CINAS-PINN: Causal inference-based neural architecture search in physics-informed neural networks for fatigue life prediction with welding strain energy
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Fatigue and fracture mechanics · Model Reduction and Neural Networks · Structural Health Monitoring Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (46)
Show source-provided references
1. Ratcheting fatigue reliability and sensitivity analysis of hydraulic pipe under in‑service loadings
2. Advances in machine learning techniques used in fatigue life prediction of welded structures10.3390/app14010398 ↗
3. Effect of welding residual stresses on the fatigue life assessment of welded connections10.1016/j.ijfatigue.2024.108570 ↗
4. A dynamic imperfect inspection‑based maintenance optimization considering dependent competing failure10.1016/j.measurement.2025.117470 ↗
5. The strain-life approach applied to welded joints: considering the real weld geometry10.1016/j.jcsr.2018.04.024 ↗
6. Uncertainty propagation of turbulence parameters for typhoon and Non-typhoon winds in buffeting analysis of Long-span bridges10.1016/j.engstruct.2023.116491 ↗
7. Adaptive expert ensembles for fault diagnosis: a graph causal framework addressing distributional shifts10.1016/j.ymssp.2025.112762 ↗
8. A stage-dependent Markov-switching fractional Brownian motion model for reliability analysis considering random effects and long-term memory10.1016/j.ress.2026.112208 ↗
9. Graph-based reliability evaluation of a reconfigurable multi-stage system using sequential unconnected path sets10.1016/j.ress.2025.111093 ↗
10. An evidence-based likelihood approach for the reliability of a complex system with overlapped failure data10.1016/j.cie.2025.110893 ↗
11. A critical plane approach to multiaxial fatigue damage including out‑of‑phase loading10.1111/j.1460-2695.1988.tb01169.x ↗
12. Energy‑based multi‑axial fatigue prediction for tubular structures under non‑proportional loading10.1016/j.ijfatigue.2025.108897 ↗
13. Equivalent energy‑based critical plane fatigue damage parameter for multiaxial LCF under variable amplitude loading10.1016/j.ijfatigue.2019.105350 ↗
14. Welded joints stiffness and damping characterization based on model updating and texture analysis10.1016/j.ymssp.2024.111684 ↗
15. Prediction of welded joint fatigue properties based on a novel hybrid SPDTRS‑CS‑ANN method10.1016/j.engfracmech.2022.108824 ↗
16. Artificial neural network based fatigue life assessment of friction welding AA2024‑T351 aluminum alloy and multi‑objective optimization of welding parameters
17. Machine learning-based fatigue life prediction of double-sided U-rib welded joints considering multiple factors
18. AI-based macro model learning for high cycle fatigue assessment of welded joints in large-span steel structures10.1016/j.ijfatigue.2024.108321 ↗
19. Dynamic model-based multi-objective optimization of valve plate for vibration reduction in axial piston pumps10.1016/j.cja.2025.104045 ↗
20. A novel three-position three-way integrate high speed on/off valve pair with opposite reverse-drive three-level strategy
21. Structural damage diagnosis and prognosis with fleet digital twin considering similarity of individual structural features
22. Copula-based collaborative multistructure damage diagnosis and prognosis for fleet maintenance digital twins10.2514/1.J063105 ↗
23. Reinforcement learning for multi‑agent with asynchronous missing information fusion method10.1007/s13042-024-02170-y ↗
24. Degradation prediction of proportional solenoid based on data-model interactive method
25. Reliability estimation method based on nonlinear Tweedie exponential dispersion process and evidential reasoning rule
26. Two‑stage optimization of computation offloading for ICN‑assisted mobile edge computing in 6G network10.1016/j.icte.2024.09.006 ↗
27. Physics‑informed machine learning10.1038/s42254-021-00314-5 ↗
28. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations10.1016/j.jcp.2018.10.045 ↗
29. MFLP‑PINN: a physics‑informed neural network for multiaxial fatigue life prediction10.1016/j.euromechsol.2022.104889 ↗
30. Predicting fatigue life of multi‑defect materials using the fracture mechanics‑based physics‑informed neural network framework10.1016/j.ijfatigue.2024.108626 ↗
31. A physics‑informed neural network method for identifying parameters and predicting remaining life of fatigue crack growth10.1016/j.ijfatigue.2024.108678 ↗
32. A modified physics-informed neural network to fatigue life prediction of deck-rib double-side welded joints10.1016/j.ijfatigue.2024.108566 ↗
33. The hard-constraint PINNs for interface optimal control problems10.1137/23M1601249 ↗
34. Multi-level digital twin for real-time deformation and stress analysis of wing structures10.1016/j.cja.2026.104160 ↗
35. Gao J., Wang S., Chen R., Zhang C., Zio E., Zhang Y. ZSTT: A Zero-Shot Time-Series Prediction Model Based on Transformer and Its Application to Hydraulic Actuator Performance Degradation Prediction. IEEE/ASME Trans Mechatron 2025;1–12.10.1109/TMECH.2025.3630496 ↗
36. A comprehensive survey of neural architecture search: challenges and solutions10.1145/3447582 ↗
37. Liu H, Simonyan K, Yang Y. Darts: Differentiable architecture search. arXiv preprint arXiv:1806.09055; 2018.
38. NAS‑PINN: Neural architecture search‑guided physics‑informed neural network for solving PDEs10.1016/j.jcp.2023.112603 ↗
39. Wang Y, Han X, Chang CY, Zha D, Braga‑Neto U, Hu X. Auto‑pinn: Understanding and optimizing physics‑informed neural architecture. arXiv preprint arXiv:2205.13748; 2022.
40. Deeppipe: a two‑stage physics‑informed neural network for predicting mixed oil concentration distribution10.1016/j.energy.2023.127452 ↗
41. Physics‑informed deep autoencoder for fault detection in new‑design systems10.1016/j.ymssp.2024.111420 ↗
42. Fatigue damage editing for accelerated durability testing using strain range and SWT parameter criteria10.1016/S0142-1123(97)00079-0 ↗
43. A novel energy‑critical multiaxial fatigue life prediction for low cycle fatigue under mixed‑mode loading10.3390/met8121066 ↗
44. Multiaxial fatigue life prediction for titanium alloy TC4 under proportional and nonproportional loading10.1016/j.ijfatigue.2013.08.028 ↗
45. On the importance of the Pearson correlation coefficient in noise reduction10.1109/TASL.2008.919072 ↗
46. On the tractability of SHAP explanations10.1613/jair.1.13283 ↗