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Found 4 papers, 0 papers with code
Insight Gained from Migrating a Machine Learning Model to Intelligence Processing Units
The discoveries in this paper show that Intelligence Processing Units (IPUs) offer a viable accelerator alternative to GPUs for machine learning (ML) applications within the fields of materials science and battery research.
Enhancing Polynomial Chaos Expansion Based Surrogate Modeling using a Novel Probabilistic Transfer Learning Strategy
For black-box simulations, non-intrusive PCE allows the construction of these surrogates using a set of simulation response evaluations.
First arrival picking using U-net with Lovasz loss and nearest point picking method
Similar to \cite{wu2019semi}, we use U-net to perform the segmentation as it is proven to be state-of-the-art in many image segmentation tasks.
Progressive transfer learning for low frequency data prediction in full waveform inversion
For the purpose of effective suppression of the cycle-skipping phenomenon in full waveform inversion (FWI), we developed a Deep Neural Network (DNN) approach to predict the absent low-frequency components by exploiting the implicit relation connecting the low-frequency and high-frequency data through the subsurface geological and geophysical properties.
