Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 173 tok/s
Gemini 2.5 Pro 48 tok/s Pro
GPT-5 Medium 33 tok/s Pro
GPT-5 High 28 tok/s Pro
GPT-4o 94 tok/s Pro
Kimi K2 177 tok/s Pro
GPT OSS 120B 450 tok/s Pro
Claude Sonnet 4.5 36 tok/s Pro
2000 character limit reached

Micromechanics-Informed Parametric Deep Material Network for Physics Behavior Prediction of Heterogeneous Materials with a Varying Morphology (2309.11814v3)

Published 21 Sep 2023 in cs.CE

Abstract: Deep Material Network (DMN) has recently emerged as a data-driven surrogate model for heterogeneous materials. Given a particular microstructural morphology, the effective linear and nonlinear behaviors can be successfully approximated by such physics-based neural-network like architecture. In this work, a novel micromechanics-informed parametric DMN (MIpDMN) architecture is proposed for multiscale materials with a varying microstructure characterized by several parameters. A single-layer feedforward neural network is used to account for the dependence of DMN fitting parameters on the microstructural ones. Micromechanical constraints are prescribed both on the architecture and the outputs of this new neural network. The proposed MIpDMN is also recast in a multiple physics setting, where physical properties other than the mechanical ones can also be predicted. In the numerical simulations conducted on three parameterized microstructures, MIpDMN demonstrates satisfying generalization capabilities when morphology varies. The effective behaviors of such parametric multiscale materials can thus be predicted and encoded by MIpDMN with high accuracy and efficiency.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (50)
  1. doi:10.1007/s11831-008-9028-8.
  2. doi:10.1016/j.jcp.2016.10.070.
  3. doi:10.1038/s41563-020-00913-0.
  4. doi:10.1016/s0266-3538(98)00120-1.
  5. doi:10.1016/j.compscitech.2003.11.009.
  6. doi:10.1016/j.cam.2009.08.077.
  7. doi:10.1016/j.cma.2018.09.020.
  8. doi:10.1007/978-3-030-87312-7_27.
  9. doi:10.1016/j.jmps.2019.03.004.
  10. doi:10.1016/j.jmps.2020.103984.
  11. doi:10.1016/j.cma.2021.113952.
  12. doi:10.1007/s00466-021-02131-0.
  13. doi:10.1061/jenmdt.emeng-6945.
  14. doi:10.1016/j.cma.2020.112913.
  15. doi:10.1016/j.cma.2021.113914.
  16. doi:10.1007/s00419-022-02213-2.
  17. doi:10.1016/j.ijplas.2022.103484.
  18. doi:10.1016/j.compstruct.2021.114058.
  19. doi:10.1016/j.euromechsol.2021.104384.
  20. doi:10.1016/j.cma.2021.114300.
  21. doi:10.1007/978-1-4684-9286-6.
  22. doi:10.1017/cbo9780511613357.
  23. doi:10.1016/0022-5096(62)90004-2.
  24. doi:10.1007/bf00280908.
  25. doi:10.1007/s00466-019-01704-4.
  26. doi:10.1016/j.cma.2022.115197.
  27. doi:10.1122/1.549945.
  28. doi:10.1016/j.camwa.2015.08.025.
  29. doi:10.1016/0020-7225(70)90066-2.
  30. doi:10.1007/978-1-4613-8919-4_4.
  31. doi:10.1016/j.scriptamat.2005.06.013.
  32. doi:10.1016/j.actamat.2011.06.051.
  33. doi:10.1038/s41598-019-50144-w.
  34. doi:10.1016/j.compositesb.2021.109282.
  35. doi:10.1016/j.jcp.2018.10.045.
  36. doi:10.1177/10812865211057602.
  37. doi:10.1016/j.compscitech.2016.04.009.
  38. doi:10.1007/s10659-022-09977-2.
  39. doi:10.1016/j.commatsci.2004.09.041.
  40. doi:10.1109/cdc.2008.4739058.
  41. doi:10.1080/00401706.2000.10485979.
  42. doi:10.1016/0041-5553(67)90144-9.
  43. doi:10.1016/j.cma.2022.114790.
  44. doi:10.1109/icnn.1993.298623.
  45. doi:10.1016/b978-0-12-819005-0.00008-3.
  46. doi:10.1016/j.advengsoft.2019.03.005.
  47. doi:10.1016/j.cma.2017.11.005.
  48. doi:10.1016/j.euromechsol.2021.104247.
  49. doi:10.1016/j.ijplas.2009.06.003.
  50. doi:10.1002/nme.1620010306.
Citations (4)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Authors (1)

  1. Tianyi Li
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up