Fetching the paper…
Reading the bibliography…
Humans intuitively recognize objects' physical properties and predict their motion, even when the objects are engaged in complicated interactions.
Non-linear system identification using neural networks
Chen, S., Billings, S., and Grant, P · 1990
Earlier work this paper cites.
System identification
Ljung, L · 2001
Earlier work this paper cites.
Model predictive neural control of a high-fidelity helicopter model
Wan, E., Baptista, A., Carlsson, M., Kiebutz, R., Zhang, Y., and Bogdanov, A · 2001
Earlier work this paper cites.
Beyond pixels: exploring new representations and applications for motion analysis
Liu, C. et al · 2009
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Todorov, E., Erez, T., and Tassa, Y · 2012
Earlier work this paper cites.
Simulation as an engine of physical scene understanding
Battaglia, P. W., Hamrick, J. B., and Tenenbaum, J. B · 2013
Earlier work this paper cites.
Position based fluids
Macklin, M. and Müller, M · 2013
Earlier work this paper cites.
Empirical evaluation of gated recurrent neural networks on sequence modeling
Chung, J., Gulcehre, C., Cho, K., and Bengio, Y · 2014
Earlier work this paper cites.
Unified particle physics for real-time applications
Macklin, M., Müller, M., Chentanez, N., and Kim, T.-Y · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
Earlier work this paper cites.
Galileo: Perceiving physical object properties by integrating a physics engine with deep learning
Wu, J., Yildirim, I., Lim, J. J., Freeman, W. T., and Tenenbaum, J. B · 2015
Earlier work this paper cites.
Interaction networks for learning about objects, relations and physics
Battaglia, P. W., Pascanu, R., Lai, M., Rezende, D., and Kavukcuoglu, K · 2016
Earlier work this paper cites.
Learning visual predictive models of physics for playing billiards
Fragkiadaki, K., Agrawal, P., Levine, S., and Malik, J · 2016
Earlier work this paper cites.
Inferring mass in complex scenes by mental simulation
Hamrick, J. B., Battaglia, P. W., Griffiths, T. L., and Tenenbaum, J. B · 2016
Earlier work this paper cites.
A compositional object-based approach to learning physical dynamics
Chang, M. B., Ullman, T., Torralba, A., and Tenenbaum, J. B · 2017
Cited alongside, same era.
A point set generation network for 3d object reconstruction from a single image
Fan, H., Su, H., and Guibas, L · 2017
Cited alongside, same era.
Deep visual foresight for planning robot motion
Finn, C. and Levine, S · 2017
Cited alongside, same era.
Visual interaction networks
Watters, N., Tacchetti, A., Weber, T., Pascanu, R., Battaglia, P., and Zoran, D · 2017
Cited alongside, same era.
Learning to see physics via visual de-animation
Wu, J., Lu, E., Kohli, P., Freeman, B., and Tenenbaum, J · 2017
Cited alongside, same era.
Stochastic variational video prediction
Babaeizadeh, M., Finn, C., Erhan, D., Campbell, R. H., and Levine, S · 2018
Cited alongside, same era.
Learning latent dynamics for planning from pixels
Hafner, D., Lillicrap, T., Fischer, I., Villegas, R., Ha, D., Lee, H., and Davidson, J · 2019
Later among the works it cites.
Chainqueen: A real-time differentiable physical simulator for soft robotics
Hu, Y., Liu, J., Spielberg, A., Tenenbaum, J. B., Freeman, W. T., Wu, J., Rus, D., and Matusik, W · 2019
Later among the works it cites.
Reasoning about physical interactions with object-oriented prediction and planning
Janner, M., Levine, S., Freeman, W. T., Tenenbaum, J. B., Finn, C., and Wu, J · 2019
Later among the works it cites.
Differentiable cloth simulation for inverse problems
Liang, J., Lin, M., and Koltun, V · 2019
Later among the works it cites.
Unsupervised learning of object structure and dynamics from videos
Minderer, M., Sun, C., Villegas, R., Cole, F., Murphy, K. P., and Lee, H · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
End-to-end differentiable physics for learning and control
de Avila Belbute-Peres, F., Smith, K., Allen, K., Tenenbaum, J., and Kolter, J. Z · 2018
Cited alongside, same era.
Recurrent world models facilitate policy evolution
Ha, D. and Schmidhuber, J · 2018
Cited alongside, same era.
Flexible neural representation for physics prediction
Mrowca, D., Zhuang, C., Wang, E., Haber, N., Fei-Fei, L., Tenenbaum, J. B., and Yamins, D. L · 2018
Cited alongside, same era.
Graph networks as learnable physics engines for inference and control
Sanchez-Gonzalez, A., Heess, N., Springenberg, J. T., Merel, J., Riedmiller, M., Hadsell, R., and Battaglia, P · 2018
Cited alongside, same era.
Spnets: Differentiable fluid dynamics for deep neural networks
Schenck, C. and Fox, D · 2018
Cited alongside, same era.
Modeling human intuitions about liquid flow with particle-based simulation
Bates, C. J., Yildirim, I., Tenenbaum, J. B., and Battaglia, P · 2019
Cited alongside, same era.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
Later among the works it cites.
Drake: Model-based design and verification for robotics, 2019
Tedrake, R. and the Drake Development Team · 2019
Later among the works it cites.
Densephysnet: Learning dense physical object representations via multi-step dynamic interactions
Xu, Z., Wu, J., Zeng, A., Tenenbaum, J. B., and Song, S · 2019
Later among the works it cites.
Difftaichi: Differentiable programming for physical simulation
Hu, Y., Anderson, L., Li, T.-M., Sun, Q., Carr, N., Ragan-Kelley, J., and Durand, F · 2020
Closest in time.
Learning compositional koopman operators for model-based control
Li, Y., He, H., Wu, J., Katabi, D., and Torralba, A · 2020
Closest in time.
Learning to simulate complex physics with graph networks
Sanchez-Gonzalez, A., Godwin, J., Pfaff, T., Ying, R., Leskovec, J., and Battaglia, P. W · 2020
Closest in time.
Lagrangian fluid simulation with continuous convolutions
Ummenhofer, B., Prantl, L., Thuerey, N., and Koltun, V · 2020
Closest in time.
{CLEVRER}: Collision events for video representation and reasoning
Yi, K., Gan, C., Li, Y., Kohli, P., Wu, J., Torralba, A., and Tenenbaum, J. B · 2020
Closest in time.