Unified Hysteresis Modeling via Physics‐Based Deep Learning and Data Augmentation
Bibliographic record
Abstract
ABSTRACT Deep learning‐based models have recently emerged as alternatives to traditional form‐constrained hysteresis models, including Bouc‐Wen class models, offering significant potential for unified hysteresis modeling to capture complex nonlinearities and diverse response patterns exhibited under stochastic excitations such as ground motions. This paper proposes a unified hysteresis modeling framework based on deep learning, leveraging (1) physics‐encoded deep learning through a custom architecture that emulates the solution process of traditional models, (2) physics‐informed deep learning with an efficient loss function to enforce non‐negative energy dissipation, and (3) data augmentation via resampling hysteresis data to enhance the training dataset. The proposed unified hysteresis model can be trained on a relatively small amount of force–displacement data obtained under seismic excitations and enabling efficient and accurate time history analysis. The proposed model can account for complex stiffness and strength degradations and pinching effects. Tests across various traditional hysteresis models demonstrate that the proposed deep learning‐based unified hysteresis model can effectively reproduce diverse hysteresis behaviors. The proposed model is also validated against experimental hysteresis data from modular yielding links, confirming its capability to accurately represent real‐world hysteresis behavior. The source code and accompanying data can be accessed online for reproducibility at https://github.com/JaehwanJeon/Testing_torch .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".