Inverse design of anisotropic microstructures using physics-augmented neural networks Article Swipe
Asghar Jadoon
,
Karl A. Kalina
,
Manuel K. Rausch
,
Reese Jones
,
Jan N. Fuhg
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.13370
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.13370
Composite materials often exhibit mechanical anisotropy owing to the material properties or geometrical configurations of the microstructure. This makes their inverse design a two-fold problem. First, we must learn the type and orientation of anisotropy and then find the optimal design parameters to achieve the desired mechanical response. In our work, we solve this challenge by first training a forward surrogate model based on the macroscopic stress-strain data obtained via computational homogenization for a given multiscale material. To this end, we use partially Input Convex Neural Networks (pICNNs) to obtain a polyconvex representation of the strain energy in terms of the invariants of the Cauchy-Green deformation tensor. The network architecture and the strain energy function are modified to incorporate, by construction, physics and mechanistic assumptions into the framework. While training the neural network, we find the type of anisotropy, if any, along with the preferred directions. Once the model is trained, we solve the inverse problem using an evolution strategy to obtain the design parameters that give a desired mechanical response. We test the framework against synthetic macroscale and also homogenized data. For cases where polyconvexity might be violated during the homogenization process, we present viable alternate formulations. The trained model is also integrated into a finite element framework to invert design parameters that result in a desired macroscopic response. We show that the invariant-based model is able to solve the inverse problem for a stress-strain dataset with a different preferred direction than the one it was trained on and is able to not only learn the polyconvex potentials of hyperelastic materials but also recover the correct parameters for the inverse design problem.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2412.13370
- https://arxiv.org/pdf/2412.13370
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405875856
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https://openalex.org/W4405875856Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2412.13370Digital Object Identifier
- Title
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Inverse design of anisotropic microstructures using physics-augmented neural networksWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-17Full publication date if available
- Authors
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Asghar Jadoon, Karl A. Kalina, Manuel K. Rausch, Reese Jones, Jan N. FuhgList of authors in order
- Landing page
-
https://arxiv.org/abs/2412.13370Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2412.13370Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2412.13370Direct OA link when available
- Concepts
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Inverse, Artificial neural network, Anisotropy, Microstructure, Inverse problem, Inverse method, Statistical physics, Physics, Computer science, Materials science, Mathematics, Applied mathematics, Mathematical analysis, Artificial intelligence, Geometry, Optics, Composite materialTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.First, | 25 |
| abstract_inverted_index.Neural | 85 |
| abstract_inverted_index.design | 21, 40, 163, 211, 271 |
| abstract_inverted_index.during | 189 |
| abstract_inverted_index.energy | 96, 113 |
| abstract_inverted_index.finite | 206 |
| abstract_inverted_index.invert | 210 |
| abstract_inverted_index.neural | 131 |
| abstract_inverted_index.obtain | 89, 161 |
| abstract_inverted_index.result | 214 |
| abstract_inverted_index.strain | 95, 112 |
| abstract_inverted_index.viable | 195 |
| abstract_inverted_index.achieve | 43 |
| abstract_inverted_index.against | 175 |
| abstract_inverted_index.correct | 266 |
| abstract_inverted_index.dataset | 236 |
| abstract_inverted_index.desired | 45, 168, 217 |
| abstract_inverted_index.element | 207 |
| abstract_inverted_index.exhibit | 3 |
| abstract_inverted_index.forward | 59 |
| abstract_inverted_index.inverse | 20, 154, 231, 270 |
| abstract_inverted_index.network | 108 |
| abstract_inverted_index.optimal | 39 |
| abstract_inverted_index.physics | 121 |
| abstract_inverted_index.present | 194 |
| abstract_inverted_index.problem | 155, 232 |
| abstract_inverted_index.recover | 264 |
| abstract_inverted_index.tensor. | 106 |
| abstract_inverted_index.trained | 199, 247 |
| abstract_inverted_index.(pICNNs) | 87 |
| abstract_inverted_index.Networks | 86 |
| abstract_inverted_index.function | 114 |
| abstract_inverted_index.material | 9 |
| abstract_inverted_index.modified | 116 |
| abstract_inverted_index.network, | 132 |
| abstract_inverted_index.obtained | 68 |
| abstract_inverted_index.problem. | 24, 272 |
| abstract_inverted_index.process, | 192 |
| abstract_inverted_index.strategy | 159 |
| abstract_inverted_index.trained, | 150 |
| abstract_inverted_index.training | 57, 129 |
| abstract_inverted_index.two-fold | 23 |
| abstract_inverted_index.violated | 188 |
| abstract_inverted_index.Composite | 0 |
| abstract_inverted_index.alternate | 196 |
| abstract_inverted_index.challenge | 54 |
| abstract_inverted_index.different | 239 |
| abstract_inverted_index.direction | 241 |
| abstract_inverted_index.evolution | 158 |
| abstract_inverted_index.framework | 174, 208 |
| abstract_inverted_index.material. | 76 |
| abstract_inverted_index.materials | 1, 261 |
| abstract_inverted_index.partially | 82 |
| abstract_inverted_index.preferred | 144, 240 |
| abstract_inverted_index.response. | 47, 170, 219 |
| abstract_inverted_index.surrogate | 60 |
| abstract_inverted_index.synthetic | 176 |
| abstract_inverted_index.anisotropy | 5, 34 |
| abstract_inverted_index.framework. | 127 |
| abstract_inverted_index.integrated | 203 |
| abstract_inverted_index.invariants | 101 |
| abstract_inverted_index.macroscale | 177 |
| abstract_inverted_index.mechanical | 4, 46, 169 |
| abstract_inverted_index.multiscale | 75 |
| abstract_inverted_index.parameters | 41, 164, 212, 267 |
| abstract_inverted_index.polyconvex | 91, 257 |
| abstract_inverted_index.potentials | 258 |
| abstract_inverted_index.properties | 10 |
| abstract_inverted_index.anisotropy, | 138 |
| abstract_inverted_index.assumptions | 124 |
| abstract_inverted_index.deformation | 105 |
| abstract_inverted_index.directions. | 145 |
| abstract_inverted_index.geometrical | 12 |
| abstract_inverted_index.homogenized | 180 |
| abstract_inverted_index.macroscopic | 65, 218 |
| abstract_inverted_index.mechanistic | 123 |
| abstract_inverted_index.orientation | 32 |
| abstract_inverted_index.Cauchy-Green | 104 |
| abstract_inverted_index.architecture | 109 |
| abstract_inverted_index.hyperelastic | 260 |
| abstract_inverted_index.incorporate, | 118 |
| abstract_inverted_index.computational | 70 |
| abstract_inverted_index.construction, | 120 |
| abstract_inverted_index.formulations. | 197 |
| abstract_inverted_index.polyconvexity | 185 |
| abstract_inverted_index.stress-strain | 66, 235 |
| abstract_inverted_index.configurations | 13 |
| abstract_inverted_index.homogenization | 71, 191 |
| abstract_inverted_index.representation | 92 |
| abstract_inverted_index.invariant-based | 224 |
| abstract_inverted_index.microstructure. | 16 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |