Seminar
There are 10 time slots until the end the term. So, please prepare a 1 hour 15 minutes lecture on a one paper or a set of related papers. The selected paper(s) should align with the general topic areas of the course. Students are encouraged to choose a topic that also connects to their PhD research interests. When in doubt please reach out.
List of papers
Here is a list of papers that may be of interest when presenting your seminar. Please check the papers cited in these papers
Digital Twins
A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies by Thelen et. al.
A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives by Thelen et. al.
Digital Twins for Materials by Kalidindi et. al.
A Probabilistic Graphical Model Foundation for Enabling Predictive Digital Twins at Scale by Kapteyn et. al.
National Academies of Sciences, Engineering, and Medicine. 2023. Foundational Research Gaps and Future Directions for Digital Twins. Washington, DC: The National Academies Press.
Torzoni, M., Tezzele, M., Mariani, S., Manzoni, A. and Willcox K. A digital twin framework for civil engineering structures. To appear, Computer Methods in Applied Mechanics and Engineering, 2023.
Kapteyn, M. and Willcox, K., Design of digital twin sensing strategies via predictive modeling and interpretable machine learning. Journal of Mechanical Design, June 2022. https://doi.org/10.1115/1.4054907
Niederer, S., Sacks, M., Girolami, M. and Willcox, K., Scaling digital twins from the artisanal to the industrial. Nature Computational Science, Vol. 1, No. 5, May 2021, pp. 313-320.
San, O., Pawar, S., & Rasheed, A. 2023. Decentralized digital twins of complex dynamical systems. Scientific Reports 13, 20087.https://doi.org/10.1038/s41586-025-08744-2
Bruer, Grant., Gahlot, Abhinav Prakash., Chow, Edmond., Herrmann, Felix. 2025. Seismic Monitoring of CO₂ Plume Dynamics Using Ensemble Kalman Filtering. IEEE Transactions on Geoscience and Remote Sensing, 63, 1-22.
Gahlot, A. P., Orozco, R., Yin, Z., Bruer, G., & Herrmann, F. J. (2025). An uncertainty-aware digital shadow for underground multimodal CO₂ storage monitoring. Geophysical Journal International, 242(1), ggaf176.
Kalidindi, S. R., Buzzy, M., Boyce, B. L., & Dingreville, R. (2022). Digital twins for materials. Frontiers in Materials, 9, 818535. https://doi.org/10.3389/fmats.2022.818535
Sharma, A., Kosasih, E., Zhang, J., Brintrup, A., & Calinescu, A. (2022). Digital twins: State of the art theory and practice, challenges, and open research questions. Journal of Industrial Information Integration, 30, 100383.
Antoun, M., et al. (2025). Interactive digital twins enabling responsible extended reality applications. Scientific Reports, 15, 34539.
Zhou, R., et al. (2026). Digital twin AI: Opportunities and challenges from large language models to world models. arXiv preprint arXiv:2601.01321.
Robles, J., Martín, C., & Díaz, M. (2023). OpenTwins: An open-source framework for the development of next-gen compositional digital twins. Computers in Industry, 152, 104007.
Simulation Based Inference
Deistler, M., Boelts, J., Steinbach, P., Moss, G., Moreau, T., Gloeckler, M., Rodrigues, P. L. C., Linhart, J., Lappalainen, J. K., Kurt Miller, B., Gonçalves, P. J., Lueckmann, J.-M., Schröder, C., & Macke, J. H. (2025). Simulation-Based Inference: A Practical Guide. arXiv preprint arXiv:2508.12939.
Brehmer, J., & Cranmer, K. (2020). Simulation-based inference methods for particle physics. arXiv preprint arXiv:2010.06439.
Arruda, J., Bracher, N., Köthe, U., Hasenauer, J., & Radev, S. T. (2025). Diffusion models in simulation-based inference: A tutorial review. arXiv preprint arXiv:2512.20685.
Bracher, N., Kühmichel, L., Ivanova, D. R., Intes, X., Bürkner, P.-C., & Radev, S. T. (2025). JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference. arXiv preprint arXiv:2512.22999.
Schmitt, M., Pratz, V., Köthe, U., Bürkner, P.-C., & Radev, S. T. (2024). Consistency models for scalable and fast simulation-based inference. Advances in Neural Information Processing Systems, 37 (NeurIPS 2024).
Säilynoja, T., Schmitt, M., Bürkner, P.-C., & Vehtari, A. (2026). Posterior SBC: Simulation-based calibration checking conditional on data. Statistics and Computing, 36, 78.
Reiser, P., Aguilar, J. E., Guthke, A., & Bürkner, P.-C. (2025). Uncertainty quantification and propagation in surrogate-based Bayesian inference. Statistics and Computing, 35(3), 66.
Highly recommend checking this website for other relevant publications (https://simulation-based-inference.org/)
Generative Modeling
Lipman, Y., et al. (2024). Flow matching guide and code. arXiv preprint arXiv:2412.06264.
Gebreab, S., et al. (2024). Accelerating digital twin development with generative AI: A framework for 3D modeling and data integration. IEEE Access, 12.
Naiff, D., Pires, G., Schaeffer, B. P., Stojkovic, D., Rapstine, T., & Ramos, F. (2025). Controlled latent diffusion models for 3D porous media reconstruction. arXiv preprint arXiv:2503.24083v1.
Kadkhodaie, Z., Mallat, S., & Simoncelli, E. P. (2024). Feature-guided score diffusion for sampling conditional densities. arXiv preprint arXiv:2410.11646.
Holderrieth, P., & Erives, E. (2025). An introduction to flow matching and diffusion models. arXiv preprint arXiv:2506.02070.
World models
Hafner, D., Pasukonis, J., Ba, J., & Lillicrap, T. 2025. Mastering diverse control tasks through world models. Nature 640, 647–653.
Zhang, P.-F., Cheng, Y., Sun, X., Wang, S., Li, F., Zhu, L., & Shen, H. T. (2025). A step toward world models: A survey on robotic manipulation. arXiv preprint arXiv:2511.02097.
Yang, S. (2026). World models as an intermediary between agents and the real world. arXiv preprint arXiv:2602.00785.