RECORD
Augmentation of scarce data -- a new approach for deep-learning modeling of composites
Materials Science · Submitted 24 Nov 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The obtained results showed a considerable improvement of the network predictions trained on expanded datasets using the proposed data augmentation approach.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
Open the compiled record
The abstract, sentence by sentence
High-fidelity full-field micro-mechanical modeling of the non-linear path-dependent materials demands a substantial computational effort. Recent trends in the field incorporates data-driven Artificial Neural Networks (ANNs) as surrogate models. However, ANNs are inherently data-hungry, functioning as a bottleneck for the development of high-fidelity data-driven models. This study introduces a novel approach for data augmentation, expanding an original dataset without additional computational simulations. A Recurrent Neural Network (RNN) was trained and validated on high-fidelity micro-mechanical simulations of elasto-plastic short fiber reinforced composites. The obtained results showed a considerable improvement of the network predictions trained on expanded datasets using the proposed data augmentation approach. The proposed method for augmentation of scarce data may be used not only for other kind of composites, but also for other materials and at different length scales, and hence, opening avenues for innovative data-driven models in materials science and computational mechanics.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | High-fidelity full-field micro-mechanical modeling of the non-linear path-dependent materials demands a substantial computational effort. | background 1.00 | 0.06 | 0.02 | asserted 1.00 |
| s1 | Recent trends in the field incorporates data-driven Artificial Neural Networks (ANNs) as surrogate models. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s2 | However, ANNs are inherently data-hungry, functioning as a bottleneck for the development of high-fidelity data-driven models. | gap 0.58 | 0.05 | 0.03 | asserted 1.00 |
| s3 | This study introduces a novel approach for data augmentation, expanding an original dataset without additional computational simulations. | contribution 0.99 | 0.89 | 0.10 | asserted 1.00 |
| s4 | A Recurrent Neural Network (RNN) was trained and validated on high-fidelity micro-mechanical simulations of elasto-plastic short fiber reinforced composites. | method 0.98 | 0.94 | 0.04 | asserted 1.00 |
| s5 | The obtained results showed a considerable improvement of the network predictions trained on expanded datasets using the proposed data augmentation approach. | result 1.00 | 0.96 | 0.65 | asserted 1.00 |
| s6 | The proposed method for augmentation of scarce data may be used not only for other kind of composites, but also for other materials and at different length scales, and hence, opening avenues for innovative data-driven models in materials science and computational mechanics. | future work 0.56 | 0.29 | 0.38 | hedged possibility 1.00 |
What was examined, and what was not
- arXiv listing abstract v1
- full text
- figures and tables
- references and citing sentences
- relations to other papers
- P abstract
- S sections
- T body sentences
- C citations
- R cross-paper relations
The paper
Original abstract
High-fidelity full-field micro-mechanical modeling of the non-linear path-dependent materials demands a substantial computational effort. Recent trends in the field incorporates data-driven Artificial Neural Networks (ANNs) as surrogate models. However, ANNs are inherently data-hungry, functioning as a bottleneck for the development of high-fidelity data-driven models. This study introduces a novel approach for data augmentation, expanding an original dataset without additional computational simulations. A Recurrent Neural Network (RNN) was trained and validated on high-fidelity micro-mechanical simulations of elasto-plastic short fiber reinforced composites. The obtained results showed a considerable improvement of the network predictions trained on expanded datasets using the proposed data augmentation approach. The proposed method for augmentation of scarce data may be used not only for other kind of composites, but also for other materials and at different length scales, and hence, opening avenues for innovative data-driven models in materials science and computational mechanics.
Human review
Discussion
Know something about this paper? Leave a comment, or highlight any passage above to comment on it.