This image shows Julius Herb

Julius Herb

M. Sc.

Doctoral Researcher
Institute of Applied Mechanics (MIB)
Data Analytics in Engineering
[Photo: Julius Herb]

Contact

Universitätsstraße 32
70569 Stuttgart
Germany
Room: 2.331

Office Hours

by arrangement  - please write me an e-mail or come to my office

Subject

Submitted:

  • Keshav, S., Herb, J., & Fritzen, F. (2025). Spectral Normalization and Voigt-Reuss net: A universal approach to microstructure-property forecasting with physical guarantees. Available at arXiv: https://arxiv.org/abs/2504.00712
  • Rettberg, J., Kneifl, J., Herb, J., Buchfink, P., Fehr, J. & Haasdonk, B. (2024). Data-driven identification of latent port-Hamiltonian systems. Available at arXiv: https://arxiv.org/abs/2408.08185

Published:

  • Fritzen, F., Herb, J. & Sharba, S. (2024). Thermo-Plastic Nonuniform Transformation Field Analysis for Eigenstress Analysis of Materials Undergoing Laser Melt Injection. Computer Methods in Applied Mechanics and Engineering (CMAME). DOI: 10.1016/j.cma.2024.117487
  • Sharba, S., Herb, J. & Fritzen, F. (2023). Reduced order homogenization of thermoelastic materials with strong temperature dependence and comparison to a machine-learned model. Archive of Applied Mechanics, 93, 2855-2876. DOI: 10.1007/s00419-023-02411-6
Summer term 2025 Data Processing for Engineers and Scientists (Block course)
Winter term 2024/25 Data Processing for Engineers and Scientists
Summer term 2024 Data Processing for Engineers and Scientists (Block course)
since 03/2024 Doctoral Researcher at the University of Stuttgart, Institute of Applied Mechanics, Data Analytics in Engineering (DAE)
10/2021-02/2024 Master studies in Simulation Technology at the University of Stuttgart (M.Sc.)
10/2018-11/2021 Bachelor studies in Simulation Technology at the University of Stuttgart (B.Sc.)
06/2018 Abitur at Gymnasium Friedrich II. Lorch
  • 18. February 2025: ECCOMAS Artificial Intelligence and Computational Methods in Applied Science (AICOMAS 2025), Paris, "FNO-CG: Accelerating Conjugate Gradient Solvers for Homogenization Problems with Fourier Neural Operators"
  • 12. February 2025: GAMM Workshop Data-driven modeling and numerical simulation of microstructured materials (GAMM AG Data), Darmstadt, "FNO-CG: Accelerating Conjugate Gradient Solvers for Homogenization Problems with Fourier Neural Operators"
  • 25. September 2024: DGM Materials Science and Engineering (MSE 2024), Darmstadt, "FNO-CG: Accelerating CG solvers using Fourier Neural Operators (FNOs)"
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