Robust design framework for negative poisson’s ratio structures through FEM-PINN: Uncertainty analysis and multi-objective optimization
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: Probabilistic and Robust Engineering Design · Cellular and Composite Structures · Topology Optimization in Engineering
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (38)
Show source-provided references
1. Negative poisson’s ratio materials10.1126/science.238.4826.551.a ↗
2. Nature-inspired 3d printing-based double-graded aerospace negative poisson’s ratio metastructure: Design, fabrication, investigation, optimization10.1016/j.compstruct.2024.118482 ↗
3. Auxetics in biomedical applications: a review
4. Functionally-graded npr (negative poisson’s ratio) material for a blast-protective deflector
5. Novel structure with negative poisson’s ratio and enhanced young’s modulus10.1016/j.compstruct.2015.11.036 ↗
6. Machine learning prediction of mechanical properties in metal additive manufacturing
7. D. Gunasegaram, A. Barnard, M. Matthews, B. Jared, A. Andreaco, K. Bartsch, A. Murphy, Machine learningassisted in-situ adaptive strategies for the control of defects and anomalies in metal additive manufacturing addit (2024).10.1016/j.addma.2024.104013 ↗
8. Uncertainty quantification and propagation across a multi-model computational framework for the tailored design of additively manufactured shape memory alloys
9. Active learning via adaptive weighted uncertainty sampling applied to additive manufacturing
10. Micromechanical analysis of dynamic behavior of conventional and negative poisson’s ratio foams10.1115/1.2806807 ↗
11. Three decades of auxetic polymers: a review10.1515/epoly-2014-0193 ↗
12. T.-C. Lim, Auxetic materials and structures, Vol. 33, Springer, 2015.10.1007/978-981-287-275-3 ↗
13. Auxetic polymers: a new range of materials10.1016/0160-9327(91)90123-S ↗
14. Additive manufacturing of re-entrant structures: Well-tailored structures, unique properties, modelling approaches and real applications
15. Crashworthiness optimization method for sandwich plate structure under impact loading10.1016/j.oceaneng.2022.110870 ↗
16. Analytical investigation and optimal design of sandwich panels subjected to shock loading10.1016/j.matdes.2008.04.027 ↗
17. Energy absorption enhancement of additively manufactured hexagonal and¨ re-entrant (auxetic) lattice structures by using multi-material reinforcements
18. Impact resistance analysis and multi-objective optimization of polyurea-coated auxetic honeycomb sandwich panels
19. Isogeometric shape optimization of missing rib auxetics with prescribed negative poisson’s ratio over large strains using genetic algorithm10.1016/j.ijmecsci.2020.106169 ↗
20. M. Raissi, P. Perdikaris, G. E. Karniadakis, Physics informed deep learning (part i): Data-driven solutions of nonlinear partial differential equations, arXiv preprint arXiv:1711.10561 (2017).
21. Data-driven discovery of partial differential equations10.1126/sciadv.1602614 ↗
22. A physics-informed neural network for kresling origami structures10.1016/j.ijmecsci.2024.109080 ↗
23. Physics-informed neural networks for topological metamaterial design and mechanical applications
24. Optimal design of sandwich panels with hybrid core10.1177/1099636217742574 ↗
25. Optimal design of a honeycomb core composite sandwich panel using evolutionary optimization algorithms10.1016/j.compstruct.2015.12.019 ↗
26. Design and optimization of negative poisson ratio thin-walled tube using sensitivity-based response surface model methodology10.1142/S0219455423500360 ↗
27. Multi-objective explosion-proof performance optimization of a novel vehicle door with negative poisson’s ratio structure10.1007/s00158-018-2026-z ↗
28. Design optimization of sandwich panels under impact loads10.1080/0305215X.2020.1818737 ↗
29. An advanced physics-informed neural operator for comprehensive design optimization of highly-nonlinear systems: an aerospace composites processing case study10.1016/j.engappai.2024.109886 ↗
30. Physics-informed neural networks for data-free surrogate modelling¨ and engineering optimization–an example from composite manufacturing10.1016/j.matdes.2023.112034 ↗
31. Multi-layer thermal simulation using physics-informed neural network
32. Physics-integrated neural differentiable (pindiff) model for composites manufacturing10.1016/j.cma.2023.115902 ↗
33. Cracking particles: a simplified meshfree method for arbitrary evolving cracks10.1002/nme.1151 ↗
34. Transfer learning enhanced physics informed neural network for phase-field modeling of fracture10.1016/j.tafmec.2019.102447 ↗
35. A three-dimensional large deformation meshfree method for arbitrary evolving cracks10.1016/j.cma.2006.06.020 ↗
36. An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications10.1016/j.cma.2019.112790 ↗
37. Variational physics-informed neural operator (vino) for solving partial differential equations10.1016/j.cma.2025.117785 ↗
38. A. C. D.-. on Plastics, Standard Test Method for High Speed Puncture Properties of Plastics Using Load and Displacement Sensors, ASTM International, 2002.