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The paper surveys automated scientific discovery, from equation discovery and symbolic regression to autonomous discovery systems and agents.
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Udrescu, S.-M., Tan, A., Feng, J., Neto, O., Wu, T., Tegmark, M.: Ai feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity. In: Advances in Neural Information Processing Systems 33 (2020)
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Cranmer, M.D., Sanchez-Gonzalez, A., Battaglia, P.W., Xu, R., Cranmer, K., Spergel, D.N., Ho, S.: Discovering symbolic models from deep learning with inductive biases. In: Advances in Neural Information Processing Systems 33 (2020)
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Burger, B., Maffettone, P.M., Gusev, V.V., Aitchison, C.M., Bai, Y., Wang, X., Li, X., Alston, B.M., Li, B., Clowes, R., Rankin, N., Harris, B., Sprick, R.S., Cooper, A.I.: A mobile robotic chemist. Nature 583
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Shojaee, P., Meidani, K., Farimani, A.B., Reddy, C.K.: Transformer-based Planning for Symbolic Regression (2023) https://doi.org/10.48550/ARXIV.2303.06833 . Publisher: arXiv Version Number: 4. Accessed 2023-08-09
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Brence, J., Todorovski, L., Džeroski, S.: Probabilistic grammars for equation discovery. Knowledge Based Systems 224
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2021
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Petersen, B.K., Larma, M.L., Mundhenk, T.N., Santiago, C.P., Kim, S.K., Kim, J.T.: Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients. In: Proceedings of the 9th International Conference on Learning Representations (ICLR 2021) (2021)
2021
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Biggio, L., Bendinelli, T., Neitz, A., Lucchi, A., Parascandolo, G.: Neural symbolic regression that scales. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event. Proceedings of Machine Learning Research, vol. 139, pp. 936–945. PMLR, Virtual event (2021). http://proceedings.mlr.press/v139/biggio21a.html
2021
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2021
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Lu, L., Jin, P., Pang, G., Zhang, Z., Karniadakis, G.E.: Learning nonlinear operators via deeponet based on the universal approximation theorem of operators. Nature Machine Intelligence 3
2021
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Li, Z., Kovachki, N.B., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A.M., Anandkumar, A.: Fourier neural operator for parametric partial differential equations. In: Proceedings of the 9th International Conference on Learning Representations (ICLR 2021) (2021)
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2023
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Makke, N., Chawla, S.: Interpretable scientific discovery with symbolic regression: a reviews. Artificial Intelligence Review 57
2024
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Gao, S., Fang, A., Huang, Y., Giunchiglia, V., Noori, A., Schwarz, J.R., Ektefaie, Y., Kondic, J., Zitnik, M.: Empowering biomedical discovery with ai agents. Cell 187
2024
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Langley, P.: Integrated systems for computational scientific discovery. In: Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), pp. 22598–22606. AAAI Press, Vancouver, Canada (2024)
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Bychkov, A., Issan, I., Pogudin, G., Krämer, B.: Exact and optimal quadratization of nonlinear finite-dimensional non-autonomous dynamical systems. SIAM Journal on Applied Dynamical Systems 23
2024
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Sahoo, S., Lampert, C., Martius, G.: Learning Equations for Extrapolation and Control. In: Proceedings of the 35th International Conference on Machine Learning, pp. 4442–4450. PMLR, Stockholm, Sweden (2018). ISSN: 2640-3498. https://proceedings.mlr.press/v80/sahoo18a.html
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2024
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Jansen, P.e.a.: DiscoveryWorld: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents (2024)
2024
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Beeler, C., Subramanian, S.G., Sprague, K., Baula, M., Chatti, N., Dawit, A., Li, X., Paquin, N., Shahen, M., Yang, Z., Bellinger, C., Crowley, M., Tamblyn, I.: Chemgymrl: A customizable interactive framework for reinforcement learning for digital chemistry. Digital Discovery 3
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Cerrato, M., Schmitt, N., Baur, L., Finkelstein, E., Jukic, S., Münzel, L., Paul, F.P., Pfannes, P., Rohr, B., Schellenberg, J., Wolf, P., Kramer, S.: Science-Gym: A simple testbed for ai-driven scientific discovery. In: Proceedings of the 26th International Conference on Discovery Science (DS). Lecture Notes in Computer Science, vol. 15243, pp. 229–243. Springer, Pisa, Italy (2024). https://doi.org/10.1007/978-3-031-78977-9_15 . Gym-compatible simulation library for physics/epidemiology scenarios
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