Prof. Ryan Adams (COS 411, OH by appt)
Seminar 05: 11am-12:20pm (COS 402)
Seminar 06: 1:30pm-2:50pm (COS 402)

There have been exciting recent developments in artificial intelligence for things like images and natural languages, but what about interfacing with the real world? Deep learning, automatic differentiation, Bayesian optimization and other tools from machine learning are starting to impact the way we think about many physical systems, from modeling quantum mechanics to constructing buildings. We're unlocking new ways to accelerate simulations, come up with new designs, fabricate complex structures, and control the behavior of embodied systems. In this seminar, students can work on a variety of problems, from neural network solutions of partial differential equations for nonlinear elasticity to generative models for distant galaxies to differentiable simulation of soft robots. Students don't need to already be experts in these topics but will need to bring enthusiasm to dive into areas outside their computer science expertise. Note that generic machine learning topics are not in scope for the seminar, and neither are projects on, e.g., biology, healthcare, or the social sciences.

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Expectations

• Actively participate in meetings:
  • Attend all the meetings.
  • Show up on time.
  • Post weekly updates to the #snippets channel on Slack.
  • Do whatever pre-reading or preparation is required.
  • Engage with other students on their projects.
  • Provide thoughtful feedback to others.
• Seek help when necessary:
  • First: Ask questions on Slack.
  • Second: Come to TA office hours.
  • Third: Schedule an appointment with Prof. Adams.
• Learn what you need to know to tackle your problem.
• Meet all department IW deadlines.
• Put in the time to do a great project.

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Some Possible Project Types (not exhaustive):

• Implement and extend a paper.
• Apply a machine learning method to a new domain.
• Develop a new model or algorithm to tackle a problem.
• Compare different methods to solve a problem.
• Create a dataset for an interesting problem and establish benchmarks.
• Write a technical survey of ML applied to a class of problems.

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Slack

We will use Slack as our primary form of communication. Some notes:

• Please make sure to join the Slack workspace and ensure that notifications are set in such a way that you can keep up with announcements and such.
• Feel free to post questions, musings, report exciting developments, etc.
• The workspace is shared between both IW seminars (05 and 06) on this topic.
• If you have seminar-specific logistics question, ask them in #seminar05 or #seminar06, respectively.
• If you have a question that is relevant to both seminars, ask it in #general.
• Use the #snippets channel to post weekly updates on your project. These should be short, and should include:
  • What you did last week.
  • What you plan to do in the coming week.
  • Any questions or issues you are having.

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Grading

IWs do not have a clearly defined grading rubric but your grade will depend on the depth and quality of your research work, the quality of your writeup and presentation, attendance and participation in the seminar, and meeting all IW deadlines.

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February 2024

Sun	Mon	Tue	Wed	Thu	Fri	Sat
Jan 28	Jan 29 Introductions and brainstorming	Jan 30 Attend information meeting for all IW students.	Jan 31	1	2	3
4 Post in #snippets by 11:59pm.	5 Present a paper: 5 minutes and 5 slides	6	7	8	9	10
11 Post in #snippets by 11:59pm.	12 Proposal presentations: 5 minutes and 5 slides.	13	14	15	16	17
18 Post in #snippets by 11:59pm.	19 Workshop the proposals. Bring a draft of your written proposal.	20	21	22 Submit written project proposal.	23	24
25 Post in #snippets by 11:59pm.	26 Updates: 5 minute and 5 slides.	27	28	29

March 2024

Sun	Mon	Tue	Wed	Thu	Fri	Sat
					1	2
3 Post in #snippets by 11:59pm.	4 Updates: 5 minutes and 5 slides.	5	6	7	8	9 Submit checkpoint form.
10	11 Spring Break	12 Spring Break	13 Spring Break	14 Spring Break	15 Spring Break	16
17 Post in #snippets by 11:59pm.	18 Updates: 5 minutes and 5 slides.	19	20	21	22	23
24 Post in #snippets by 11:59pm.	25 Updates: 5 minutes and 5 slides.	26 Attend "How to give an IW talk".	27	28	29	30
31 Post in #snippets by 11:59pm.

April 2024

Sun	Mon	Tue	Wed	Thu	Fri	Sat
	1 Updates: 5 minutes and 5 slides.	2	3	4	5	6
7 Post in #snippets by 11:59pm.	8 Practice final presentations	9 Attend "How to write an IW paper".	10	11	12	13
14 Submit oral presentation slides and recording of oral presentation.	15 Updates: 5 minutes and 5 slides	16	17	18	19	20
21 Post in #snippets by 11:59pm.	22 Workshop final papers: bring a draft.	23	24	25	26	27
28 Submit final written report.	29	30

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Papers

General Differential Equations

• Chen, R. T., Rubanova, Y., Bettencourt, J., & Duvenaud, D. K. (2018). Neural Ordinary Differential Equations. Advances in Neural Information Processing Systems, 31.
• Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations. Journal of Computational Physics, 378, 686-707.
• Kovachki, N., Li, Z., Liu, B., Azizzadenesheli, K., Bhattacharya, K., Stuart, A., & Anandkumar, A. (2021). Neural Operator: Learning Maps between Function Spaces. arXiv preprint arXiv:2108.08481.
• Qin, T., Beatson, A., Oktay, D., McGreivy, N., & Adams, R. P. (2022). Meta-PDE: Learning to Solve PDEs Quickly Without a Mesh. arXiv preprint arXiv:2211.01604.
• Brandstetter, J., Welling, M., & Worrall, D. E. (2022, June). Lie Point Symmetry Data Augmentation for Neural PDE Solvers. In International Conference on Machine Learning (pp. 2241-2256). PMLR.
• Gin, C. R., Shea, D. E., Brunton, S. L., & Kutz, J. N. (2021). DeepGreen: Deep Learning of Green’s Functions for Nonlinear Boundary Value Problems. Scientific Reports, 11(1), 21614.
• Brandstetter, J., Worrall, D. E., & Welling, M. (2021, October). Message Passing Neural PDE Solvers. In International Conference on Learning Representations.

Quantum Mechanics

• Pfau, D., Spencer, J. S., Matthews, A. G., & Foulkes, W. M. C. (2020). Ab Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks. Physical Review Research, 2(3), 033429.
• Hermann, J., Schätzle, Z., & Noé, F. (2020). Deep-Neural-Network Solution of the Electronic Schrödinger Equation. Nature Chemistry, 12(10), 891-897.
• Li, X., Li, Z., & Chen, J. (2022). Ab Initio Calculation of Real Solids via Neural Network Ansatz. Nature Communications, 13(1), 7895.
• Gebhart, V., Santagati, R., Gentile, A. A., Gauger, E. M., Craig, D., Ares, N., ... & Bonato, C. (2023). Learning Quantum Systems. Nature Reviews Physics, 5(3), 141-156.
• Kochkov, D., Pfaff, T., Sanchez-Gonzalez, A., Battaglia, P., & Clark, B. K. (2021). Learning Ground States of Quantum Hamiltonians with Graph Networks. arXiv preprint arXiv:2110.06390.
• Neklyudov, K., Nys, J., Thiede, L., Alvarez, J. F. C., Welling, M., & Makhzani, A. (2023, November). Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrödinger Equation. In Thirty-seventh Conference on Neural Information Processing Systems.

Dynamics

• Schoenholz, S. S., & Cubuk, E. D. (2021). JAX, MD: A Framework for Differentiable Physics. Journal of Statistical Mechanics: Theory and Experiment, 2021(12), 124016.
• Greydanus, S., Dzamba, M., & Yosinski, J. (2019). Hamiltonian Neural Networks. Advances in Neural Information Processing Systems, 32.
• Cranmer, M., Greydanus, S., Hoyer, S., Battaglia, P., Spergel, D., & Ho, S. (2020, February). Lagrangian Neural Networks. In ICLR 2020 Workshop on Integration of Deep Neural Models and Differential Equations.
• Finzi, M., Wang, K. A., & Wilson, A. G. (2020). Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints. Advances in Neural Information Processing Systems, 33, 13880-13889.
• Liu, Z., & Tegmark, M. (2021). Machine Learning Conservation Laws from Trajectories. Physical Review Letters, 126(18), 180604.
• Wang, R., & Yu, R. (2021). Physics-Guided Deep Learning for Dynamical Systems: A Survey. arXiv preprint arXiv:2107.01272.
• Legaard, C., Schranz, T., Schweiger, G., Drgoňa, J., Falay, B., Gomes, C., ... & Larsen, P. (2023). Constructing Neural Network Based Models for Simulating Dynamical Systems. ACM Computing Surveys, 55(11), 1-34.

Symmetries

• Alet, F., Doblar, D., Zhou, A., Tenenbaum, J., Kawaguchi, K., & Finn, C. (2021). Noether Networks: Meta-Learning Useful Conserved Quantities. Advances in Neural Information Processing Systems, 34, 16384-16397.
• Liu, Z., & Tegmark, M. (2022). Machine Learning Hidden Symmetries. Physical Review Letters, 128(18), 180201.
• Thomas, N., Smidt, T., Kearnes, S., Yang, L., Li, L., Kohlhoff, K., & Riley, P. (2018). Tensor Field Networks: Rotation-and Translation-Equivariant Neural Networks for 3D Point Clouds. arXiv preprint arXiv:1802.08219.
• Villar, S., Yao, W., Hogg, D. W., Blum-Smith, B., & Dumitrascu, B. (2023). Dimensionless Machine Learning: Imposing Exact Units Equivariance. Journal of Machine Learning Research, 24(109), 1-32.
• Garcia Satorras, V., Hoogeboom, E., Fuchs, F., Posner, I., & Welling, M. (2021). E(n) Equivariant Normalizing Flows. Advances in Neural Information Processing Systems, 34, 4181-4192.
• Van der Pol, E., Worrall, D., van Hoof, H., Oliehoek, F., & Welling, M. (2020). MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning. Advances in Neural Information Processing Systems, 33, 4199-4210.
• Fuchs, F., Worrall, D., Fischer, V., & Welling, M. (2020). SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks. Advances in Neural Information Processing Systems, 33, 1970-1981.
• Adams, R. P., & Orbanz, P. (2023). Representing and Learning Functions Invariant Under Crystallographic Groups. arXiv preprint arXiv:2306.05261.

Fluids

• Kutz, J. N. (2017). Deep Learning in Fluid Dynamics. Journal of Fluid Mechanics, 814, 1-4.
• Kochkov, D., Smith, J. A., Alieva, A., Wang, Q., Brenner, M. P., & Hoyer, S. (2021). Machine Learning–Accelerated Computational Fluid Dynamics. Proceedings of the National Academy of Sciences, 118(21), e2101784118.
• Vinuesa, R., Brunton, S. L., & McKeon, B. J. (2023). The Transformative Potential of Machine Learning for Experiments in Fluid Mechanics. Nature Reviews Physics, 5(9), 536-545.
• Brunton, S. L. (2021). Applying Machine Learning to Study Fluid Mechanics. Acta Mechanica Sinica, 37(12), 1718-1726.
• Vinuesa, R., & Brunton, S. L. (2022). Enhancing Computational Fluid Dynamics with Machine Learning. Nature Computational Science, 2(6), 358-366.
• Liu, B., Tang, J., Huang, H., & Lu, X. Y. (2020). Deep Learning Methods for Super-Resolution Reconstruction of Turbulent Flows. Physics of Fluids, 32(2).

Mechanical Design and Topology Optimization

• Gongora, A. E., Xu, B., Perry, W., Okoye, C., Riley, P., Reyes, K. G., ... & Brown, K. A. (2020). A Bayesian Experimental Autonomous Researcher for Mechanical Design. Science Advances, 6(15), eaaz1708.
• Oktay, D., Mirramezani, M., Medina, E., & Adams, R. P. (2022, September). Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity. In The Eleventh International Conference on Learning Representations.
• Chi, H., Zhang, Y., Tang, T. L. E., Mirabella, L., Dalloro, L., Song, L., & Paulino, G. H. (2021). Universal Machine Learning for Topology Optimization. Computer Methods in Applied Mechanics and Engineering, 375, 112739.
• Shin, S., Shin, D., & Kang, N. (2023). Topology Optimization via Machine Learning and Deep Learning: A Review. Journal of Computational Design and Engineering, 10(4), 1736-1766.
• Tian, Y., Xu, J., Li, Y., Luo, J., Sueda, S., Li, H., ... & Matusik, W. (2022). Assemble them all: Physics-based planning for generalizable assembly by disassembly. ACM Transactions on Graphics (TOG), 41(6), 1-11.
• Sun, X., Cai, C., Adams, R. P., & Rusinkiewicz, S. (2023, October). Gradient-Based Dovetail Joint Shape Optimization for Stiffness. In Proceedings of the 8th ACM Symposium on Computational Fabrication (pp. 1-8).

Material Design

• Merchant, A., Batzner, S., Schoenholz, S. S., Aykol, M., Cheon, G., & Cubuk, E. D. (2023). Scaling Deep Learning for Materials Discovery. Nature, 1-6.
• Guo, K., Yang, Z., Yu, C. H., & Buehler, M. J. (2021). Artificial Intelligence and Machine Learning in Design of Mechanical Materials. Materials Horizons, 8(4), 1153-1172.
• Choudhary, K., DeCost, B., Chen, C., Jain, A., Tavazza, F., Cohn, R., ... & Wolverton, C. (2022). Recent Advances and Applications of Deep Learning Methods in Materials Science. npj Computational Materials, 8(1), 59.
• Moosavi, S. M., Jablonka, K. M., & Smit, B. (2020). The Role of Machine Learning in the Understanding and Design of Materials. Journal of the American Chemical Society, 142(48), 20273-20287.

Astronomy

• Lemos, P., Jeffrey, N., Cranmer, M., Ho, S., & Battaglia, P. (2023). Rediscovering orbital mechanics with machine learning. Machine Learning: Science and Technology, 4(4), 045002.
• Legin, R., Ho, M., Lemos, P., Perreault-Levasseur, L., Ho, S., Hezaveh, Y., & Wandelt, B. (2024). Posterior sampling of the initial conditions of the universe from non-linear large scale structures using score-based generative models. Monthly Notices of the Royal Astronomical Society: Letters, 527(1), L173-L178.
• Hirashima, K., Moriwaki, K., Fujii, M. S., Hirai, Y., Saitoh, T. R., Makino, J., & Ho, S. (2023, October). Surrogate Modeling for Computationally Expensive Simulations of Supernovae in High-Resolution Galaxy Simulations. In NeurIPS 2023 AI for Science Workshop.
• Jamieson, D., Li, Y., de Oliveira, R. A., Villaescusa-Navarro, F., Ho, S., & Spergel, D. N. (2023). Field-level Neural Network Emulator for Cosmological N-body Simulations. The Astrophysical Journal, 952(2), 145.
• Nibauer, J., Belokurov, V., Cranmer, M., Goodman, J., & Ho, S. (2022). Charting Galactic Accelerations with Stellar Streams and Machine Learning. The Astrophysical Journal, 940(1), 22.
• Delgado, A. M., Wadekar, D., Hadzhiyska, B., Bose, S., Hernquist, L., & Ho, S. (2022). Modelling the galaxy–halo connection with machine learning. Monthly Notices of the Royal Astronomical Society, 515(2), 2733-2746.
• Villar, V. A., Cranmer, M., Berger, E., Contardo, G., Ho, S., Hosseinzadeh, G., & Lin, J. Y. Y. (2021). A deep-learning approach for live anomaly detection of extragalactic transients. The Astrophysical Journal Supplement Series, 255(2), 24.
• Villaescusa-Navarro, F., Anglés-Alcázar, D., Genel, S., Spergel, D. N., Somerville, R. S., Dave, R., ... & Bryan, G. L. (2021). The CAMELS project: Cosmology and astrophysics with machine-learning simulations. The Astrophysical Journal, 915(1), 71.
• Miller, A. C., Anderson, L., Leistedt, B., Cunningham, J. P., Hogg, D. W., & Blei, D. M. (2022). Mapping interstellar dust with Gaussian processes. The Annals of Applied Statistics, 16(4), 2672-2692.
• Eilers, A. C., Hogg, D. W., Schölkopf, B., Foreman-Mackey, D., Davies, F. B., & Schindler, J. T. (2022). A generative model for quasar spectra. The Astrophysical Journal, 938(1), 17.

Mechanical Engineering

• Seff, A., Zhou, W., Richardson, N., & Adams, R. P. (2021, October). Vitruvion: A Generative Model of Parametric CAD Sketches. In International Conference on Learning Representations.
• Wu, C., Zhao, H., Nandi, C., Lipton, J. I., Tatlock, Z., & Schulz, A. (2019). Carpentry compiler. ACM Transactions on Graphics (TOG), 38(6), 1-14.
• Wu, R., Xiao, C., & Zheng, C. (2021). DeepCAD: A deep generative network for computer-aided design models. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 6772-6782).
• Jones, R. K., Barton, T., Xu, X., Wang, K., Jiang, E., Guerrero, P., ... & Ritchie, D. (2020). ShapeAssembly: Learning to generate programs for 3d shape structure synthesis. ACM Transactions on Graphics (TOG), 39(6), 1-20.
• Chang, K. H., & Cheng, C. Y. (2020, November). Learning to simulate and design for structural engineering. In International Conference on Machine Learning (pp. 1426-1436). PMLR.
• Favilli, A., Laccone, F., Cignoni, P., Malomo, L., & Giorgi, D. (2024). Geometric deep learning for statics-aware grid shells. Computers & Structures, 292, 107238.
• Bleker, L., Pastrana, R., Ohlbrock, P. O., & D’Acunto, P. (2022, September). Structural Form-Finding Enhanced by Graph Neural Networks. In Design Modelling Symposium Berlin (pp. 24-35). Cham: Springer International Publishing.

Chemistry

• Guo, M., Thost, V., Li, B., Das, P., Chen, J., & Matusik, W. (2021, October). Data-Efficient Graph Grammar Learning for Molecular Generation. In International Conference on Learning Representations.
• Ma, H., Narayanaswamy, A., Riley, P., & Li, L. (2022). Evolving symbolic density functionals. Science Advances, 8(36), eabq0279.
• Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., & Dahl, G. E. (2017, July). Neural message passing for quantum chemistry. In International conference on machine learning (pp. 1263-1272). PMLR.
• Hoogeboom, E., Satorras, V. G., Vignac, C., & Welling, M. (2022, June). Equivariant diffusion for molecule generation in 3d. In International conference on machine learning (pp. 8867-8887). PMLR.

Differentiable Simulation

• Lavin, A., Krakauer, D., Zenil, H., Gottschlich, J., Mattson, T., Brehmer, J., ... & Pfeffer, A. (2021). Simulation intelligence: Towards a new generation of scientific methods. arXiv preprint arXiv:2112.03235.
• Suh, H. J., Simchowitz, M., Zhang, K., & Tedrake, R. (2022, June). Do differentiable simulators give better policy gradients?. In International Conference on Machine Learning (pp. 20668-20696). PMLR.
• Antonova, R., Yang, J., Jatavallabhula, K. M., & Bohg, J. (2023, March). Rethinking optimization with differentiable simulation from a global perspective. In Conference on Robot Learning (pp. 276-286). PMLR.
• Um, K., Brand, R., Fei, Y. R., Holl, P., & Thuerey, N. (2020). Solver-in-the-loop: Learning from differentiable physics to interact with iterative pde-solvers. Advances in Neural Information Processing Systems, 33, 6111-6122.
• Qiao, Y. L., Liang, J., Koltun, V., & Lin, M. C. (2021, July). Efficient differentiable simulation of articulated bodies. In International Conference on Machine Learning (pp. 8661-8671). PMLR.
• Allen, K. R., Lopez-Guevara, T., Stachenfeld, K., Sanchez-Gonzalez, A., Battaglia, P., Hamrick, J., & Pfaff, T. (2022). Physical design using differentiable learned simulators. arXiv preprint arXiv:2202.00728.

Symbolic Regression

• Cranmer, M., Sanchez Gonzalez, A., Battaglia, P., Xu, R., Cranmer, K., Spergel, D., & Ho, S. (2020). Discovering symbolic models from deep learning with inductive biases. Advances in Neural Information Processing Systems, 33, 17429-17442.
• Kamienny, P. A., d'Ascoli, S., Lample, G., & Charton, F. (2022). End-to-end symbolic regression with transformers. Advances in Neural Information Processing Systems, 35, 10269-10281.

Particle Physics

• Shlomi, J., Battaglia, P., & Vlimant, J. R. (2020). Graph neural networks in particle physics. Machine Learning: Science and Technology, 2(2), 021001.
• DeZoort, G., Battaglia, P. W., Biscarat, C., & Vlimant, J. R. (2023). Graph neural networks at the Large Hadron Collider. Nature Reviews Physics, 1-23.

Aerospace

• Brunton, S. L., Nathan Kutz, J., Manohar, K., Aravkin, A. Y., Morgansen, K., Klemisch, J., ... & McDonald, D. (2021). Data-driven aerospace engineering: reframing the industry with machine learning. AIAA Journal, 59(8), 2820-2847.
• Li, J., Du, X., & Martins, J. R. (2022). Machine learning in aerodynamic shape optimization. Progress in Aerospace Sciences, 134, 100849.
• Le Clainche, S., Ferrer, E., Gibson, S., Cross, E., Parente, A., & Vinuesa, R. (2023). Improving aircraft performance using machine learning: a review. Aerospace Science and Technology, 108354.
• Tejero, F., MacManus, D. G., Sanchez-Moreno, F., & Sheaf, C. (2023). Neural network-based multi-point, multi-objective optimisation for transonic applications. Aerospace Science and Technology, 136, 108208.

Robotics

• Zhao, A., Xu, J., Konaković-Luković, M., Hughes, J., Spielberg, A., Rus, D., & Matusik, W. (2020). RoboGrammar: Graph Grammar for Terrain-Optimized Robot Design. ACM Transactions on Graphics (TOG), 39(6), 1-16.
• Hu, Y., Liu, J., Spielberg, A., Tenenbaum, J. B., Freeman, W. T., Wu, J., ... & Matusik, W. (2019, May). ChainQueen: A real-time differentiable physical simulator for soft robotics. In 2019 International conference on robotics and automation (ICRA) (pp. 6265-6271). IEEE.
• Mengaldo, G., Renda, F., Brunton, S. L., Bächer, M., Calisti, M., Duriez, C., ... & Laschi, C. (2022). A concise guide to modelling the physics of embodied intelligence in soft robotics. Nature Reviews Physics, 4(9), 595-610.
• Chen, F., & Wang, M. Y. (2020). Design optimization of soft robots: A review of the state of the art. IEEE Robotics & Automation Magazine, 27(4), 27-43.
• Della Santina, C., Duriez, C., & Rus, D. (2023). Model-Based Control of Soft Robots: A Survey of the State of the Art and Open Challenges. IEEE Control Systems Magazine, 43(3), 30-65.
• Zhuang, Z., Fu, Z., Wang, J., Atkeson, C. G., Schwertfeger, S., Finn, C., & Zhao, H. (2023, August). Robot Parkour Learning. In 7th Annual Conference on Robot Learning.
• Huang, K., Rana, R., Spitzer, A., Shi, G., & Boots, B. (2023, August). DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control. In 7th Annual Conference on Robot Learning.
• Li, Y., Zeng, A., & Song, S. (2023, December). Rearrangement Planning for General Part Assembly. In Conference on Robot Learning (pp. 127-143). PMLR.
• Kasaei, M., Babarahmati, K. K., Li, Z., & Khadem, M. (2023, August). A Data-efficient Neural ODE Framework for Optimal Control of Soft Manipulators. In The Conference on Robot Learning 2023 (pp. 1-14). PMLR.
• Feng, G., Zhang, H., Li, Z., Peng, X. B., Basireddy, B., Yue, L., ... & Levine, S. (2023, March). Genloco: Generalized locomotion controllers for quadrupedal robots. In Conference on Robot Learning (pp. 1893-1903). PMLR.
• Lee, A. X., Devin, C. M., Zhou, Y., Lampe, T., Bousmalis, K., Springenberg, J. T., ... & Nori, F. (2022, January). Beyond pick-and-place: Tackling robotic stacking of diverse shapes. In Conference on Robot Learning (pp. 1089-1131). PMLR.

Manufacturing

• Goh, G. D., Sing, S. L., & Yeong, W. Y. (2021). A review on machine learning in 3D printing: applications, potential, and challenges. Artificial Intelligence Review, 54(1), 63-94.
• Tamir, T. S., Xiong, G., Fang, Q., Yang, Y., Shen, Z., Zhou, M., & Jiang, J. (2023). Machine-learning-based monitoring and optimization of processing parameters in 3D printing. International Journal of Computer Integrated Manufacturing, 36(9), 1362-1378.
• Soori, M., Arezoo, B., & Dastres, R. (2023). Machine learning and artificial intelligence in CNC machine tools, A review. Sustainable Manufacturing and Service Economics, 100009.
• Piovarči, M., Foshey, M., Xu, J., Erps, T., Babaei, V., Didyk, P., ... & Bickel, B. (2022). Closed-loop control of direct ink writing via reinforcement learning. ACM Transactions on Graphics (TOG), 41(4), 1-10.
• Johns, R. L., Wermelinger, M., Mascaro, R., Jud, D., Hurkxkens, I., Vasey, L., ... & Hutter, M. (2023). A framework for robotic excavation and dry stone construction using on-site materials. Science Robotics, 8(84), eabp9758.
• Brion, D. A., & Pattinson, S. W. (2022). Generalisable 3D printing error detection and correction via multi-head neural networks. Nature Communications, 13(1), 4654.
• Sun, X., Roeder, G., Xue, T., Adams, R. P., & Rusinkiewicz, S. (2023, October). More stiffness with less fiber: End-to-end fiber path optimization for 3d-printed composites. In Proceedings of the 8th ACM Symposium on Computational Fabrication (pp. 1-14).

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Tools

• JAX (sophisticated, acclerated automatic differentiation)
• PyTorch (deep learning focused automatic differentiation)
• FLAX (neural network library for JAX)
• Equinox (useful additional JAX tools)
• ChainQueen (differentiable soft physics)
• Varmint (differentiable nonlinear elasticity)
• FENIX (finite element modeling)
• Taichi (GPU acceleration)
• JAX-MD (differentiable molecular dynamics)
• Tanushree's IW Starter Code

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Frequently Asked Questions

• Can I work with a partner? Yes, just make sure that it is clear what parts each person "owns".
• How long should my updates be? Five minutes with five slides.
• What goes into a #snippet? 2-3 bullets on what you did last week, and 2-3 bullets on what you're going to do this week.
• Do I really need to post a #snippet every week? Yes.
• I have to miss class for a reason. That's fine, but you still need to post a #snippet and submit slides as appropriate.
• What compute resources can I use? Check out the various cloud credit programs through Princeton research computing.
• Can I get funding? There is funding available for independent work through SEAS. The deadline is usually pretty early, i.e., Feb 15, so you'll want to get on this pretty rapidly.
• What fabrication facilities can I use? There are a couple of maker spaces: PUL and Keller Center, plus more resources here. If these don't suit your needs, talk to me.