Learning constitutive models and rheology from partial flow measurements
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911460713562112 |
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| author | Sunol, Alp M. Roggeveen, James V. Alhashim, Mohammed G. Bae, Henry S. Brenner, Michael P. |
| author_facet | Sunol, Alp M. Roggeveen, James V. Alhashim, Mohammed G. Bae, Henry S. Brenner, Michael P. |
| contents | Constitutive laws relate fluid stress to deformation and underpin predictions of non-Newtonian behavior in industrial and biological fluids. Standard characterization relies on measurements in idealized flows that often miss physics relevant to complex geometries. Existing data-driven methods overfit sparse data, lack geometry portability, or presuppose constitutive forms. To unify measurement and constitutive discovery, we developed an end-to-end framework that leverages automatic differentiation through a full physics simulation. By embedding a frame-invariant tensor basis neural network (TBNN) within a differentiable non-Newtonian solver, we learn form-agnostic stress-strain mappings from any flow observable. Unlike coordinate-dependent methods, learning local material response enables prediction in unseen geometries. We then distill this closure into symbolic form via automated Bayesian model selection, extracting interpretable physical parameters. This work establishes a foundation for comprehensive characterization of complex fluids directly within their operating environment ("digital rheometry") with broad applicability to constitutive discovery across engineering and the physical sciences. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_24673 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning constitutive models and rheology from partial flow measurements Sunol, Alp M. Roggeveen, James V. Alhashim, Mohammed G. Bae, Henry S. Brenner, Michael P. Fluid Dynamics Constitutive laws relate fluid stress to deformation and underpin predictions of non-Newtonian behavior in industrial and biological fluids. Standard characterization relies on measurements in idealized flows that often miss physics relevant to complex geometries. Existing data-driven methods overfit sparse data, lack geometry portability, or presuppose constitutive forms. To unify measurement and constitutive discovery, we developed an end-to-end framework that leverages automatic differentiation through a full physics simulation. By embedding a frame-invariant tensor basis neural network (TBNN) within a differentiable non-Newtonian solver, we learn form-agnostic stress-strain mappings from any flow observable. Unlike coordinate-dependent methods, learning local material response enables prediction in unseen geometries. We then distill this closure into symbolic form via automated Bayesian model selection, extracting interpretable physical parameters. This work establishes a foundation for comprehensive characterization of complex fluids directly within their operating environment ("digital rheometry") with broad applicability to constitutive discovery across engineering and the physical sciences. |
| title | Learning constitutive models and rheology from partial flow measurements |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2510.24673 |