skip to main content
research-article

Differentiable solver for time-dependent deformation problems with contact

Published:22 May 2024Publication History
Skip Abstract Section

Abstract

We introduce a general differentiable solver for time-dependent deformation problems with contact and friction. Our approach uses a finite element discretization with a high-order time integrator coupled with the recently proposed incremental potential contact method for handling contact and friction forces to solve ODE- and PDE-constrained optimization problems on scenes with complex geometry. It supports static and dynamic problems and differentiation with respect to all physical parameters involved in the physical problem description, which include shape, material parameters, friction parameters, and initial conditions. Our analytically derived adjoint formulation is efficient, with a small overhead (typically less than 10% for nonlinear problems) over the forward simulation, and shares many similarities with the forward problem, allowing the reuse of large parts of existing forward simulator code.

We implement our approach on top of the open-source PolyFEM library and demonstrate the applicability of our solver to shape design, initial condition optimization, and material estimation on both simulated results and physical validations.

Skip Supplemental Material Section

Supplemental Material

tog-22-0099-file005.mp4

mp4

117.9 MB

REFERENCES

  1. Alappat Christie, Basermann Achim, Bishop Alan R., Fehske Holger, Hager Georg, Schenk Olaf, Thies Jonas, and Wellein Gerhard. 2020. A recursive algebraic coloring technique for hardware-efficient symmetric sparse matrix-vector multiplication. ACM Trans. Parallel Comput. 7, 3, Article 19 (June2020), 37 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Allaire Grégoire, Dapogny Charles, and Jouve François. 2021. Chapter 1 - Shape and topology optimization. In Geometric Partial Differential Equations - Part II, Bonito Andrea and Nochetto Ricardo H. (Eds.). Handbook of Numerical Analysis, Vol. 22. Elsevier, 1132. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  3. Alnaes M. S., Blechta J., Hake J., Johansson A., Kehlet B., Logg A., Richardson C., Ring J., Rognes M. E., and Wells G. N.. 2015. The FEniCS project version 1.5. Archive of Numerical Software 3 (2015). DOI:Google ScholarGoogle ScholarCross RefCross Ref
  4. Bächer Moritz, Knoop Espen, and Schumacher Christian. 2021. Design and control of soft robots using differentiable simulation. Current Robotics Reports (2021), 111.Google ScholarGoogle Scholar
  5. Baque Pierre, Remelli Edoardo, Fleuret François, and Fua Pascal. 2018. Geodesic convolutional shape optimization. In International Conference on Machine Learning. PMLR, 472481.Google ScholarGoogle Scholar
  6. Belytschko Ted, Liu Wing Kam, and Moran Brian. 2000. Nonlinear Finite Elements for Continua and Structures. John Wiley & Sons, Ltd.Google ScholarGoogle Scholar
  7. Beremlijski P., Haslinger J., Outrata J., and Pathó R.. 2014. Shape optimization in contact problems with Coulomb friction and a solution-dependent friction coefficient. SIAM Journal on Control and Optimization 52, 5 (Jan.2014), 33713400. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Bern James, Banzet Pol, Poranne Roi, and Coros Stelian. 2019. Trajectory optimization for cable-driven soft robot locomotion. In Robotics: Science and Systems XV, Vol. 1. Robotics: Science and Systems Foundation. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  9. Bern James M., Schnider Yannick, Banzet Pol, Kumar Nitish, and Coros Stelian. 2020. Soft robot control with a learned differentiable model. In 2020 3rd IEEE International Conference on Soft Robotics (RoboSoft ’20). IEEE, 417423. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  10. Bischof C. H. and Bücker H. M.. 2000. Computing derivatives of computer programs. In Modern Methods and Algorithms of Quantum Chemistry: Proceedings, Second Edition, Grotendorst J. (Ed.). NIC Series, Vol. 3. NIC-Directors, Jülich, 315327. http://hdl.handle.net/2128/6053Google ScholarGoogle Scholar
  11. Bollhöfer Matthias, Eftekhari Aryan, Scheidegger Simon, and Schenk Olaf. 2019. Large-scale sparse inverse covariance matrix estimation. SIAM Journal on Scientific Computing 41, 1 (2019), A380–A401. DOI:arXiv:https://doi.org/10.1137/17M1147615Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Bollhöfer Matthias, Schenk Olaf, Janalik Radim, Hamm Steve, and Gullapalli Kiran. 2020. State-of-the-art sparse direct solvers. (2020), 333. Google ScholarGoogle ScholarCross RefCross Ref
  13. Bridson Robert, Fedkiw Ronald, and Anderson John. 2002. Robust treatment of collisions, contact and friction for cloth animation. ACM Trans. on Graph. 21 (052002).Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Brogliato Bernard. 1999. Nonsmooth Mechanics. Springer-Verlag.Google ScholarGoogle ScholarCross RefCross Ref
  15. Brown George E., Overby Matthew, Forootaninia Zahra, and Narain Rahul. 2018. Accurate dissipative forces in optimization integrators. ACM Trans. Graph. 37, 6, Article 282 (Dec.2018), 14 pages. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Chang Michael B., Ullman Tomer, Torralba Antonio, and Tenenbaum Joshua B.. 2016. A compositional object-based approach to learning physical dynamics. arXiv preprint arXiv:1612.00341 (2016).Google ScholarGoogle Scholar
  17. Chen Bicheng, Wang Nianfeng, Zhang Xianmin, and Chen Wei. 2020. Design of dielectric elastomer actuators using topology optimization on electrodes. Smart Mater. Struct. 29, 7 (June2020), 075029. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  18. Daviet Gilles, Bertails-Descoubes Florence, and Boissieux Laurence. 2011. A hybrid iterative solver for robustly capturing Coulomb friction in hair dynamics. ACM Trans. on Graph. 30 (122011).Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Vaucorbeil Alban de, Nguyen Vinh Phu, Sinaie Sina, and Wu Jian Ying. 2019. Material point method after 25 years: Theory, implementation and applications. Submitted to Advances in Applied Mechanics (2019), 1.Google ScholarGoogle Scholar
  20. Desmorat B.. 2007. Structural rigidity optimization with frictionless unilateral contact. International Journal of Solids and Structures 44, 3 (Feb.2007), 11321144. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  21. Dokken Jørgen S., Mitusch Sebastian K., and Funke Simon W.. 2020. Automatic Shape Derivatives for Transient PDEs in FEniCS and Firedrake. (2020). arxiv:math.OC/2001.10058Google ScholarGoogle Scholar
  22. Du Tao, Wu Kui, Ma Pingchuan, Wah Sebastien, Spielberg Andrew, Rus Daniela, and Matusik Wojciech. 2021. DiffPD: Differentiable projective dynamics. ACM Trans. Graph. 41, 2, Article 13 (Nov.2021), 21 pages. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Eck Christof, Jarusek Jiri, Krbec Miroslav, Jarusek Jiri, and Krbec Miroslav. 2005. Unilateral Contact Problems: Variational Methods and Existence Theorems. CRC Press. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  24. Zachary Ferguson and others. 2020. IPC Toolkit. Retrieved from https://github.com/ipc-sim/ipc-toolkitGoogle ScholarGoogle Scholar
  25. Ferguson Zachary, Li Minchen, Schneider Teseo, Gil-Ureta Francisca, Langlois Timothy, Jiang Chenfanfu, Zorin Denis, Kaufman Danny M., and Panozzo Daniele. 2021. Intersection-free rigid body dynamics. ACM Transactions on Graphics (SIGGRAPH) 40, 4, Article 183 (2021).Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Gavriil Konstantinos, Guseinov Ruslan, Pérez Jesús, Pellis Davide, Henderson Paul, Rist Florian, Pottmann Helmut, and Bickel Bernd. 2020. Computational design of cold bent glass FaçAdes. ACM Trans. Graph. 39, 6, Article 208 (Nov.2020), 16 pages. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Geilinger Moritz, Hahn David, Zehnder Jonas, Bächer Moritz, Thomaszewski Bernhard, and Coros Stelian. 2020. ADD: Analytically differentiable dynamics for multi-body systems with frictional contact. ACM Transactions on Graphics (TOG) 39, 6 (2020), 115.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Geuzaine Christophe and Remacle Jean-François. 2009. Gmsh: A 3-D finite element mesh generator with built-in pre- and post-processing facilities. Internat. J. Numer. Methods Engrg. 79, 11 (2009), 13091331. DOI:arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/nme.2579Google ScholarGoogle ScholarCross RefCross Ref
  29. Griewank Andreas and Walther Andrea. 2008. Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation. Vol. 105. SIAM. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  30. Hafner Christian, Schumacher Christian, Knoop Espen, Auzinger Thomas, Bickel Bernd, and Bächer Moritz. 2019. X-CAD: Optimizing CAD models with extended finite elements. ACM Trans. Graph. 38, 6, Article 157 (Nov.2019), 15 pages. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Hahn David, Banzet Pol, Bern James M., and Coros Stelian. 2019. Real2Sim: Visco-elastic parameter estimation from dynamic motion. ACM Trans. Graph. 38, 6, Article 236 (Nov.2019), 13 pages. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Harmon David, Vouga Etienne, Smith Breannan, Tamstorf Rasmus, and Grinspun Eitan. 2009. Asynchronous contact mechanics. In ACM Trans. on Graph. (TOG ’09), Vol. 28. ACM.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Harmon David, Vouga Etienne, Tamstorf Rasmus, and Grinspun Eitan. 2008. Robust treatment of simultaneous collisions. SIGGRAPH (ACM Trans. on Graph.) 27, 3 (2008).Google ScholarGoogle Scholar
  34. Haslinger Jaroslav, Neittaanmaki Pekka, and Tiihonen Timo. 1986. Shape optimization in contact problems based on penalization of the state inequality. Aplikace Matematiky 31, 1 (1986), 5477. https://eudml.org/doc/15435Google ScholarGoogle Scholar
  35. Heiden Eric, Macklin Miles, Narang Yashraj S., Fox Dieter, Garg Animesh, and Ramos Fabio. 2021. DiSECt: A differentiable simulation engine for autonomous robotic cutting. In Proceedings of Robotics: Science and Systems. Virtual. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  36. Heiden Eric, Millard David, Coumans Erwin, Sheng Yizhou, and Sukhatme Gaurav S.. 2020. NeuralSim: Augmenting differentiable simulators with neural networks. arXiv preprint arXiv:2011.04217 (2020).Google ScholarGoogle Scholar
  37. Herskovits J., Leontiev A., Dias G., and Santos G.. 2000. Contact shape optimization: A bilevel programming approach. Structural and Multidisciplinary Optimization 20, 3 (Nov.2000), 214221. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Hoshyari Shayan, Xu Hongyi, Knoop Espen, Coros Stelian, and Bächer Moritz. 2019. Vibration-minimizing motion retargeting for robotic characters. ACM Trans. Graph. 38, 4 (July2019), 114. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Hsu Jerry, Truong Nghia, Yuksel Cem, and Wu Kui. 2022. A general two-stage initialization for sag-free deformable simulations. ACM Trans. Graph. 41, 4, Article 64 (Jul.2022), 13 pages. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Hu Yuanming, Anderson Luke, Li Tzu-Mao, Sun Qi, Carr Nathan, Ragan-Kelley Jonathan, and Durand Fredo. 2019a. DiffTaichi: Differentiable programming for physical simulation. In International Conference on Learning Representations.Google ScholarGoogle Scholar
  41. Hu Yuanming, Liu Jiancheng, Spielberg Andrew, Tenenbaum Joshua B., Freeman William T., Wu Jiajun, Rus Daniela, and Matusik Wojciech. 2019b. ChainQueen: A real-time differentiable physical simulator for soft robotics. In 2019 International Conference on Robotics and Automation (ICRA ’19). IEEE, 62656271.Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Hu Yixin, Schneider Teseo, Wang Bolun, Zorin Denis, and Panozzo Daniele. 2020. Fast tetrahedral meshing in the wild. ACM Trans. Graph. 39, 4, Article 117 (July2020), 18 pages. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Jakob Wenzel. 2010. Mitsuba Renderer. (2010). http://www.mitsuba-renderer.orgGoogle ScholarGoogle Scholar
  44. Jatavallabhula Krishna Murthy, Macklin Miles, Golemo Florian, Voleti Vikram, Petrini Linda, Weiss Martin, Considine Breandan, Parent-Levesque Jerome, Xie Kevin, Erleben Kenny, Paull Liam, Shkurti Florian, Nowrouzezahrai Derek, and Fidler Sanja. 2021. gradSim: Differentiable simulation for system identification and visuomotor control. International Conference on Learning Representations (ICLR) (2021). https://openreview.net/forum?id=c_E8kFWfhp0Google ScholarGoogle Scholar
  45. Jiang Zhongshi, Schneider Teseo, Zorin Denis, and Panozzo Daniele. 2020. Bijective projection in a shell. ACM Trans. Graph. 39, 6, Article 247 (Nov.2020), 18 pages. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Kikuchi Noboru and Oden John Tinsley. 1988. Contact Problems in Elasticity: A Study of Variational Inequalities and Finite Element Methods. SIAM Studies in App. and Numer. Math., Vol. 8. Society for Industrial and Applied Mathematics.Google ScholarGoogle ScholarCross RefCross Ref
  47. Knupp Patrick M.. 2001. Algebraic mesh quality metrics. SIAM Journal on Scientific Computing 23, 1 (2001), 193218. DOI:arXiv:https://doi.org/10.1137/S1064827500371499Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Li Minchen, Ferguson Zachary, Schneider Teseo, Langlois Timothy, Zorin Denis, Panozzo Daniele, Jiang Chenfanfu, and Kaufman Danny M.. 2020. Incremental potential contact: Intersection- and inversion-free large deformation dynamics. ACM Transactions on Graphics 39, 4 (2020).Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Li Minchen, Ferguson Zachary, Schneider Teseo, Langlois Timothy, Zorin Denis, Panozzo Daniele, Jiang Chenfanfu, and Kaufman Danny M.. 2023a. Convergent Incremental Potential Contact. (2023). arxiv:math.NA/2307.15908Google ScholarGoogle Scholar
  50. Li Yifei, Du Tao, Wu Kui, Xu Jie, and Matusik Wojciech. 2022. DiffCloth: Differentiable cloth simulation with dry frictional contact. ACM Trans. Graph. (Mar.2022). DOI:Just Accepted.Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Li Zhehao, Xu Qingyu, Ye Xiaohan, Ren Bo, and Liu Ligang. 2023b. DiffFR: Differentiable SPH-based fluid-rigid coupling for rigid body control. ACM Trans. Graph. 42, 6, Article 179 (Dec.2023), 17 pages. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Liang Junbang, Lin Ming, and Koltun Vladlen. 2019. Differentiable cloth simulation for inverse problems. Neural Information Processing Systems (2019).Google ScholarGoogle Scholar
  53. Ly Mickaël, Casati Romain, Bertails-Descoubes Florence, Skouras Mélina, and Boissieux Laurence. 2018. Inverse elastic shell design with contact and friction. ACM Trans. Graph. 37, 6, Article 201 (Dec.2018), 16 pages. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Maloisel Guirec, Knoop Espen, Schumacher Christian, and Bacher Moritz. 2021. Automated routing of muscle fibers for soft robots. IEEE Trans. Robot. 37, 3 (June2021), 9961008. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  55. Margossian Charles C.. 2019. A review of automatic differentiation and its efficient implementation. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 9, 4 (2019), e1305. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  56. Maury Aymeric, Allaire Grégoire, and Jouve François. 2017. Shape Optimisation with the Level Set Method for Contact Problems in Linearised Elasticity. (Jan.2017). https://hal.archives-ouvertes.fr/hal-01435325Google ScholarGoogle Scholar
  57. McNamara Antoine, Treuille Adrien, Popović Zoran, and Stam Jos. 2004. Fluid control using the adjoint method. ACM Transactions on Graphics / SIGGRAPH 2004 23, 3 (Aug.2004).Google ScholarGoogle Scholar
  58. Mitusch Sebastian K., Funke Simon W., and Dokken Jørgen S.. 2019. dolfin-adjoint 2018.1: Automated adjoints for FEniCS and Firedrake. Journal of Open Source Software 4, 38 (2019), 1292. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  59. Moses William S., Narayanan Sri Hari Krishna, Paehler Ludger, Churavy Valentin, Schanen Michel, Hückelheim Jan, Doerfert Johannes, and Hovland Paul. 2022. Scalable automatic differentiation of multiple parallel paradigms through compiler augmentation. In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis (SC ’22). IEEE Press, Article 60, 18 pages.Google ScholarGoogle ScholarCross RefCross Ref
  60. Naumann Uwe. 2012. The Art of Differentiating Computer Programs: An Introduction to Algorithmic Differentiation. Vol. 24. SIAM. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  61. Otaduy Miguel, Tamstorf Rasmus, Steinemann Denis, and Gross Markus. 2009. Implicit contact handling for deformable objects. Comp. Graph. Forum 28 (042009).Google ScholarGoogle ScholarCross RefCross Ref
  62. Panetta Julian, Rahimian Abtin, and Zorin Denis. 2017. Worst-case stress relief for microstructures. ACM Transactions on Graphics 36, 4 (2017). DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  63. Panetta Julian, Zhou Qingnan, Malomo Luigi, Pietroni Nico, Cignoni Paolo, and Zorin Denis. 2015. Elastic textures for additive fabrication. ACM Trans. Graph. 34, 4, Article 135 (July2015), 12 pages.Google ScholarGoogle ScholarDigital LibraryDigital Library
  64. Qiao Yi-Ling, Liang Junbang, Koltun Vladlen, and Lin Ming. 2020. Scalable differentiable physics for learning and control. In International Conference on Machine Learning. PMLR, 78477856.Google ScholarGoogle Scholar
  65. Rabinovich Michael, Poranne Roi, Panozzo Daniele, and Sorkine-Hornung Olga. 2017. Scalable locally injective mappings. ACM Transactions on Graphics (TOG) 36, 4 (2017), 1.Google ScholarGoogle ScholarDigital LibraryDigital Library
  66. Rojas Junior, Sifakis Eftychios, and Kavan Ladislav. 2021. Differentiable implicit soft-body physics. arXiv preprint arXiv:2102.05791 (2021).Google ScholarGoogle Scholar
  67. Schenck Connor and Fox Dieter. 2018. SPNets: Differentiable fluid dynamics for deep neural networks. In Proceedings of The 2nd Conference on Robot Learning (Proceedings of Machine Learning Research), Billard Aude, Dragan Anca, Peters Jan, and Morimoto Jun (Eds.), Vol. 87. PMLR, 317335. https://proceedings.mlr.press/v87/schenck18a.htmlGoogle ScholarGoogle Scholar
  68. Schneider Teseo, Dumas Jérémie, Gao Xifeng, Zorin Denis, and Panozzo Daniele. 2019. PolyFEM. https://polyfem.github.io/ (2019).Google ScholarGoogle Scholar
  69. Schumacher Christian, Knoop Espen, and Bacher Moritz. 2020. Simulation-ready characterization of soft robotic materials. IEEE Robot. Autom. Lett. 5, 3 (July2020), 37753782. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  70. Schumacher Christian, Zehnder Jonas, and Bächer Moritz. 2018. Set-in-stone: Worst-case optimization of structures weak in tension. ACM Trans. Graph. 37, 6, Article 252 (Dec.2018), 13 pages. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  71. Shan Sicong, Kang Sung, Raney Jordan, Wang Pai, Fang Lichen, Candido Francisco, Lewis Jennifer, and Bertoldi Katia. 2015. Multistable architected materials for trapping elastic strain energy. Advanced Materials (Deerfield Beach, Fla.) 27 (062015). DOI:Google ScholarGoogle ScholarCross RefCross Ref
  72. Sharma Ashesh and Maute Kurt. 2018. Stress-based topology optimization using spatial gradient stabilized XFEM. Structural and Multidisciplinary Optimization 57, 1 (2018), 1738.Google ScholarGoogle ScholarDigital LibraryDigital Library
  73. Skouras Mélina, Thomaszewski Bernhard, Coros Stelian, Bickel Bernd, and Gross Markus. 2013. Computational design of actuated deformable characters. ACM Trans. Graph. 32, 4, Article 82 (Jul.2013), 10 pages. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  74. Stewart David E.. 2001. Finite-dimensional contact mechanics. Phil. Trans. R. Soc. Lond. A 359 (2001).Google ScholarGoogle ScholarCross RefCross Ref
  75. Stupkiewicz Stanisław, Lengiewicz Jakub, and Korelc Jovze. 2010. Sensitivity analysis for frictional contact problems in the augmented Lagrangian formulation. Computer Methods in Applied Mechanics and Engineering 199, 33 (July2010), 21652176. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  76. Tapia Javier, Knoop Espen, Mutný Mojmir, Otaduy Miguel A., and Bächer Moritz. 2020. MakeSense: Automated sensor design for proprioceptive soft robots. Soft Rob. 7, 3 (June2020), 332345. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  77. Tozoni Davi Colli, Dumas Jérémie, Jiang Zhongshi, Panetta Julian, Panozzo Daniele, and Zorin Denis. 2020. A low-parametric rhombic microstructure family for irregular lattices. ACM Trans. Graph. 39, 4, Article 101 (Jul.2020), 20 pages. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  78. Tozoni Davi Colli, Zhou Yunfan, and Zorin Denis. 2021. Optimizing contact-based assemblies. ACM Trans. Graph. 40, 6, Article 269 (Dec.2021), 19 pages. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  79. Keulen F. van, Haftka R. T., and Kim N. H.. 2005. Review of options for structural design sensitivity analysis. Part 1: Linear systems. Computer Methods in Applied Mechanics and Engineering 194, 30 (2005), 32133243. DOI:Structural and Design Optimization.Google ScholarGoogle ScholarCross RefCross Ref
  80. Verschoor Mickeal and Jalba Andrei C.. 2019. Efficient and accurate collision response for elastically deformable models. ACM Trans. on Graph. (TOG) 38, 2 (2019).Google ScholarGoogle ScholarDigital LibraryDigital Library
  81. Wieschollek Patrick. 2016. CppOptimizationLibrary. https://github.com/PatWie/CppNumericalSolversGoogle ScholarGoogle Scholar
  82. Wriggers Peter. 1995. Finite element algorithms for contact problems. Archives of Comp. Meth. in Eng. 2 (121995).Google ScholarGoogle ScholarCross RefCross Ref
  83. Xu Jie, Makoviychuk Viktor, Narang Yashraj, Ramos Fabio, Matusik Wojciech, Garg Animesh, and Macklin Miles. 2022. Accelerated Policy Learning with Parallel Differentiable Simulation. (2022). DOI:Google ScholarGoogle ScholarCross RefCross Ref
  84. Zhang Xiaoting, Le Xinyi, Wu Zihao, Whiting Emily, and Wang Charlie C. L.. 2016. Data-driven bending elasticity design by shell thickness. Computer Graphics Forum (Proceedings of Symposium on Geometry Processing) 35, 5 (2016), 157166.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Differentiable solver for time-dependent deformation problems with contact

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image ACM Transactions on Graphics
      ACM Transactions on Graphics  Volume 43, Issue 3
      June 2024
      160 pages
      ISSN:0730-0301
      EISSN:1557-7368
      DOI:10.1145/3613683
      Issue’s Table of Contents

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 22 May 2024
      • Online AM: 26 April 2024
      • Accepted: 27 February 2024
      • Revised: 8 February 2024
      • Received: 7 November 2022
      Published in tog Volume 43, Issue 3

      Check for updates

      Qualifiers

      • research-article
    • Article Metrics

      • Downloads (Last 12 months)168
      • Downloads (Last 6 weeks)168

      Other Metrics

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Full Text

    View this article in Full Text.

    View Full Text