| Unsupervised identification of topological phase transitions using predictive models E Greplova, A Valenti, G Boschung, F Schäfer, N Lörch, S Huber New Journal of Physics, 2020 | 93 | 2020 |
| A differentiable programming method for quantum control F Schäfer, M Kloc, C Bruder, N Lörch Machine Learning: Science and Technology 1 (3), 035009, 2020 | 61 | 2020 |
| Vector field divergence of predictive model output as indication of phase transitions F Schäfer, N Lörch Physical Review E 99 (6), 062107, 2019 | 52 | 2019 |
| Automatic differentiation of programs with discrete randomness G Arya, M Schauer, F Schäfer, C Rackauckas Advances in Neural Information Processing Systems 35, 10435-10447, 2022 | 48 | 2022 |
| Interpretable and unsupervised phase classification J Arnold, F Schäfer, M Žonda, AUJ Lode Physical Review Research 3 (3), 033052, 2021 | 45 | 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 | 30 | 2022 |
| Control of stochastic quantum dynamics by differentiable programming F Schäfer, P Sekatski, M Koppenhöfer, C Bruder, M Kloc Machine Learning: Science and Technology 2 (3), 035004, 2021 | 26 | 2021 |
| Differentiable Programming for Differential Equations: A Review F Sapienza, J Bolibar, F Schäfer, B Groenke, A Pal, V Boussange, ... arXiv preprint arXiv:2406.09699, 2024 | 13 | 2024 |
| NonlinearSolve. jl: High-Performance and Robust Solvers for Systems of Nonlinear Equations in Julia A Pal, F Holtorf, A Larsson, T Loman, F Schäfer, Q Qu, A Edelman, ... arXiv preprint arXiv:2403.16341, 2024 | 13 | 2024 |
| AbstractDifferentiation. jl: Backend-Agnostic Differentiable Programming in Julia F Schäfer, M Tarek, L White, C Rackauckas arXiv preprint arXiv:2109.12449, 2021 | 13 | 2021 |
| Mapping out phase diagrams with generative classifiers J Arnold, F Schäfer, A Edelman, C Bruder Physical Review Letters 132 (20), 207301, 2024 | 12 | 2024 |
| Continuous monitoring and feedback control of qubit dynamics using differentiable programming F Schäfer, P Sekatski, M Koppenhoefer, N Loerch, C Bruder, M Kloc Bulletin of the American Physical Society, 2021 | 12* | 2021 |
| Differentiating Metropolis-Hastings to optimize intractable densities G Arya, R Seyer, F Schäfer, K Chandra, AK Lew, M Huot, VK Mansinghka, ... arXiv preprint arXiv:2306.07961, 2023 | 8 | 2023 |
| Spectral Structure and Many-Body Dynamics of Ultracold Bosons in a Double-Well F Schäfer, MA Bastarrachea-Magnani, AUJ Lode, L de Forges de Parny, ... Entropy 22 (4), 382, 2020 | 8 | 2020 |
| 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 |
| Data-Driven Reconstruction of Spectral Conductivity and Chemical Potential Using Thermoelectric Transport Properties T Hirosawa, F Schäfer, H Maebashi, H Matsuura, M Ogata Journal of the Physical Society of Japan 91 (11), 114603, 2022 | 5 | 2022 |
| Fast Detection of Phase Transitions with Multi-Task Learning-by-Confusion J Arnold, F Schäfer, N Lörch arXiv preprint arXiv:2311.09128, 2023 | 4 | 2023 |
| Performance Bounds for Quantum Control F Holtorf, F Schäfer, J Arnold, C Rackauckas, A Edelman arXiv preprint arXiv:2304.03366, 2023 | 3 | 2023 |
| Automated reconstruction of bound states in bilayer graphene quantum dots J Bucko, F Schäfer, F Herman, R Garreis, C Tong, A Kurzmann, T Ihn, ... Physical Review Applied 19 (2), 024015, 2023 | 3 | 2023 |
| Cooperative scattering of scalar waves by optimized configurations of point scatterers F Schäfer, F Eckert, T Wellens Journal of Physics B: Atomic, Molecular and Optical Physics 50 (23), 235502, 2017 | 3 | 2017 |
Julian ArnoldPhD Candidate, University of Baselایمیل تأیید شده در unibas.ch