Dieses Bild zeigt Peter Domanski

Peter Domanski

M.Sc.

Wissenschaftlicher Mitarbeiter
IPVS
Scientific Computing

Kontakt

+49 711 685 88384

Universitätsstraße 38
70569 Stuttgart
Deutschland
Raum: 2.041

  1. Schwachhofer, D., Domanski, P., Becker, S., Wagner, S., Sauer, M., Pflüger, D., Polian, I.: Training Large Language Models for System-Level Test Program Generation Targeting Non-functional Properties. In: 2024 IEEE European Test Symposium (ETS). pp. 1–4 (2024). https://doi.org/10.1109/ETS61313.2024.10567741.
  2. Schwachhofer, D., Domanski, P., Becker, S., Wagner, S., Sauer, M., Pflüger, D., Polian, I.: Large Language Models to Generate System-Level Test Programs Targeting Non-functional Properties, https://arxiv.org/abs/2403.10086, (2024).
  3. Tan, X., Domanski, P., Banerjee, S., Chakrabarty, K.: ML-TIME: ML-driven Timing Analysis of Integrated Circuits in the Presence of Process Variations and Aging Effects. In: Proceedings of the 2024 ACM/IEEE International Symposium on Machine Learning for CAD. pp. 1–9. Association for Computing Machinery, Salt Lake City, UT, USA (2024). https://doi.org/10.1145/3670474.3685968.
  4. Domanski, P., Ray, A., Lafata, K., Firouzi, F., Chakrabarty, K., Pflüger, D.: Advancing blood glucose prediction with neural architecture search and deep reinforcement learning for type 1 diabetics. Biocybernetics and Biomedical Engineering. 44, 481--500 (2024).
  5. Bantel, L., Domanski, P., Pflüger, D.: High-Fidelity Simulation of a Cartpole for Sim-to-Real Deep Reinforcement Learning. In: 2024 4th Interdisciplinary Conference on Electrics and Computer (INTCEC). pp. 1–6 (2024). https://doi.org/10.1109/INTCEC61833.2024.10602859.
  6. Domanski, P., Ray, A., Firouzi, F., Lafata, K., Chakrabarty, K., Pflüger, D.: Blood Glucose Prediction for Type-1 Diabetics using Deep Reinforcement Learning. In: 2023 IEEE International Conference on Digital Health (ICDH). pp. 339–347 (2023). https://doi.org/10.1109/ICDH60066.2023.00042.
  7. Domanski, P., Pflüger, D., Latty, R.: Learn to Tune: Robust Performance Tuning in Post-Silicon Validation. In: 2023 IEEE European Test Symposium (ETS). pp. 1–4 (2023). https://doi.org/10.1109/ETS56758.2023.10174123.
  8. Domanski, P., Pflüger, D., Rivoir, J., Latty, R.: Self-Learning Tuning for Post-Silicon Validation, https://arxiv.org/abs/2111.08995, (2022).
  9. Amrouch, H., Anders, J., Becker, S., Betka, M., Bleher, G., Domanski, P., Elhamawy, N., Ertl, T., Gatzastras, A., Genssler, P., Hasler, S., Heinrich, M., van Hoorn, A., Jafarzadeh, H., Kallfass, I., Klemme, F., Koch, S., Küsters, R., Lalama, A., Latty, R., Liao, Y., Lylina, N., Haghi, Z.N., Pflüger, D., Polian, I., Rivoir, J., Sauer, M., Schwachhofer, D., Templin, S., Volmer, C., Wagner, S., Weiskopf, D., Wunderlich, H.-J., Yang, B., Zimmermann, M.: Intelligent Methods for Test and Reliability. In: 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE). pp. 969–974 (2022). https://doi.org/10.23919/DATE54114.2022.9774526.
  10. Domanski, P., Pflüger, D., Latty, R., Rivoir, J.: ORSA: Outlier Robust Stacked Aggregation for Best- and Worst-Case Approximations of Ensemble Systems. In: Wani, M.A., Sethi, I.K., Shi, W., Qu, G., Raicu, D.S., and Jin, R. (eds.) 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). pp. 1357–1364. IEEE, Piscataway (2021). https://doi.org/10.1109/ICMLA52953.2021.00220.
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