Prediction through Description

A. Mayr, S. Lehner, A. Mayrhofer, C. Kloss, S. Hochreiter, and J. Brandstetter (2023) Boundary Graph Neural Networks for 3D Simulations. Proceedings of the AAAI Conference on Artificial Intelligence, 37, 8, 9099-9107, 2023-06-26. (more) (download)

L. Lewis, H.-Y. Huang, V. T. Tran, S. Lehner, R. Kueng, and J. Preskill (2023) Improved Machine Learning Algorithm for Predicting Ground State Properties. arXiv:2301.13169, 2023-01-30. (more) (download)

V. T. Tran, L. Lewis, H.-Y. Huang, J. Kofler, R. Kueng, S. Hochreiter, and S. Lehner (2022) Using Shadows to Learn Ground State Properties of Quantum Hamiltonians. Machine Learning and the Physical Sciences at NeurIPS 2022, 2022-12-03. (more) (download)

S. Sanokowski, W. Berghammer, J. Kofler, S. Hochreiter, and S. Lehner (2022) One Network to Approximate Them All: Amortized Variational Inference of Ising Ground States. Machine Learning and the Physical Sciences at NeurIPS 2022, 2022-12-03. (more) (download)

P. M. Winter, C. Burger, S. Lehner, J. Kofler, T. I. Maindl, and C. M. Schäfer (2022) Residual Neural Networks for the Prediction of Planetary Collision Outcomes. Monthly Notices of the Royal Astronomical Society, 520, 1, 1224-1242, 2022-10-14. (more) (download)

T. Adler, M. Erhard, M. Krenn, J. Brandstetter, J. Kofler, and S. Hochreiter (2021) Quantum Optical Experiments Modeled by Long Short-Term Memory. Photonics, 8, 12, 535, 2021-11-26. (more) (download)

D. Flam-Shepherd, T. Wu, X. Gu, A. Cervera-Lierta, M. Krenn, and A. Aspuru-Guzik (2021) Learning Interpretable Representations of Entanglement in Quantum Optics Experiments using Deep Generative Models. arXiv:2109.02490, 2021-09-06. (more) (download)

M. Krenn, J. Kottmann, N. Tischler, and A. Aspuru-Guzik (2021) Conceptual Understanding through Efficient Automated Design of Quantum Optical Experiments. Physical Review X, 11, 3, 031044, 2021-08-26. (more) (download)

A. Mayr, S. Lehner, A. Mayrhofer, C. Kloss, S. Hochreiter, and J. Brandstetter (2021) Learning 3D Granular Flow Simulations. arXiv: 2105.01636, 2021-05-04. (more) (download)

M. Reitner, P. Chalupa, L. D. Re, D. Springer, S. Ciuchi, G. Sangiovanni, and A. Toschi (2020) Attractive Effect of a Strong Electronic Repulsion: the Physics of Vertex Divergences. Physical Review Letters, 125, 19, 196403, 2020-11-05. (more) (download)

D. Springer, B. Kim, P. Liu, S. Khmelevskyi, S. Adler, M. Capone, G. Sangiovanni, C. Franchini, and a. A. Toschi (2020) Osmates on the Verge of a Hund’s-Mott Transition: The Different Fates of NaOsO3 and LiOsO3. Physical Review Letters, 125, 16, 166402, 2020-10-16. (more) (download)

T. Adler, M. Erhard, M. Krenn, J. Brandstetter, J. Kofler, and S. Hochreiter (2019) LSTM-Designed Quantum Experiments. Machine Learning and the Physical Sciences Workshop at NeurIPS 2019, Vancouver, 10-12 Dec 2019. (more) (download)