Speakers
Description
While Machine Learning (ML) caused a boom in computational sciences and its broad field of applications, some if its weaknesses as its low accuracy, huge training data amount requirement and hard interpretability tighten its domain of applications in complex systems science that demand high scientific, perfomance quality.
Due to our recent findings, we improved classic multivariate polynomial interpolation schemes (MIP) whose strengths and weaknesses tend to be complementary to those of ML methods.
In this presentation, we will demonstrate how to extract the best of both worlds in order to provide models for sparse and scattered data merasurements , enable efficient post processing analysis of ML surrogate models, optimise ML hyperparameters and regularise ML autoencoders.
Especially, we are looking forward to introduce you to the open source minterpy python package, whose alpha-release includes the core implementations making our contribution accessible to the ML community.
| Physical Presentation | I would be willing present physically. |
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