0.55 | hedged possibility 0.97 |
| s7 | In future work, we will apply the same techniques to microstructures, with the goal of obtaining low-dimensional representations of material microstructures. | future work 1.00 | 0.05 | 0.07 | hedged possibility 0.43 |
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Original abstract
In Materials Science, material development involves evaluating and optimizing the internal structures of the material, generically referred to as microstructures. Microstructures structure is stochastic, analogously to image textures. A particular microstructure can be well characterized by its spatial statistics, analogously to image texture being characterized by the response to a Fourier-like filter bank. Material design would benefit from low-dimensional representation of microstructures Paulson et al. (2017). In this work, we train a Variational Autoencoders (VAE) to produce reconstructions of textures that preserve the spatial statistics of the original texture, while not necessarily reconstructing the same image in data space. We accomplish this by adding a differentiable term to the cost function in order to minimize the distance between the original and the reconstruction in spatial statistics space. Our experiments indicate that it is possible to train a VAE that minimizes the distance in spatial statistics space between the original and the reconstruction of synthetic images. In future work, we will apply the same techniques to microstructures, with the goal of obtaining low-dimensional representations of material microstructures.
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