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Physics-Informed Neural Networks for Quantum Eigenvalue Problems

Autor: Jin, Henry, Mattheakis, Marios, Protopapas, Pavlos

Eigenvalue problems are critical to several fields of science and engineering. We expand on the method of using unsupervised neural networks for discovering eigenfunctions and eigenvalues for differential eigenvalue problems. The obtained solutions a

Externí odkaz: http://arxiv.org/abs/2203.00451

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Unsupervised Neural Networks for Quantum Eigenvalue Problems

Autor: Jin, Henry, Mattheakis, Marios, Protopapas, Pavlos

Eigenvalue problems are critical to several fields of science and engineering. We present a novel unsupervised neural network for discovering eigenfunctions and eigenvalues for differential eigenvalue problems with solutions that identically satisfy

Externí odkaz: http://arxiv.org/abs/2010.05075

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NBDT: Neural-Backed Decision Trees

Autor: Wan, Alvin, Dunlap, Lisa, Ho, Daniel, Yin, Jihan, Lee, Scott, Jin, Henry, Petryk, Suzanne, Bargal, Sarah Adel, Gonzalez, Joseph E.

Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use. In response, previous work combines decision trees with deep learning, yielding models that (1) sacri

Externí odkaz: http://arxiv.org/abs/2004.00221

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