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We propose a hybrid neural network (NN) and PDE approach for learning generalizable PDE dynamics from motion observations.
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Jiang, C., Schroeder, C., Teran, J., Stomakhin, A., and Selle, A · 2016
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Drucker-prager elastoplasticity for sand animation
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Vlassis, N. N., Ma, R., and Sun, W · 2020
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Wang, B., Deng, Y., Kry, P., Ascher, U., Huang, H., and Chen, B · 2020
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Hybrid discrete-continuum modeling of shear localization in granular media
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Vector neurons: A general framework for so (3)-equivariant networks
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Dnn2: A hyper-parameter reinforcement learning game for self-design of neural network based elasto-plastic constitutive descriptions
Fuchs, A., Heider, Y., Wang, K., Sun, W., and Kaliske, M · 2021
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Plasticinelab: A soft-body manipulation benchmark with differentiable physics
Huang, Z., Hu, Y., Du, T., Zhou, S., Su, H., Tenenbaum, J. B., and Gan, C · 2021
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Physics-informed machine learning
Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., and Yang, L · 2021
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Diffaqua: A differentiable computational design pipeline for soft underwater swimmers with shape interpolation
Ma, P., Du, T., Zhang, J. Z., Wu, K., Spielberg, A., Katzschmann, R. K., and Matusik, W · 2021
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Characteristics of dough rheology and the structural, mechanical, and sensory properties of sponge cakes with sweeteners
Marzec, A., Kowalska, J., Domian, E., Galus, S., Ciurzyńska, A., and Kowalska, H · 2021
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Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening
Vlassis, N. N. and Sun, W · 2021
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Augmenting physical models with deep networks for complex dynamics forecasting
Yin, Y., Le Guen, V., Dona, J., de Bézenac, E., Ayed, I., Thome, N., and Gallinari, P · 2021
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A mechanics-informed artificial neural network approach in data-driven constitutive modeling
As’ ad, F., Avery, P., and Farhat, C · 2022
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Virtual elastic objects
Chen, H.-y., Tretschk, E., Stuyck, T., Kadlecek, P., Kavan, L., Vouga, E., and Lassner, C · 2022
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Polyconvex anisotropic hyperelasticity with neural networks
Klein, D. K., Fernández, M., Martin, R. J., Neff, P., and Weeger, O · 2022
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Plasticitynet: Learning to simulate metal, sand, and snow for optimization time integration
Li, X., Cao, Y., Li, M., Yang, Y., Schroeder, C., and Jiang, C · 2022
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Graph neural network-accelerated lagrangian fluid simulation
Li, Z. and Farimani, A. B · 2022
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A learning-based multiscale method and its application to inelastic impact problems
Liu, B., Kovachki, N., Li, Z., Azizzadenesheli, K., Anandkumar, A., Stuart, A. M., and Bhattacharya, K · 2022
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Ma, P., Du, T., Tenenbaum, J. B., Matusik, W., and Gan, C · 2022
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Warp: A high-performance python framework for gpu simulation and graphics
Macklin, M · 2022
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Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation
Sun, X., Bahmani, B., Vlassis, N. N., Sun, W., and Xu, Y · 2022
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Molecular dynamics inferred transfer learning models for finite-strain hyperelasticity of monoclinic crystals: Sobolev training and validations against physical constraints
Vlassis, N. N., Zhao, P., Ma, R., Sewell, T., and Sun, W · 2022
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Softzoo: A soft robot co-design benchmark for locomotion in diverse environments
Wang, T.-H., Ma, P., Spielberg, A. E., Xian, Z., Zhang, H., Tenenbaum, J. B., Rus, D., and Gan, C · 2023
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Fluidlab: A differentiable environment for benchmarking complex fluid manipulation
Xian, Z., Zhu, B., Xu, Z., Tung, H.-Y., Torralba, A., Fragkiadaki, K., and Gan, C · 2023
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