A physics-encoded Fourier neural operator approach for surrogate modeling of divergence-free stress fields in solids
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915137744535552 |
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| author | Khorrami, Mohammad S. Goyal, Pawan Mianroodi, Jaber R. Svendsen, Bob Benner, Peter Raabe, Dierk |
| author_facet | Khorrami, Mohammad S. Goyal, Pawan Mianroodi, Jaber R. Svendsen, Bob Benner, Peter Raabe, Dierk |
| contents | The purpose of the current work is the development of a so-called physics-encoded Fourier neural operator (PeFNO) for surrogate modeling of the quasi-static equilibrium stress field in solids. Rather than accounting for constraints from physics in the loss function as done in the (now standard) physics-informed approach, the physics-encoded approach incorporates or "encodes" such constraints directly into the network or operator architecture. As a result, in contrast to the physics-informed approach in which only training is physically constrained, both training and output are physically constrained in the physics-encoded approach. For the current constraint of divergence-free stress, a novel encoding approach based on a stress potential is proposed.
As a "proof-of-concept" example application of the proposed PeFNO, a heterogeneous polycrystalline material consisting of isotropic elastic grains subject to uniaxial extension is considered. Stress field data for training are obtained from the numerical solution of a corresponding boundary-value problem for quasi-static mechanical equilibrium. This data is also employed to train an analogous physics-guided FNO (PgFNO) and physics-informed FNO (PiFNO) for comparison. As confirmed by this comparison and as expected on the basis of their differences, the output of the trained PeFNO is significantly more accurate in satisfying mechanical equilibrium than the output of either the trained PgFNO or the trained PiFNO. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_15408 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A physics-encoded Fourier neural operator approach for surrogate modeling of divergence-free stress fields in solids Khorrami, Mohammad S. Goyal, Pawan Mianroodi, Jaber R. Svendsen, Bob Benner, Peter Raabe, Dierk Computational Engineering, Finance, and Science Materials Science Machine Learning Analysis of PDEs The purpose of the current work is the development of a so-called physics-encoded Fourier neural operator (PeFNO) for surrogate modeling of the quasi-static equilibrium stress field in solids. Rather than accounting for constraints from physics in the loss function as done in the (now standard) physics-informed approach, the physics-encoded approach incorporates or "encodes" such constraints directly into the network or operator architecture. As a result, in contrast to the physics-informed approach in which only training is physically constrained, both training and output are physically constrained in the physics-encoded approach. For the current constraint of divergence-free stress, a novel encoding approach based on a stress potential is proposed. As a "proof-of-concept" example application of the proposed PeFNO, a heterogeneous polycrystalline material consisting of isotropic elastic grains subject to uniaxial extension is considered. Stress field data for training are obtained from the numerical solution of a corresponding boundary-value problem for quasi-static mechanical equilibrium. This data is also employed to train an analogous physics-guided FNO (PgFNO) and physics-informed FNO (PiFNO) for comparison. As confirmed by this comparison and as expected on the basis of their differences, the output of the trained PeFNO is significantly more accurate in satisfying mechanical equilibrium than the output of either the trained PgFNO or the trained PiFNO. |
| title | A physics-encoded Fourier neural operator approach for surrogate modeling of divergence-free stress fields in solids |
| topic | Computational Engineering, Finance, and Science Materials Science Machine Learning Analysis of PDEs |
| url | https://arxiv.org/abs/2408.15408 |