A review on data-driven constitutive laws for solids
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910436351279104 |
|---|---|
| author | Fuhg, Jan Niklas Padmanabha, Govinda Anantha Bouklas, Nikolaos Bahmani, Bahador Sun, WaiChing Vlassis, Nikolaos N. Flaschel, Moritz Carrara, Pietro De Lorenzis, Laura |
| author_facet | Fuhg, Jan Niklas Padmanabha, Govinda Anantha Bouklas, Nikolaos Bahmani, Bahador Sun, WaiChing Vlassis, Nikolaos N. Flaschel, Moritz Carrara, Pietro De Lorenzis, Laura |
| contents | This review article highlights state-of-the-art data-driven techniques to discover, encode, surrogate, or emulate constitutive laws that describe the path-independent and path-dependent response of solids. Our objective is to provide an organized taxonomy to a large spectrum of methodologies developed in the past decades and to discuss the benefits and drawbacks of the various techniques for interpreting and forecasting mechanics behavior across different scales. Distinguishing between machine-learning-based and model-free methods, we further categorize approaches based on their interpretability and on their learning process/type of required data, while discussing the key problems of generalization and trustworthiness. We attempt to provide a road map of how these can be reconciled in a data-availability-aware context. We also touch upon relevant aspects such as data sampling techniques, design of experiments, verification, and validation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_03658 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A review on data-driven constitutive laws for solids Fuhg, Jan Niklas Padmanabha, Govinda Anantha Bouklas, Nikolaos Bahmani, Bahador Sun, WaiChing Vlassis, Nikolaos N. Flaschel, Moritz Carrara, Pietro De Lorenzis, Laura Computational Engineering, Finance, and Science Machine Learning Applied Physics 74-02 (Primary) This review article highlights state-of-the-art data-driven techniques to discover, encode, surrogate, or emulate constitutive laws that describe the path-independent and path-dependent response of solids. Our objective is to provide an organized taxonomy to a large spectrum of methodologies developed in the past decades and to discuss the benefits and drawbacks of the various techniques for interpreting and forecasting mechanics behavior across different scales. Distinguishing between machine-learning-based and model-free methods, we further categorize approaches based on their interpretability and on their learning process/type of required data, while discussing the key problems of generalization and trustworthiness. We attempt to provide a road map of how these can be reconciled in a data-availability-aware context. We also touch upon relevant aspects such as data sampling techniques, design of experiments, verification, and validation. |
| title | A review on data-driven constitutive laws for solids |
| topic | Computational Engineering, Finance, and Science Machine Learning Applied Physics 74-02 (Primary) |
| url | https://arxiv.org/abs/2405.03658 |