Analysis of the relationship between training data volume and model quality for surrogate models in physical simulations
- Typically, the quality of AI models is highly dependent on the amount and quality of the available training data. While for many applications of AI several million training datasets are available in accessible databases and can be easily extended, the generation of training data for surrogate models of physics simulations is computationally demanding. To identify the necessary amount of training data for a sufficient good AI model, we are evaluating the performance of a surrogate model for a thermomechanical production process with different sizes of artificially generated training data.
| Author: | Tom Roeger, Ludwig VogtGND, Tobias Friedrich, Johannes SchilpGND |
|---|---|
| URN: | urn:nbn:de:bvb:384-opus4-1158601 |
| Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/115860 |
| ISSN: | 2212-8271OPAC |
| Parent Title (English): | Procedia CIRP |
| Publisher: | Elsevier BV |
| Place of publication: | Amsterdam |
| Type: | Article |
| Language: | English |
| Year of first Publication: | 2024 |
| Publishing Institution: | Universität Augsburg |
| Release Date: | 2024/10/14 |
| Volume: | 126 |
| First Page: | 745 |
| Last Page: | 750 |
| DOI: | https://doi.org/10.1016/j.procir.2024.08.302 |
| Institutes: | Fakultät für Angewandte Informatik |
| Fakultät für Angewandte Informatik / Institut für Informatik | |
| Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Ingenieurinformatik mit Schwerpunkt Produktionsinformatik | |
| Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
| Licence (German): |  CC-BY-NC-ND 4.0: Creative Commons: Namensnennung - Nicht kommerziell - Keine Bearbeitung |
