GitHub

DeepONet: Learning nonlinear operators

The source code for the paper L. Lu, P. Jin, G. Pang, Z. Zhang, & G. E. Karniadakis. Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nature Machine ...
archive

Learning data mining with Python : harness the power of Python to analyze data and create insightful predictive models

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Hosted on MSN

2D Green's Theorem Example; Solving Both Sides of the Equation

Physics and Python stuff. Most of the videos here are either adapted from class lectures or solving physics problems. I really like to use numerical calculations without all the fancy programming ...
GitHub

An analysis tool for Python that blurs the line between testing and type systems.

THE LATEST NEWS: Python's most popular property-based testing tool, Hypothesis, now supports running CrossHair as an optional backend! If you have a function with type annotations and add a contract ...
C&EN

The Power of Hellmann–Feynman Theorem: Kohn–Sham DFT Energy Derivatives with Respect to the Parameters of the Exchange-Correlation Functional at Linear Cost

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. You may have access to this article through your institution.
Frontiers

Bayesian inference: more than Bayes’s theorem

The Bayesian approach to statistical inference and other data analysis tasks gets its name from Bayes’s theorem (BT). BT specifies that a posterior probability for a hypothesis concerning a data ...