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Max Simon Otto / Git For BA
Creative Commons Zero v1.0 UniversalUpdated -
The present study builds upon the findings present in open literature by implementing a physics-inspired neural network and systematically comparing its accuracy to the finite element method, which serves as a benchmark for evaluation. While this work does not claim to introduce novel contributions, it aims to highlight the disparities between traditional numerical methods and ML based ones. The key objectives met with the study are the followed:
The development of a generalized physics-inspired neural network capable of solving PDEs of varying orders. A comparative study evaluating the accuracy, computational complexity, and efficiency of PINNs against the finite element method.Updated -
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BVT-HTBD / KIWI / TF3 / raman_master
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Teaching material for the lecture and tutorial in digital signal processing
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TEADAL / Advocate
MIT LicenseA toolkit to provide verifiable and immutable records of execution in Kubernetes that turns observability data into verifiable credentials.
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Projekt: Optimale Platzierung von Deckenankern in komplexen Gebäudestrukturen
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Numerische Mathematik I für Ingenieurwissenschaften
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