Boundary Graph Neural Networks for 3D Simulations
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[1] M. Neubauer,et al. Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges , 2022, ArXiv.
[2] S. Sra,et al. Sign and Basis Invariant Networks for Spectral Graph Representation Learning , 2022, ICLR.
[3] Christoph Schwab,et al. De Rham compatible Deep Neural Networks , 2022, ArXiv.
[4] Daniel A. Roberts,et al. The Principles of Deep Learning Theory , 2021, ArXiv.
[5] Sepp Hochreiter,et al. Learning 3D Granular Flow Simulations , 2021, ArXiv.
[6] Max Welling,et al. E(n) Equivariant Graph Neural Networks , 2021, ICML.
[7] Nathanael Perraudin,et al. Scalable Graph Networks for Particle Simulations , 2020, AAAI.
[8] T. Pfaff,et al. Learning Mesh-Based Simulation with Graph Networks , 2020, International Conference on Learning Representations.
[9] Nicolas Courty,et al. POT: Python Optimal Transport , 2021, J. Mach. Learn. Res..
[10] Vladlen Koltun,et al. Lagrangian Fluid Simulation with Continuous Convolutions , 2020, ICLR.
[11] C. Coetzee. Calibration of the discrete element method: Strategies for spherical and non-spherical particles , 2020 .
[12] Jure Leskovec,et al. Learning to Simulate Complex Physics with Graph Networks , 2020, ICML.
[13] T. Roessler,et al. DEM parameter calibration of cohesive bulk materials using a simple angle of repose test , 2019, Particuology.
[14] Jiajun Wu,et al. Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids , 2018, ICLR.
[15] Andre Pradhana,et al. A moving least squares material point method with displacement discontinuity and two-way rigid body coupling , 2018, ACM Trans. Graph..
[16] Connor Schenck,et al. SPNets: Differentiable Fluid Dynamics for Deep Neural Networks , 2018, CoRL.
[17] R. Zemel,et al. Neural Relational Inference for Interacting Systems , 2018, ICML.
[18] Samuel S. Schoenholz,et al. Neural Message Passing for Quantum Chemistry , 2017, ICML.
[19] Alexander J. Smola,et al. Deep Sets , 2017, 1703.06114.
[20] Leonidas J. Guibas,et al. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[22] Andreas A. Aigner,et al. fastDEM: A method for faster DEM simulations of granular media , 2017 .
[23] Xavier Bresson,et al. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.
[24] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Barbara Solenthaler,et al. Data-driven fluid simulations using regression forests , 2015, ACM Trans. Graph..
[26] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[27] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[28] G. Lodewijks,et al. DEM speedup: Stiffness effects on behavior of bulk material , 2014 .
[29] Christophe Kassiotis,et al. Unified semi-analytical wall boundary conditions in SPH: analytical extension to 3-D , 2014, Numerical Algorithms.
[30] Miles Macklin,et al. Position based fluids , 2013, ACM Trans. Graph..
[31] A. Mangeney,et al. Exact solution for granular flows , 2013 .
[32] A. Nicolis,et al. Dissipation in the effective field theory for hydrodynamics: First order effects , 2012, 1211.6461.
[33] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[34] C. Kloss,et al. Models, algorithms and validation for opensource DEM and CFD-DEM , 2012 .
[35] T. Metzger,et al. Moisture content and residence time distributions in mixed-flow grain dryers , 2011 .
[36] M. V. D. Panne,et al. Displacement Interpolation Using Lagrangian Mass Transport , 2011 .
[37] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[38] Ah Chung Tsoi,et al. The Graph Neural Network Model , 2009, IEEE Transactions on Neural Networks.
[39] David Eberly,et al. Distance Between Point and Triangle in 3D , 2008 .
[40] I. Rothstein,et al. Effective field theory of gravity for extended objects , 2004, hep-th/0409156.
[41] Andreas Klein,et al. A Generalized Kahan-Babuška-Summation-Algorithm , 2005, Computing.
[42] D. Sulsky. Erratum: Application of a particle-in-cell method to solid mechanics , 1995 .
[43] H. Leutwyler. On the foundations of chiral perturbation theory , 1993, hep-ph/9311274.
[44] Steve Plimpton,et al. Fast parallel algorithms for short-range molecular dynamics , 1993 .
[45] P. Cundall,et al. A discrete numerical model for granular assemblies , 1979 .
[46] J. Monaghan,et al. Smoothed particle hydrodynamics: Theory and application to non-spherical stars , 1977 .
[47] L. Lucy. A numerical approach to the testing of the fission hypothesis. , 1977 .
[48] L. Fan,et al. Application of a Discrete Mixing Model to the Study of Mixing of Multicomponent Solid Particles , 1975 .
[49] William Kahan,et al. Pracniques: further remarks on reducing truncation errors , 1965, CACM.