A universal Approach to Reduce Computation Complexity: Utilize Machine Learning to Capture Dynamics of Large-Scale High-Resolution Numerical Models.
| dc.rights.license | CC-BY-NC-ND | |
| dc.contributor.advisor | Karssenberg, Derek | |
| dc.contributor.author | Eikelder, Rick ten | |
| dc.date.accessioned | 2023-07-25T00:02:10Z | |
| dc.date.available | 2023-07-25T00:02:10Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/44307 | |
| dc.description.abstract | ["",""] | |
| dc.description.sponsorship | Utrecht University | |
| dc.language.iso | EN | |
| dc.subject | A universal Approach to Reduce Computation Complexity: Utilize Machine Learning to Capture Dynamics of Large-Scale High-Resolution Numerical Models. | |
| dc.title | A universal Approach to Reduce Computation Complexity: Utilize Machine Learning to Capture Dynamics of Large-Scale High-Resolution Numerical Models. | |
| dc.type.content | Master Thesis | |
| dc.rights.accessrights | Open Access | |
| dc.subject.courseuu | Applied Data Science | |
| dc.thesis.id | 20037 |