Prediction through Description

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 - 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 - 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) Boundary Graph Neural Networks for 3D Simulations. arXiv:2106.11299, 2021-06-21. (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)