Holland, K., Ipp, A., Müller, D. I., & Wenger, U. (2024). Machine learning a fixed point action for SU(3) gauge theory with a gauge equivariant convolutional neural network. Physical Review D, 110(7), Article 074502. https://doi.org/10.1103/PhysRevD.110.074502 ( reposiTUm)
Ipp, A., Müller, D. I., Schlichting, S., & Singh, P. (2021). Spacetime structure of (3+1)D color fields in high energy nuclear collisions. Physical Review D, 104(114040). https://doi.org/10.1103/physrevd.104.114040 ( reposiTUm)
Bulusu, S., Favoni, M., Ipp, A., Müller, D. I., & Schuh, D. (2021). Generalization capabilities of translationally equivariant neural networks. Physical Review D, 104(074504). https://doi.org/10.1103/physrevd.104.074504 ( reposiTUm)
Ipp, A., Müller, D. I., & Schuh, D. (2020). Anisotropic momentum broadening in the 2+1D glasma: Analytic weak field approximation and lattice simulations. Physical Review D, 102(7), Article 074001. https://doi.org/10.1103/physrevd.102.074001 ( reposiTUm)
Holland, K., Ipp, A., Müller, D. I., & Wenger, U. (2024). Application of gauge equivariant convolutional neural networks to learning a fixed point action for SU(3) gauge theory. In ICLR 2024 Workshop on AI4DifferentialEquations In Science. ICLR 2024 Workshop on AI4DifferentialEquations in Science, Vienna, Austria. http://hdl.handle.net/20.500.12708/210283 ( reposiTUm)
Holland, K., Ipp, A., Müller, D., & Wenger, U. (2023). Fixed point actions from convolutional neural networks. In Proceedings of Science (PoS). 40th International Symposium on Lattice Field Theory (Lattice 2023), Fermilab, Batavia, Illinois, United States of America (the). https://doi.org/10.48550/ARXIV.2311.17816 ( reposiTUm)
Ipp, A., Mueller, D., Favoni, M., & Schuh, D. (2022). Preserving gauge invariance in neural networks. In EPJ Web of Conferences (p. 09004). EPJ Web of Conferences. https://doi.org/10.1051/epjconf/202225809004 ( reposiTUm)
Präsentationen
Ipp, A., Holland, K., Müller, D. I., & Wenger, U. (2024, July 19). Learning a fixed point action for SU(3) gauge theory with lattice gauge equivariant convolutional neural networks [Poster Presentation]. iTHEMS NOW & NEXT 2024, RIKEN Wako, Tokio, Japan. http://hdl.handle.net/20.500.12708/208967 ( reposiTUm)
Wenger, U., Holland, K., & Ipp, A. (2024, July 30). HMC and gradient flow with machine-learned classically perfect fixed point actions [Poster Presentation]. Lattice 2024, Liverpool, United Kingdom of Great Britain and Northern Ireland (the). http://hdl.handle.net/20.500.12708/208987 ( reposiTUm)
Ipp, A., Holland, K., Müller, D. I., & Wenger, U. (2024, July 16). Machine learning fixed point actions with lattice gauge equivariant convolutional neural networks [Presentation]. HAL QCD Collaboration Meeting 2024, RIKEN Wako, Tokio, Japan. http://hdl.handle.net/20.500.12708/208965 ( reposiTUm)
Ipp, A., Holland, K., Müller, D. I., & Wenger, U. (2024, July 23). Symmetries and Generalization for Machine Learning on a Lattice [Presentation]. RIKEN iTHEMS DEEP-IN Seminar 2024, RIKEN Wako, Tokio, Japan. http://hdl.handle.net/20.500.12708/208966 ( reposiTUm)
Ipp, A., Holland, K., Müller, D. I., & Wenger, U. (2024, May 2). Improved Fixed Point Actions from Gauge Equivariant Neural Networks [Conference Presentation]. EuCAIFCon 2024 - European AI for Fundamental Physics Conference, Amsterdam, Netherlands (the). http://hdl.handle.net/20.500.12708/208964 ( reposiTUm)
Ipp, A., Holland, K., Müller, D. I., & Wenger, U. (2024, April 19). Fixed point actions from lattice gauge equivariant convolutional neural networks [Conference Presentation]. Bridging scales: At the crossroads among renormalisation group, multi-scale modelling, and deep learning 2024, ECT* Trento, Italy. http://hdl.handle.net/20.500.12708/208963 ( reposiTUm)
Müller, D. (2023, May 16). Applications of group and gauge equivariant neural networks to problems in lattice field theory [Poster Presentation]. HPC Workshop for Nuclear Explosion Monitoring, Vienna, Austria. ( reposiTUm)
Ipp, A. (2023, August 18). Symmetries and ML [Presentation]. 51st SLAC Summer Institute (SSI 2023), SLAC, California, United States of America (the). ( reposiTUm)
Ipp, A., Müller, D., Schuh, D., & Favoni, M. (2023, June 27). Visualizing the inner workings of L-CNNs [Conference Presentation]. Machine learning for lattice field theory and beyond 2023, Trento, Italy. ( reposiTUm)
Wenger, U., Ipp, A., Müller, D., & Holland, K. (2023, June 28). Machine learning a fixed point action [Conference Presentation]. Machine learning for lattice field theory and beyond 2023, Trento, Italy. http://hdl.handle.net/20.500.12708/193366 ( reposiTUm)
Schuh, D. (2022, April 6). Transverse momentum broadening in the glasma: real-time lattice simulations and the weak-field limit [Poster Presentation]. Quark Matter 2022, Kraków, Poland. ( reposiTUm)
Bulusu, S., Favoni, M., Ipp, A., Mueller, D., & Schuh, D. (2021). Translational equivariance in neural networks. Machine Learning Techniques in Lattice QCD, Mainz Institute for Theoretical Physics, Johannes Gutenberg University, Germany. http://hdl.handle.net/20.500.12708/135521 ( reposiTUm)
Ipp, A., Favoni, M., Mueller, D., & Schuh, D. (2021). Preserving lattice gauge equivariance in neural networks. Heidelberg Stavanger Lattice & Machine Learning Seminar, Heidelberg and Stavanger (online), Germany. http://hdl.handle.net/20.500.12708/135609 ( reposiTUm)
Ipp, A., Favoni, M., Mueller, D., & Schuh, D. (2021). Generic Lattice gauge equivariant CNN. Machine Learning Techniques in Lattice QCD, Mainz Institute for Theoretical Physics, Johannes Gutenberg University, Germany. http://hdl.handle.net/20.500.12708/135610 ( reposiTUm)
Sato, N., Shanahan, P., Ipp, A., & Cranmer, K. (2021). Machine Learning Round Table. A Virtual Tribute to Quark Confinement and the Hadron Spectrum 2021, Stavanger (Norwegen), Norway. http://hdl.handle.net/20.500.12708/135612 ( reposiTUm)
Bulusu, S., Favoni, M., Ipp, A., Mueller, D., & Schuh, D. (2021). Generalization capabilities of neural networks in lattice applications. 38th International Symposium on Lattice Field Theory, Cambridge, Massachusetts, United States of America (the). http://hdl.handle.net/20.500.12708/135628 ( reposiTUm)
Ipp, A., Mueller, D., & Schuh, D. (2021). Transverse momentum broadening in real-time lattice simulations of the glasma. 38th International Symposium on Lattice Field Theory, Cambridge, Massachusetts, United States of America (the). http://hdl.handle.net/20.500.12708/135576 ( reposiTUm)
Favoni, M., Ipp, A., Mueller, D., & Schuh, D. (2021). Implementing gauge symmetry in machine learning models. Strong and Electro-Weak Matter 2021, Paris, Frankreich, France. http://hdl.handle.net/20.500.12708/135544 ( reposiTUm)
Favoni, M., Ipp, A., Mueller, D., & Schuh, D. (2021). Lattice Gauge Symmetry in Neural Networks. 38th International Symposium on Lattice Field Theory, Cambridge, Massachusetts, United States of America (the). http://hdl.handle.net/20.500.12708/135545 ( reposiTUm)
Ipp, A., Mueller, D., & Schuh, D. (2021). Jet momentum broadening in real-time lattice simulations of the glasma. The VI-th International Conference on the Initial Stages of High-Energy Nuclear Collisions, Weizmann Institute of Science, Rehovot, Israel. http://hdl.handle.net/20.500.12708/135345 ( reposiTUm)
Ipp, A., Mueller, D., & Schuh, D. (2021). Transverse momentum broadening of jets in the weak field limit of the glasma. The VI-th International Conference on the Initial Stages of High-Energy Nuclear Collisions, Weizmann Institute of Science, Rehovot, Israel. http://hdl.handle.net/20.500.12708/135346 ( reposiTUm)
Ipp, A., Mueller, D., & Schuh, D. (2020). Momentum broadening in the Glasma. Instituto Gallego de Física de Altas Energías (IGFAE) Seminar, Universidad de Santiago de Compostela, 15782 Santiago de Compostela, Spain. http://hdl.handle.net/20.500.12708/135344 ( reposiTUm)
Schuh, D., Mueller, D., & Ipp, A. (2019). Momentum Broadening in a Highly Diluted Glasma. 15th Vienna Central European Seminar on Particle Physics and Quantum Field Theory 2019, Wien, Austria. http://hdl.handle.net/20.500.12708/135032 ( reposiTUm)
