D. Wagner, A. Nair, B. J. Franks, J. Arweiler, A. Muraleedharan, I. Jungjohann, F. Hartung,
M. C. Ahuja, A. Balinskyy, N. H. Syed, M. Nagda, P. Liznerski, S. Reithermann, M. Rudolph,
S. Vollmer, R. Schulz, T. Katz, S. Mandt, M. Bortz, H. Leitte, D. Neider, J. Burger, F.
Jirasek, H. Hasse, S. Fellenz, and M. Kloft. “Formally exploring time-series anomaly detection
evaluation metrics”. In: AISTATS. Vol. abs/2510.17562. 2026. doi: 10.48550/arXiv.2510.
17562. url: https://arxiv.org/abs/2510.17562.
F. A. Zaid, D. Neider, and M. Yalçiner. “VeriFlow: Modeling distributions for neural network
verification”. In: AAAI. AAAI Press, 2026, to appear. doi: 10.48550/ARXIV.2406.14265.
arXiv: 2406.14265. url: https://doi.org/10.48550/arXiv.2406.14265.
M. Nagda, J. Abijuru, P. Ostheimer, M. Kloft, and S. Fellenz. PIANO: Physics informed
autoregressive network. 2026. doi: 10.48550/arXiv.2508.16235. arXiv: 2508.16235 (cs.LG). url: https://arxiv.org/abs/2508.16235. Submitted.
M. Nagda, P. Ostheimer, T. Specht, F. Rhein, F. Jirasek, S. Mandt, M. Kloft, and S. Fellenz.
“SetPINNs: Set-based physics-informed neural networks”. In: AISTATS. Spotlight (2.5%
of papers). 2026. doi: 10.48550/arXiv.2409.20206. url: https://arxiv.org/abs/2409.20206.
P. Liznerski, S. Varshneya, E. Calikus, P. Wang, A. Bartscher, S. J. Vollmer, S. Fellenz, and
M. Kloft. “Reimagining anomalies: What if anomalies were normal?” In: AAAI. 2026. doi:
10.48550/arXiv.2402.14469. url: https://arxiv.org/abs/2402.14469.
J. Werner, I. Jungjohann, J. Arweiler, J. Schmid, H. Hasse, F. Jirasek, and M. Bortz. “Simulation data to augment experimental data for anomaly detection in batch distillation”. Submitted version will be available for inspection in April. 2026.
W. Li, W. Mustafa, M. Monteiro, P. Wang, M. Kloft, and S. Fellenz. “TORA: Train once,
realign anytime for offline multi-objective reinforcement learning”. In: AAAI. (to appear). 2026.
A. Muraleedharan, A. Ferre, J. Arweiler, I. Jungjohann, F. Jirasek, H. Hasse, and J. Burger.
“Experimental time series data with and without anomalies from a continuous distillation
mini-plant for development of machine learning anomaly detection methods”. In: engrXiv:5631
(2025). doi: 10.31224/5631. url: https://engrxiv.org/preprint/view/5631.
C. Qiu, M. Kloft, S. Mandt, and M. Rudolph. “Self-supervised anomaly detection with neural transformations”. In: IEEE Transactions on Pattern Analysis and Machine Intelligence
(2025). doi: 10.1109/tpami.2024.3519543. url: https://pubmed.ncbi.nlm.nih.gov/40030646/.
D. Wagner, F. Hartung, J. Arweiler, A. Muraleedharan, I. Jungjohann, A. Nair, S. Reithermann, R. Schulz, M. Bortz, D. Neider, H. Leitte, J. Pfeffinger, S. Mandt, S. Fellenz, T. Katz,
F. Jirasek, J. Burger, H. Hasse, and M. Kloft. “NoBOOM: Chemical process datasets for industrial anomaly detection”. In: NeurIPS. 2025. url: https://neurips.cc/virtual/2025/loc/san-diego/poster/121442.
J. Abijuru, M. Nagda, P. Ostheimer, J. C. Aurich, S. J. Vollmer, M. Kloft, and S. Fellenz.
“Physics-informed residual flows”. In: DiffSys Workshop. 2025. url: https://openreview. net/forum?id=OiIoXAUGHZ.
J. Arweiler, I. Jungjohann, A. Muraleedharan, H. Leitte, J. Burger, K. Münnemann, F. Jirasek,
and H. Hasse. “Batch distillation data for developing machine learning anomaly detection
methods”. In: arXiv:2510.18075 (2025). doi: 10.48550/arXiv.2510.18075. url: https://arxiv.org/abs/2510.18075.
J. Arweiler, I. Jungjohann, A. Muraleedharan, H. Leitte, J. Burger, K. Münnemann, F. Jirasek,
and H. Hasse. “Batch distillation data for developing machine learning anomaly detection
methods”. In: Zenodo (2025). doi: 10.5281/zenodo.17395543. url: https://doi.org/10.5281/zenodo.17395543.
J. Tauberschmidt, S. Fellenz, S. J. Vollmer, and A. B. Duncan. “Physics-constrained finetuning of flow-matching models for generation and inverse problems”. In: ICLR. OpenReview
submission. 2025. url: https://openreview.net/forum?id=khBHJz2wcV. accepted.
J. Werner, J. Schmid, L. T. Biegler, and M. Bortz. “An equation-based batch distillation
simulation to evaluate the effect of multiplicities in thermodynamic activity coefficients”. In:
Fluid Phase Equilibria 598 (2025), p. 114465. doi: 10.1016/j.fluid.2025.114465. url: https://www.sciencedirect.com/science/article/pii/S0378381225001359.
L. Vollmer, R. Loubet, F. Jirasek, S. Fellenz, H. Hasse, and H. Leitte. “Enabling transparent
problem solving in thermodynamics with ontologies and knowledge graphs”. In: ESWC 2025 Workshops and Tutorials Joint Proceedings (ESWC-JP 2025). 2025. url: https://ceur-ws.org/Vol-3977/XAIKG-3.pdf.
L. Westhofen, J. C. Jung, and D. Neider. “Temporal conjunctive query answering via rewriting”. In: AAAI. Ed. by T. Walsh, J. Shah, and Z. Kolter. AAAI Press, 2025, pp. 15221–15229.
doi: 10.1609/AAAI.V39I14.33670. url: https://doi.org/10.1609/aaai.v39i14.33670.
M. Hoffmann, H. Hasse, and F. Jirasek. “GRAPPA—A hybrid graph neural network for predicting pure component vapor pressures”. In: Chemical Engineering Journal Advances (2025),
p. 100750. doi: 10.1016/j.ceja.2025.100750. url: https://arxiv.org/abs/2501.08729.
M. Hoffmann, T. Specht, Q. Göttl, J. Burger, S. Mandt, H. Hasse, and F. Jirasek. “A machinelearned expression for the excess Gibbs energy”. In: arXiv:2509.06484 (2025). doi: 10.48550/
arXiv.2509.06484. url: https://arxiv.org/abs/2509.06484.
M. Nagda, J. Abijuru, P. Ostheimer, J. C. Aurich, S. Mandt, M. Kloft, and S. Fellenz. “Autoregressive PINNs for time-dependent PDEs”. In: DiffSys Workshop. 2025. url: https://openreview.net/forum?id=1iOZVPmudk.
M. Nagda, P. Ostheimer, and S. Fellenz. “Tethering broken themes: Aligning neural topic models with labels and authors”. In: Findings of the Association for Computational Linguistics:
NAACL 2025, Albuquerque, New Mexico, USA, April 29 – May 4, 2025. Ed. by L. Chiruzzo,
A. Ritter, and L. Wang. Association for Computational Linguistics, 2025, pp. 740–760. doi:
10.18653/V1/2025.FINDINGS- NAACL.44. url: https://doi.org/10.18653/v1/2025.findings-naacl.44.
M. Nagda, P. Ostheimer, J. Arweiler, I. Jungjoha, J. Werner, D. Wagner, A. Muraleedharan,
P. Jafari, J. Schmid, F. Jirasek, J. Burger, M. Bortz, H. Hasse, S. Mandt, M. Kloft, and
S. Fellenz. “Style transfer for high-fidelity time series augmentation”. In: ECML Workshop
SynDAiTE. 2025.
M. Nagda, P. Ostheimer, J. Arweiler, I. Jungjohann, J. Werner, D. Wagner, A. Muraleedharan,
P. Jafari, J. Schmid, F. Jirasek, J. Burger, M. Bortz, H. Hasse, S. Mandt, M. Kloft, and S.
Fellenz. DiffStyleTS: Diffusion model for style transfer in time series. 2025. doi: 10.48550/
arXiv.2510.11335. arXiv: 2510.11335 (cs.LG). url: https://arxiv.org/abs/2510.11335.
N. Ghanooni, W. Mustafa, D. Wagner, S. Fellenz, A. W. Lin, and M. Kloft. “Mitigating
spurious features in contrastive learning with spectral regularization”. In: NeurIPS. 2025.
url: https://neurips.cc/virtual/2025/loc/san-diego/poster/117854.
N. Hayer, T. Wendel, S. Mandt, H. Hasse, and F. Jirasek. “Advancing thermodynamic groupcontribution methods by machine learning: UNIFAC 2.0”. In: Chemical Engineering Journal 504 (2025), p. 158667. doi: 10 . 1016 / j . cej . 2024 . 158667. url: https://www.sciencedirect.com/science/article/pii/S1385894724101581.
P. Ostheimer, M. Nagda, A. Balinskyy, J. Radig, C. Herrmann, S. Mandt, M. Kloft, and S. Fellenz. “Sparse data diffusion for scientific simulations in biology and physics”. In: EurIPS 2025
Workshop on SIMBIOCHEM. 2025. url: https://openreview.net/forum?id=O3OVn3NSRE.
R. Loubet, P. Zittlau, M. Hoffmann, L. Vollmer, S. Fellenz, H. Leitte, F. Jirasek, J. Lenhard,
and H. Hasse. Superstudent intelligence in thermodynamics. 2025. doi: 10 . 48550 / arXiv .
2506.09822. arXiv: 2506.09822 (cs.CE). url: https://arxiv.org/abs/2506.09822.
S. Lutz, D. Kaminskyi, F. Wittbold, S. Dierl, F. Howar, B. König, E. Müller, and D. Neider.
“Unsupervised automata learning via discrete optimization”. In: JELIA. Ed. by G. Casini, B. Dundua, and T. Kutsia. Vol. 16093. Lecture Notes in Computer Science. Springer, 2025,
pp. 135–153. doi: 10.1007/978-3-032-04587-4\_9. url: https://doi.org/10.1007/9783-032-04587-4%5C_9.
V. Belle, M. Benedikt, D. Drachsler-Cohen, D. Neider, and T. Yuviler. “Logic and neural
networks (Dagstuhl seminar 25061)”. In: Dagstuhl Reports 15.2 (2025), pp. 1–20. doi: 10.
4230/DAGREP.15.2.1. url: https://doi.org/10.4230/DagRep.15.2.1.
A. Ferre, J. Voggenreiter, C. F. Breitkreuz, D. Worch, U. Lubenau, H. Hasse, and J. Burger. “Experimental demonstration of the production of poly (oxymethylene) dimethyl ethers from methanolic formaldehyde solutions in a closed-loop mini-plant”. In: Chemical Engineering Research and Design 211.28 (2024), pp. 331–342. doi: 10.1016/j.cherd.2024.09.041. url: https://doi.org/10.1016/j.cherd.2024.09.041.
B. Franks, C. Morris, A. Velingker, and F. Geerts.
Weisfeiler-Leman at the margin: When more expressivity matters.
ICML, 2024.
C. James, M. Nagda, N. Ghassemi, M. Kloft, and S. Fellenz. “Evaluating Dynamic Topic Models”. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL) (2024). url: https://aclanthology.org/2024.acl-long.11.pdf.
C. Karakkaparambil James, M. Nagda, N. Haji Ghassemi, M. Kloft, and S. Fellenz. “Evaluating Dynamic Topic Models”. In: Proceedings of the 62nd Annual Meeting of the Association
for Computational Linguistics (Volume 1: Long Papers). Bangkok, Thailand: Association for Computational Linguistics, Aug. 2024, pp. 160–176. doi: 10.18653/v1/2024.acl-long.11. url: https://aclanthology.org/2024.acl-long.11/.
D. Hintersdorf, L. Struppek, D. Neider, and K. Kersting. “Defending our privacy with backdoors”. In: ECAI. Ed. by U. Endriss, F. S. Melo, K. Bach, A. J. B. Diz, J. M. Alonso-Moral,
S. Barro, and F. Heintz. Vol. 392. Frontiers in Artificial Intelligence and Applications. IOS Press, 2024, pp. 1832–1839. doi: 10.3233/FAIA240695. url: https://doi.org/10.3233/FAIA240695.
D. Neider and R. Roy. “What is formal verification without specifications? A survey on mining LTL specifications”. In: Principles of Verification: Cycling the Probabilistic Landscape – Essays
Dedicated to Joost-Pieter Katoen on the Occasion of His 60th Birthday, Part III. Ed. by N. Jansen, S. Junges, B. L. Kaminski, C. Matheja, T. Noll, T. Quatmann, M. Stoelinga, and M. Volk. Vol. 15262. Lecture Notes in Computer Science. Springer, 2024, pp. 109–125. doi: 10.1007/978-3-031-75778-5\_6. url: https://doi.org/10.1007/978-3-031-757785%5C_6.
D. Reinhardt, D. Wagner, A. Muraleedharan, J. Arweiler, I. Jungjohann, F. Jirasek, J. Burger, H. Hasse, M. Kloft, and H. Leitte. “cPAX: Comparative visualization of known and novel anomalies for monitoring chemical plants”. In: ECML PKDD Workshop ML4CCE. 2024. url: https://ml4cce-ecml.com/papers/178.pdf.
F. Hartung et al. Deep Anomaly Detection on Tennessee Eastman Process Data. ML4CCE @ ECML PKDD, 2024.
J. Arweiler, A. Muraleedharan, F. Hartung, I. Jungjohann, D. Wagner, M. Kloft, J. Burger, H. Hasse, and F. Jirasek. “Anomaly detection on experimental chemical process data”. In: ECML PKDD Workshop ML4CCE. 2024. url: https://ml4cce-ecml.com/papers/184.pdf.
J. Schmid, P. Seufert, and M. Bortz. Adaptive discretization algorithms for locally optimal experimental design. arXiv:2406.01541, 2024. https://arxiv.org/abs/2406.01541.
J. Will, J. Arweiler, I. Jungjohann, J. Werner, M. Nagda, M. Bortz, H. Hasse, F. Jirasek, J. Schmid, M. Kloft, S. Mandt, and S. Fellenz. “Enhancing realism in batch distillation simulations: Data-efficient time series style transfer with transformers”. In: ECML PKDD Workshop ML4CCE. 2024. url: https://ml4cce-ecml.com/papers/199.pdf.
L. Manduchi, K. Pandey, R. Bamler, R. Cotterell, S. Däubener, S. Fellenz, A. Fischer, T. Gärtner, M. Kirchler, M. Kloft, Y. Li, C. Lippert, G. d. Melo, E. Nalisnick, B. Ommer, R. Ranganath, M. Rudolph, K. Ullrich, G. v. d. Broeck, J. Vogt, Y. Wang, F. Wenzel, F. Wood, S. Mandt, and V. Fortuin. “On the challenges and opportunities in generative AI”. In: TMLR abs/2403.00025 (2024). doi: 10.48550/arXiv.2403.00025. url: https://arxiv.org/abs/2403.00025.
L. Streib, J. Spaak, M. Kloft, and R. Schäfer. “The spatiotemporal profile and adaptation determine the joint effects and interactions of multiple stressors”. In: Environmental Sciences Europe 36 (2024), p. 118.
L. Streib, J. W. Spaak, M. Kloft, and R. B. Schaefer. “The spatiotemporal profile and adaptation determine the joint effects and interactions of multiple stressors”. In: Environmental
Sciences Europe 36.1 (2024), p. 118. doi: 10 . 1186 / s12302 – 024 – 00945 – 2. url: https://doi.org/10.1186/s12302-024-00945-2.
L. Vollmer, S. Fellenz, F. Jirasek, H. Hasse, and H. Leitte. “KnowTD – An actionable knowledge representation system for thermodynamics”. In: Journal of Chemical Information and
Modeling 64.15 (2024), pp. 5878–5887. doi: 10.1021/acs.jcim.4c00647. url: https://arxiv.org/abs/2407.17169.
