| Modern applications of machine learning in quantum sciences A Dawid, J Arnold, B Requena, A Gresch, M Płodzień, K Donatella, ... arXiv preprint arXiv:2204.04198, 2022 | 95* | 2022 |
| Interpretable and unsupervised phase classification J Arnold, F Schäfer, M Žonda, AUJ Lode Physical Review Research 3 (3), 033052, 2021 | 43 | 2021 |
| Replacing neural networks by optimal analytical predictors for the detection of phase transitions J Arnold, F Schäfer Physical Review X 12 (3), 031044, 2022 | 28 | 2022 |
| Machine learning product state distributions from initial reactant states for a reactive atom–diatom collision system J Arnold, JC San Vicente Veliz, D Koner, N Singh, RJ Bemish, M Meuwly The Journal of Chemical Physics 156 (3), 2022 | 19 | 2022 |
| Machine learning for observables: Reactant to product state distributions for atom–diatom collisions J Arnold, D Koner, S Käser, N Singh, RJ Bemish, M Meuwly The Journal of Physical Chemistry A 124 (35), 7177-7190, 2020 | 17 | 2020 |
| Mapping out phase diagrams with generative classifiers J Arnold, F Schäfer, A Edelman, C Bruder Physical Review Letters 132 (20), 207301, 2024 | 11 | 2024 |
| Combining machine learning and spectroscopy to model reactive atom+ diatom collisions JC San Vicente Veliz, J Arnold, RJ Bemish, M Meuwly The Journal of Physical Chemistry A 126 (43), 7971-7980, 2022 | 6 | 2022 |
| Machine learning phase transitions: Connections to the Fisher information J Arnold, N Lörch, F Holtorf, F Schäfer arXiv preprint arXiv:2311.10710, 2023 | 5 | 2023 |
| Performance Bounds for Quantum Feedback Control F Holtorf, F Schäfer, J Arnold, CV Rackauckas, A Edelman IEEE Transactions on Automatic Control, 2024 | 3* | 2024 |
| Fast detection of phase transitions with multi-task learning-by-confusion J Arnold, F Schäfer, N Lörch arXiv preprint arXiv:2311.09128, 2023 | 3 | 2023 |
| Phase Transitions in the Output Distribution of Large Language Models J Arnold, F Holtorf, F Schäfer, N Lörch arXiv preprint arXiv:2405.17088, 2024 | 1 | 2024 |
| Machine learning the Ising transition: A comparison between discriminative and generative approaches D Zhang, F Schäfer, J Arnold arXiv preprint arXiv:2411.19370, 2024 | | 2024 |
| Sum-of-Squares Bounds for Quantum Optimal Control F Holtorf, F Schäfer, J Arnold, C Rackauckas, A Edelman 2023 IEEE International Conference on Quantum Computing and Engineering (QCE …, 2023 | | 2023 |
| How deep neural networks learn thermal phase transitions J Arnold, F Schäfer APS March Meeting Abstracts 2023, Q53. 002, 2023 | | 2023 |
| Revealing phase diagrams of quantum systems with optimal predictors J Arnold, F Schäfer APS March Meeting Abstracts 2023, S62. 002, 2023 | | 2023 |
| Entropy production in ticking clocks J Arnold https://arnoldjulian.github.io/project/ticking_clocks/thesis.pdf, 2021 | | 2021 |
| Quantum simulators, phase transitions, resonant tunneling, and variances: A many-body perspective AUJ Lode, OE Alon, J Arnold, A Bhowmik, M Büttner, LS Cederbaum, ... International Conference on High Performance Computing in Science and …, 2021 | | 2021 |
| Interpretable and unsupervised phase classification based on averaged input features J Arnold, F Schäfer, M Zonda, A Lode APS March Meeting Abstracts 2021, L21. 002, 2021 | | 2021 |
Frank SchäferMassachusetts Institute of TechnologyBestätigte E-Mail-Adresse bei mit.edu
