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Trost, C. O. W., Žák, S., Schaffer, S., Walch, L., Zitz, J., Klünsner, T., Leitner, H., Exl, L., & Cordill, M. J. (2025). Explainable machine learning and feature engineering applied to nanoindentation data. Materials & Design, 253, Artikel 113897. https://doi.org/10.1016/j.matdes.2025.113897, https://doi.org/10.1016/j.matdes.2025.113897

Schaffer, S., & Exl, L. (2024). Constraint free physics-informed machine learning for micromagnetic energy minimization. Computer Physics Communications, 300, Artikel 109202. https://doi.org/10.1016/j.cpc.2024.109202

Kovacs, A., Exl, L., Kornell, A., Fischbacher, J., Hovorka, M., Gusenbauer, M., Breth, L., Oezelt, H., Yano, M., Sakuma, N., Kinoshita, A., Shoji, T., Kato, A., & Schrefl, T. (2024). Image-based prediction and optimization of hysteresis properties of nanocrystalline permanent magnets using deep learning. Journal of Magnetism and Magnetic Materials, 596, Artikel 171937. https://doi.org/10.1016/j.jmmm.2024.171937

Schaffer, S., Schrefl, T., Oezelt, H., Kovacs, A., Breth, L., Mauser, N. J., Suess, D., & Exl, L. (2023). Physics-informed machine learning and stray field computation with application to micromagnetic energy minimization. Journal of Magnetism and Magnetic Materials, 576, Artikel 170761. https://doi.org/10.1016/j.jmmm.2023.170761

Trost, C. O. W., Zak, S., Schaffer, S., Saringer, C., Exl, L., & Cordill, M. J. (2022). Bridging Fidelities to Predict Nanoindentation Tip Radii Using Interpretable Deep Learning Models. JOM, 74(6), 2195-2205. https://doi.org/10.1007/s11837-022-05233-z

Kovacs, A., Exl, L., Kornell, A., Fischbacher, J., Hovorka, M., Gusenbauer, M., Breth, L., Oezelt, H., Praetorius, D., Suess, D., & Schrefl, T. (2022). Magnetostatics and micromagnetics with physics informed neural networks. Journal of Magnetism and Magnetic Materials, 548, Artikel 168951. https://doi.org/10.1016/j.jmmm.2021.168951

Schaffer, S., Mauser, N. J., Schrefl, T., Suess, D., & Exl, L. (2022). Machine learning methods for the prediction of micromagnetic magnetization dynamics. IEEE Transactions on Magnetics, 58(2), Artikel 7100506. https://doi.org/10.1109/TMAG.2021.3095251

Kovacs, A., Exl, L., Kornell, A., Fischbacher, J., Hovorka, M., Gusenbauer, M., Breth, L., Oezelt, H., Yano, M., Sakuma, N., Kinoshita, A., Shoji, T., Kato, A., & Schrefl, T. (2022). Conditional physics informed neural networks. Communications in Nonlinear Science and Numerical Simulation, 104, Artikel 106041. https://doi.org/10.1016/j.cnsns.2021.106041

Exl, L., Mauser, N. J., Schaffer, S., Schrefl, T., & Suess, D. (2021). Prediction of magnetization dynamics in a reduced dimensional feature space setting utilizing a low-rank kernel method. Journal of Computational Physics, 444, Artikel 110586. https://doi.org/10.1016/j.jcp.2021.110586

Exl, L., Mauser, N. J., Schrefl, T., & Suess, D. (2020). Learning time-stepping by nonlinear dimensionality reduction to predict magnetization dynamics. Communications in Nonlinear Science and Numerical Simulation, 84, Artikel 105205. https://doi.org/10.1016/j.cnsns.2020.105205

Pfeiler, C. M., Ruggeri, M., Stiftner, B., Exl, L., Hochsteger, M., Hrkac, G., Schöberl, J., Mauser, N. J., & Praetorius, D. (2020). Computational micromagnetics with Commics. Computer Physics Communications, 248, Artikel 106965. https://doi.org/10.1016/j.cpc.2019.106965

Kovacs, A., Fischbacher, J., Oezelt, H., Gusenbauer, M., Exl, L., Bruckner, F., Suess, D., & Schrefl, T. (2019). Learning magnetization dynamics. Journal of Magnetism and Magnetic Materials, 491, Artikel 165548. https://doi.org/10.1016/j.jmmm.2019.165548

Mennemann, J. F., Langen, T., Exl, L., & Mauser, N. J. (2019). Optimal control of the self-bound dipolar droplet formation process. Computer Physics Communications, 244, 205-216. https://doi.org/10.1016/j.cpc.2019.06.002

Exl, L. (2019). An optimization approach for dynamical Tucker tensor approximation. Results in Applied Mathematics, 1, Artikel 100002. https://doi.org/10.1016/j.rinam.2019.100002