An interleaved physics-based deep-learning framework as a new cycle jumping approach for microstructurally small fatigue crack growth simulations
Abstract Conventional fracture mechanics asserts that the relevant physics governing small crack growth occurs near the crack front. However, for fatigue, computing these physics for each crack-growth increment over the entire microstructurally small crack regime is computationally intractable. Prop...
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| Format: | Article |
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Nature Portfolio
2025-08-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01741-z |
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| author | Vignesh Babu Rao Ashley D. Spear |
| author_facet | Vignesh Babu Rao Ashley D. Spear |
| author_sort | Vignesh Babu Rao |
| collection | DOAJ |
| description | Abstract Conventional fracture mechanics asserts that the relevant physics governing small crack growth occurs near the crack front. However, for fatigue, computing these physics for each crack-growth increment over the entire microstructurally small crack regime is computationally intractable. Properly trained deep-learning surrogate models can massively accelerate fatigue crack-growth predictions by virtually propagating an initial crack using micromechanical fields corresponding to just the initially cracked microstructure. As the predicted crack front advances, however, the fields no longer reflect relevant near-crack-front physics, leading to error and uncertainty accumulation. To address this, we present an interleaved physics-based deep-learning (PBDL) framework, where updates to the crack representation in the physics-based model are triggered intermittently using model uncertainty, thereby updating micromechanical fields passed to the deep-learning model. We show that this framework, representing a novel cycle-jumping approach, effectively limits error accumulation in history-dependent fatigue crack evolution and forms a template for other time-series applications in materials. |
| format | Article |
| id | doaj-art-f3ace7ff7d404a1d922944c09c082dfd |
| institution | Kabale University |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-f3ace7ff7d404a1d922944c09c082dfd2025-08-20T04:02:56ZengNature Portfolionpj Computational Materials2057-39602025-08-011111810.1038/s41524-025-01741-zAn interleaved physics-based deep-learning framework as a new cycle jumping approach for microstructurally small fatigue crack growth simulationsVignesh Babu Rao0Ashley D. Spear1Department of Mechanical Engineering, University of UtahDepartment of Mechanical Engineering, University of UtahAbstract Conventional fracture mechanics asserts that the relevant physics governing small crack growth occurs near the crack front. However, for fatigue, computing these physics for each crack-growth increment over the entire microstructurally small crack regime is computationally intractable. Properly trained deep-learning surrogate models can massively accelerate fatigue crack-growth predictions by virtually propagating an initial crack using micromechanical fields corresponding to just the initially cracked microstructure. As the predicted crack front advances, however, the fields no longer reflect relevant near-crack-front physics, leading to error and uncertainty accumulation. To address this, we present an interleaved physics-based deep-learning (PBDL) framework, where updates to the crack representation in the physics-based model are triggered intermittently using model uncertainty, thereby updating micromechanical fields passed to the deep-learning model. We show that this framework, representing a novel cycle-jumping approach, effectively limits error accumulation in history-dependent fatigue crack evolution and forms a template for other time-series applications in materials.https://doi.org/10.1038/s41524-025-01741-z |
| spellingShingle | Vignesh Babu Rao Ashley D. Spear An interleaved physics-based deep-learning framework as a new cycle jumping approach for microstructurally small fatigue crack growth simulations npj Computational Materials |
| title | An interleaved physics-based deep-learning framework as a new cycle jumping approach for microstructurally small fatigue crack growth simulations |
| title_full | An interleaved physics-based deep-learning framework as a new cycle jumping approach for microstructurally small fatigue crack growth simulations |
| title_fullStr | An interleaved physics-based deep-learning framework as a new cycle jumping approach for microstructurally small fatigue crack growth simulations |
| title_full_unstemmed | An interleaved physics-based deep-learning framework as a new cycle jumping approach for microstructurally small fatigue crack growth simulations |
| title_short | An interleaved physics-based deep-learning framework as a new cycle jumping approach for microstructurally small fatigue crack growth simulations |
| title_sort | interleaved physics based deep learning framework as a new cycle jumping approach for microstructurally small fatigue crack growth simulations |
| url | https://doi.org/10.1038/s41524-025-01741-z |
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