Machine learning-based deformation prediction and inverse design of magnetic soft cantilever using deformation simulation data
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: Model Reduction and Neural Networks · Topology Optimization in Engineering · Piezoelectric Actuators and Control
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (72)
Show source-provided references
1. Agbasi, J.C., Egbueri, J.C.: Prediction of potentially toxic elements in water resources using MLP-NN, RBF-NN, and ANFIS: a comprehensive review. Environ. Sci. Pollut. Res. 31(21), 1–29 (2024)10.1007/s11356-024-33350-6 ↗
2. Anitescu, C., Atroshchenko, E., Alajlan, N., Rabczuk, T.: Artificial neural network methods for the solution of second order boundary value problems. Comput. Mater. Contin. 59, 345–359 (2019)
3. Bahl, S., Nagar, H., Singh, I., Sehgal, S.: Smart materials types, properties and applications: a review. Mater. Today Proc. 28, 1302–1306 (2020)10.1016/j.matpr.2020.04.505 ↗
4. Barron III, E.J., Peterson, R.S., Lazarus, N., Bartlett, M.D.: Mechanically cloaked multiphase magnetic elastomer soft composites for wearable wireless power transfer. ACS Appl. Mater. Interfaces 12, 50909–50917 (2020)10.1021/acsami.0c15909 ↗
5. Bastola, A.K., Hossain, M.: The shape–morphing performance of magnetoactive soft materials. Mater. Des. 211, 110172 (2021)10.1016/j.matdes.2021.110172 ↗
6. Bira, N., Dhagat, P., Davidson, J.R.: A review of magnetic elastomers and their role in soft robotics. Front. Robot. AI 7, 588391 (2020)10.3389/frobt.2020.588391 ↗
7. Brenes, R.F., Johannssen, A., Chukhrova, N.: An intelligent bankruptcy prediction model using a multilayer perceptron. Intell. Syst. Appl. 16, 200136 (2022)
8. Brubaker, S.C., Wu, J., Sun, J., Mullin, M.D., Rehg, J.M.: On the design of cascades of boosted ensembles for face detection. Int. J. Comput. Vis. 77, 65–86 (2008)10.1007/s11263-007-0060-1 ↗
9. Cai, S.-Y., Chang, C.-H., Lin, H.-I., Huang, Y.-F., Lin, W.-J., Lin, S.-Y., Liou, Y.-R., Shen, T.-L., Huang, Y.-H., Tsao, P.-W.: Ultrahigh sensitive and flexible magnetoelectronics with magnetic nanocomposites: toward an additional perception of artificial intelligence. ACS Appl. Mater. Interfaces 10, 17393–17400 (2018)10.1021/acsami.8b04950 ↗
10. Chang, W., Liu, Y., Xiao, Y., Yuan, X., Xu, X., Zhang, S., Zhou, S.: A machine-learning-based prediction method for hypertension outcomes based on medical data. Diagnostics 9, 178 (2019)10.3390/diagnostics9040178 ↗
11. De Zarzà, I., De Curtò, J., Hernández-Orallo, E., Calafate, C.T.: Cascading and ensemble techniques in deep learning. Electronics 12, 3354 (2023)10.3390/electronics12153354 ↗
12. Dietterich, T.G.: In: International Workshop on Multiple Classifier Systems, 2000, pp. 1–15. Springer (2000)10.1007/3-540-45014-9_1 ↗
13. Dong, X., Yu, Z., Cao, W., Shi, Y., Ma, Q.: A survey on ensemble learning. Front. Comput. Sci. 14, 241–258 (2020)10.1007/s11704-019-8208-z ↗
14. Dupont, P.E., Simaan, N., Choset, H., Rucker, C.: Continuum robots for medical interventions. Proc. IEEE 110, 847–870 (2022)10.1109/JPROC.2022.3141338 ↗
15. Ebrahimi, N., Bi, C., Cappelleri, D.J., Ciuti, G., Conn, A.T., Faivre, D., Habibi, N., Hošovský, A., Iacovacci, V., Khalil, I.S.: Magnetic actuation methods in bio/soft robotics. Adv. Funct. Mater. 31, 2005137 (2021)10.1002/adfm.202005137 ↗
16. Eshaghi, M., Ghasemi, M., Khorshidi, K.: Design, manufacturing and applications of small-scale magnetic soft robots. Extreme Mech. Lett. 44, 101268 (2021)10.1016/j.eml.2021.101268 ↗
17. Ferrentino, P., Roels, E., Brancart, J., Terryn, S., Van Assche, G., Vanderborght, B.: Finite element analysis-based soft robotic modeling: simulating a soft actuator in SOFA. IEEE Robot. Autom. Mag. 31, 97–105 (2024)10.1109/MRA.2022.3220536 ↗
18. Ganaie, M.A., Hu, M., Malik, A.K., Tanveer, M., Suganthan, P.N.: Ensemble deep learning: a review. Eng. Appl. Artif. Intell. 115, 105151 (2022)10.1016/j.engappai.2022.105151 ↗
19. Gao, F., Shen, Y., Sallach, J.B., Li, H., Liu, C., Li, Y.: Direct prediction of bioaccumulation of organic contaminants in plant roots from soils with machine learning models based on molecular structures. Environ. Sci. Technol. 55, 16358–16368 (2021)10.1021/acs.est.1c02376 ↗
20. Iban, M.C., Şentürk, E.: Machine learning regression models for prediction of multiple ionospheric parameters. Adv. Space Res. 69, 1319–1334 (2022)10.1016/j.asr.2021.11.026 ↗
21. Jagota, V., Sethi, A.P.S., Kumar, K.: Finite element method: an overview. Walailak J. Sci. Technol. 10, 1–8 (2013)
22. Jin, W., Pei, J., Xie, P., Chen, J., Zhao, H.: Machine learning-based prediction of mechanical properties and performance of nickel–graphene nanocomposites using molecular dynamics simulation data. ACS Appl. Nano Mater. 6, 12190–12199 (2023)10.1021/acsanm.3c01919 ↗
23. Jin, L., Yu, S., Cheng, J., Ye, H., Zhai, X., Jiang, J., Zhang, K., Jian, B., Bodaghi, M., Ge, Q., Liao, W.-H.: Machine learning driven forward prediction and inverse design for 4D printed hierarchical architecture with arbitrary shapes. Appl. Mater. Today 40, 102373 (2024)10.1016/j.apmt.2024.102373 ↗
24. Joyee, E.B., Pan, Y.: A fully three-dimensional printed inchworm-inspired soft robot with magnetic actuation. Soft Robot. 6, 333–345 (2019)10.1089/soro.2018.0082 ↗
25. Ju, Y., Hu, R., Xie, Y., Yao, J., Li, X., Lv, Y., Han, X., Cao, Q., Li, L.: Reconfigurable magnetic soft robots with multimodal locomotion. Nano Energy 87, 106169 (2021)10.1016/j.nanoen.2021.106169 ↗
26. Kim, Y., Zhao, X.: Magnetic soft materials and robots. Chem. Rev. 122, 5317–5364 (2022)10.1021/acs.chemrev.1c00481 ↗
27. Kim, Y., Parada, G.A., Liu, S., Zhao, X.: Ferromagnetic soft continuum robots. Sci. Robot. 4, eaax7329 (2019)10.1126/scirobotics.aax7329 ↗
28. Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A., Anandkumar, A.: Fourier neural operator for parametric partial differential equations. arXiv preprint (2020). arXiv:2010.08895
29. Li, P., Zhang, Y., Gu, J., Duan, S.: Prediction of compressive strength of concrete based on improved artificial bee colony-multilayer perceptron algorithm. Sci. Rep. 14, 6414 (2024a)10.1038/s41598-024-57131-w ↗
30. Li, Z., Chen, L., Ma, Y., Weng, D., Wang, Z., Zhang, X., Wang, J.: Multi-physics coupling simulation and design of magnetic field-driven soft microrobots in liquid environments. Int. J. Mech. Sci. 272, 109136 (2024b)10.1016/j.ijmecsci.2024.109136 ↗
31. Lin, C., Xu, J., Jiang, D., Hou, J., Liang, Y., Zou, Z., Mei, X.: Multi-model ensemble learning for battery state-of-health estimation: recent advances and perspectives. J. Energy Chem. 100, 739–759 (2025)10.1016/j.jechem.2024.09.021 ↗
32. Liu, W.K., Li, S., Park, H.S.: Eighty years of the finite element method: birth, evolution, and future. Arch. Comput. Methods Eng. 29, 4431–4453 (2022)10.1007/s11831-022-09740-9 ↗
33. Lu, L., Jin, P., Pang, G., Zhang, Z., Karniadakis, G.E.: Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nat. Mach. Intell. 3, 218–229 (2021)10.1038/s42256-021-00302-5 ↗
34. Lu, J., Cui, H., Xu, J., Zhang, J., Li, Z.: 4D printing technology based on magnetic intelligent materials: materials, processing processes, and application. 3D Print. Addit. Manuf. 11, 1025–1041 (2024a)10.1089/3dp.2023.0125 ↗
35. Lu, L., Sim, J., Zhao, R.R.: Mechanics of hard-magnetic soft materials: a review. Mech. Mater. 189, 104874 (2024b)10.1016/j.mechmat.2023.104874 ↗
36. Mair, C., Kadoda, G., Lefley, M., Phalp, K., Schofield, C., Shepperd, M., Webster, S.: An investigation of machine learning based prediction systems. J. Syst. Softw. 53, 23–29 (2000)10.1016/S0164-1212(00)00005-4 ↗
37. Markovics, D., Mayer, M.J.: Comparison of machine learning methods for photovoltaic power forecasting based on numerical weather prediction. Renew. Sustain. Energy Rev. 161, 112364 (2022)10.1016/j.rser.2022.112364 ↗
38. Mohammed, A., Kora, R.: A comprehensive review on ensemble deep learning: opportunities and challenges. J. King Saud Univ. Comput. Inf. Sci. 35, 757–774 (2023)10.1016/j.jksuci.2023.01.014 ↗
39. Nath, D., Ankit, N.D.R., Gautam, S.S.: Application of machine learning and deep learning in finite element analysis: a comprehensive review. Arch. Comput. Methods Eng. 31, 2945–2984 (2024)10.1007/s11831-024-10063-0 ↗
40. Niu, H., Feng, R., Xie, Y., Jiang, B., Sheng, Y., Yu, Y., Baoyin, H., Zeng, X.: MagWorm: a biomimetic magnet embedded worm-like soft robot. Soft Robot. 8, 507–518 (2021)10.1089/soro.2019.0167 ↗
41. Peláez-Rodríguez, C., Pérez-Aracil, J., Prieto-Godino, L., Ghimire, S., Deo, R., Salcedo-Sanz, S.: A fuzzy-based cascade ensemble model for improving extreme wind speeds prediction. J. Wind Eng. Ind. Aerodyn. 240, 105507 (2023)10.1016/j.jweia.2023.105507 ↗
42. Périgo, E.A., Faria, R.N.D.: Artificial intelligence—engineering magnetic materials: current status and a brief perspective. Magnetochemistry 7, 84 (2021)10.3390/magnetochemistry7060084 ↗
43. Qin, L., Peng, H., Huang, X., Liu, M., Huang, W.: Modeling and simulation of dynamics in soft robotics: a review of numerical approaches. Curr. Robot. Rep. 5, 1–13 (2023)10.1007/s43154-023-00105-z ↗
44. Raissi, M., Perdikaris, P., Karniadakis, G.E.: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686–707 (2019)10.1016/j.jcp.2018.10.045 ↗
45. Ravichandran, D., Ahmed, R.J., Banerjee, R., Ilami, M., Marvi, H., Miquelard-Garnier, G., Golan, Y., Song, K.: Multi-material 3D printing-enabled multilayers for smart actuationviamagnetic and thermal stimuli. J. Mater. Chem. C 10, 13762–13770 (2022)10.1039/D2TC01109C ↗
46. Sabat, L., Kundu, C.K.: History of finite element method: a review. In: Recent Developments in Sustainable Infrastructure: Select Proceedings of ICRDSI 2019, 2020, pp. 395–404 (2020)10.1007/978-981-15-4577-1_32 ↗
47. Sagi, O., Rokach, L.: Ensemble learning: a survey. WIREs Data Min. Knowl. Discov. 8, e1249 (2018)10.1002/widm.1249 ↗
48. Samaniego, E., Anitescu, C., Goswami, S., Nguyen-Thanh, V.M., Guo, H., Hamdia, K., Zhuang, X., Rabczuk, T.: An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications. Comput. Methods Appl. Mech. Eng. 362, 112790 (2020)10.1016/j.cma.2019.112790 ↗
49. Song, L.-K., Li, X.-Q., Zhu, S.-P., Choy, Y.-S.: Cascade ensemble learning for multi-level reliability evaluation. Aerosp. Sci. Technol. 148, 109101 (2024)10.1016/j.ast.2024.109101 ↗
50. Van Vilsteren, S.J., Yarmand, H., Ghodrat, S.: Review of magnetic shape memory polymers and magnetic soft materials. Magnetochemistry 7, 123 (2021)10.3390/magnetochemistry7090123 ↗
51. Wan, J., Nie, Z., Xu, J., Zhang, Z., Yao, S., Xiang, Z., Lin, X., Lu, Y., Xu, C., Zhao, P.: Millimeter-scale magnetic implants paired with a fully integrated wearable device for wireless biophysical and biochemical sensing. Sci. Adv. 10, eadm9314 (2024)10.1126/sciadv.adm9314 ↗
52. Wang, L., Kim, Y., Guo, C.F., Zhao, X.: Hard-magnetic elastica. J. Mech. Phys. Solids 142, 104045 (2020)10.1016/j.jmps.2020.104045 ↗
53. Wang, H., Zhu, Z., Jin, H., Wei, R., Bi, L., Zhang, W.: Magnetic soft robots: design, actuation, and function. J. Alloys Compd. 922, 166219 (2022)10.1016/j.jallcom.2022.166219 ↗
54. Wang, C., Zhao, Z., Zhang, X.S.: Inverse design of magneto-active metasurfaces and robots: theory, computation, and experimental validation. Comput. Methods Appl. Mech. Eng. 413, 116065 (2023a)10.1016/j.cma.2023.116065 ↗
55. Wang, Z., Weng, D., Li, Z., Chen, L., Ma, Y., Wang, J.: A magnetic-controlled flexible continuum robot with different deformation modes for vascular interventional navigation surgery. Actuators 12(6), 247 (2023b)10.3390/act12060247 ↗
56. Wang, J., Wang, D., Dong, L., Zhang, M., Gu, G.: Analytical modeling and inverse design of centimeter-scale hard-magnetic soft robots. IEEE Trans. Autom. Sci. Eng. 21, 5558–5569 (2024a)10.1109/TASE.2023.3313395 ↗
57. Wang, Y., Bai, J., Lin, Z., Wang, Q., Anitescu, C., Sun, J., Eshaghi, M.S., Gu, Y., Feng, X.-Q., Zhuang, X.: Artificial intelligence for partial differential equations in computational mechanics: a review. arXiv preprint (2024b). arXiv:2410.19843
58. Wang, Y., Sun, J., Rabczuk, T., Liu, Y.: DCEM: a deep complementary energy method for linear elasticity. Int. J. Numer. Methods Eng. 125(24), e7585 (2024c)10.1002/nme.7585 ↗
59. Wang, Y., Zhuang, X., Rabczuk, T., Liu, Y.: AI for PDEs in solid mechanics: a review. Adv. Mech. 54, 1–57 (2024d)
60. Wang, Z., Weng, D., Li, Z., Chen, L., Ma, Y., Wang, J.: Deformation analysis for magnetic soft continuum robots based on minimum potential energy principle. Smart Mater. Struct. 33, 115040 (2024e)10.1088/1361-665X/ad7ca4 ↗
61. Wang, Y., Sun, J., Bai, J., Anitescu, C., Eshaghi, M.S., Zhuang, X., Rabczuk, T., Liu, Y.: Kolmogorov–Arnold-Informed neural network: a physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov-Arnold Networks. Comput. Methods Appl. Mech. Eng. 433, 117518 (2025)10.1016/j.cma.2024.117518 ↗
62. Wu, H., Levinson, D.: The ensemble approach to forecasting: a review and synthesis. Transp. Res. C 132, 103357 (2021)10.1016/j.trc.2021.103357 ↗
63. Wu, S., Hu, W., Ze, Q., Sitti, M., Zhao, R.: Multifunctional magnetic soft composites: a review. Multifunct. Mater. 3, 042003 (2020)10.1088/2399-7532/abcb0c ↗
64. Wu, Z., Wang, Q., Huang, J., Yue, Y., Chen, D., Shi, Y., Su, B.: The soft NdFeB/Ecoflex composites for soft robot with a considerable magnetostimulated shrinkability. Compos. Sci. Technol. 217, 109129 (2022)10.1016/j.compscitech.2021.109129 ↗
65. Yang, Z., Zhang, L.: Magnetic actuation systems for miniature robots: a review. Adv. Intell. Syst. 2(9), 2000082 (2020)10.1002/aisy.202000082 ↗
66. Yang, H., Li, S., Wu, Y., Bao, X., Xiang, Z., Xie, Y., Pan, L., Chen, J., Liu, Y., Li, R.W.: Advances in flexible magnetosensitive materials and devices for wearable electronics. Adv. Mater. 36, 2311996 (2024)10.1002/adma.202311996 ↗
67. Yin, S., Jia, Z., Li, X., Zhu, J., Xu, Y., Li, T.: Machine-learning-accelerated design of functional structural components in deep-sea soft robots. Extreme Mech. Lett. 52, 101635 (2022)10.1016/j.eml.2022.101635 ↗
68. Zhang, Y., Yang, Q.: A survey on multi-task learning. IEEE Trans. Knowl. Data Eng. 34, 5586–5609 (2022)10.1109/TKDE.2021.3070203 ↗
69. Zhao, Z., Zhang, X.S.: Topology optimization of hard-magnetic soft materials. J. Mech. Phys. Solids 158, 104628 (2022)10.1016/j.jmps.2021.104628 ↗
70. Zhao, R., Kim, Y., Chester, S.A., Sharma, P., Zhao, X.: Mechanics of hard-magnetic soft materials. J. Mech. Phys. Solids 124, 244–263 (2019)10.1016/j.jmps.2018.10.008 ↗
71. Zhao, J., Li, X., Tan, Y., Liu, X., Lu, T., Shi, M.: Smart adhesives via magnetic actuation. Adv. Mater. 34, e2107748 (2022)10.1002/adma.202107748 ↗
72. Zhou, C., Yang, Y., Wang, J., Wu, Q., Gu, Z., Zhou, Y., Liu, X., Yang, Y., Tang, H., Ling, Q.: Ferromagnetic soft catheter robots for minimally invasive bioprinting. Nat. Commun. 12, 5072 (2021)10.1038/s41467-021-25386-w ↗