Julian Arnold

Arnold, Julian, Schäfer, Frank, Edelman, Alan, & Bruder, Christoph. (2024). Mapping Out Phase Diagrams with Generative Classifiers. Physical Review Letters, 132. https://doi.org/10.1103/PhysRevLett.132.207301

Holtorf, Flemming, Schafer, Frank, Arnold, Julian, Rackauckas, Christopher V., & Edelman, Alan. (2024). Performance Bounds for Quantum Feedback Control. IEEE Transactions on Automatic Control, 69, 8057–8063. https://doi.org/10.1109/TAC.2024.3416008

Holtorf, Flemming, Schafer, Frank, Arnold, Julian, Rackauckas, Christopher, & Edelman, Alan. (2023). Sum-of-Squares Bounds for Quantum Optimal Control. 2, 365–366. https://doi.org/10.1109/QCE57702.2023.10284

Arnold, Julian, & Schäfer, Frank. (2022). Replacing Neural Networks by Optimal Analytical Predictors for the Detection of Phase Transitions. Physical Review X, 12(3), 31044. https://doi.org/10.1103/physrevx.12.031044

Veliz, Juan Carlos San Vicente, Arnold, Julian, Bemish, Raymond J., & Meuwly, Markus. (2022). Combining Machine Learning and Spectroscopy to Model Reactive Atom + Diatom Collisions. Journal of Physical Chemistry A, 126(43), 7971–7980. https://doi.org/10.1021/acs.jpca.2c06267

Arnold, J., Schäfer, F., Zonda, M., & Lode, A. U. J. (2021). Interpretable and unsupervised phase classification. Physical Review Research, 3(3), 33052. https://doi.org/10.1103/physrevresearch.3.033052

Arnold, Julian, San Vicente Veliz, Juan Carlos, Koner, Debasish, Singh, Narendra, Bemish, Raymond J., & Meuwly, Markus. (2021). Machine Learning Product State Distributions from Initial Reactant States for a Reactive Atom-Diatom Collision System. Journal of Chemical Physics, 156(3), 34301. https://doi.org/10.1063/5.0078008

Arnold, Julian, Koner, Debasish, Kaeser, Silvan, Singh, Narendra, Bemish, Raymond J., & Meuwly, Markus. (2020). Machine Learning for Observables: Reactant to Product State Distributions for Atom-Diatom Collisions. Journal of Physical Chemistry A, 124(35), 7177–7190. https://doi.org/10.1021/acs.jpca.0c05173

Lode, A.U.J., Alon, O.E., Arnold, J., Bhowmik, A., Büttner, M., Cederbaum, L.S., Chatterjee, B., Chitra, R., Dutta, S., Georges, C., Hemmerich, A., Keßler, H., Klinder, J., Lévêque, C., Lin, R., Molignini, P., Schäfer, F., Schmiedmayer, J., & Žonda, M. (2023). Quantum simulators, phase transitions, resonant tunneling, and variances: A many-body perspective (pp. 35–59). https://doi.org/10.1007/978-3-031-17937-2_3

Arnold, Julian, Holtorf,Flemming, Schäfer,Frank, & Lörch,Niels. (2024). Phase Transitions in the Output Distribution of Large Language Models. In Arxiv. Cornell University. https://doi.org/10.48550/arXiv.2405.17088

Arnold,Julian, Lörch,Niels, Holtorf,Flemming, & Schäfer,Frank. (2023). Machine learning phase transitions: Connections to the Fisher information. In Arxiv. Cornell University. https://doi.org/10.48550/arXiv.2311.10710

Arnold, Julian, Schäfer, Frank, & Lörch, Niels. (2023). Fast Detection of Phase Transitions with Multi-Task Learning-by-Confusion. In Arxiv. Cornell University. https://doi.org/10.48550/arXiv.2311.09128

Holtorf, Flemming, Schäfer, Frank, Arnold, Julian, Rackauckas, Christopher, & Edelman, Alan. (2023). Performance Bounds for Quantum Control. In Arxiv. Cornell University. https://doi.org/10.48550/arXiv.2304.03366

Dawid, Anna, Arnold, Julian, Requena, Borja, Gresch, Alexander, Płodzień, Marcin, Donatella, Kaelan, Nicoli, Kim A., Stornati, Paolo, Koch, Rouven, Büttner, Miriam, Okuła, Robert, Muñoz-Gil, Gorka, Vargas-Hernández, Rodrigo A., Cervera-Lierta, Alba, Carrasquilla, Juan, Dunjko, Vedran, Gabrié, Marylou, Huembeli, Patrick, van Nieuwenburg, Evert, et al. (2022). Modern applications of machine learning in quantum sciences. In Arxiv. Cornell University. https://doi.org/10.48550/arXiv.2204.04198

Arnold, Julian, Schäfer, Frank, Zonda, Martin, & Lode, Axel. (2021, January 1). Interpretable and unsupervised phase classification based on averaged input features. APS March Meeting 2021.