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Found 4 papers, 1 papers with code
Synthesis parameter effect detection using quantitative representations and high dimensional distribution distances
Detection of effects of the parameters of the synthetic process on the microstructure of materials is an important, yet elusive goal of materials science.
Do Neural Networks Trained with Topological Features Learn Different Internal Representations?
While this field, sometimes known as topological machine learning (TML), has seen some notable successes, an understanding of how the process of learning from topological features differs from the process of learning from raw data is still limited.
Accelerated Computation of a High Dimensional Kolmogorov-Smirnov Distance
We perform power analysis of ddKS and its approximations on a corpus of datasets and compare to other common high dimensional two sample tests and distances: Hotelling's T^2 test and Kullback-Leibler divergence.
Physics-informed Neural-Network Software for Molecular Dynamics Applications
We have developed a novel differential equation solver software called PND based on the physics-informed neural network for molecular dynamics simulators.
