Abstract
The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. Machine learning (ML) offers a wealth of techniques to extract ...Read More
Keywords
Related Publications
On the Spreading of Liquids on Solid Surfaces: Static and Dynamic Contact Lines
The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiote...
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiote...
The effects of surface tension and viscosity on the stability of two superposed fluids
ABSTRACT The effect of surface tension on the stability of two superposed fluids can be described in a universal way by a non-dimensional ‘surface tension number’ S which provid...
Simple fluids near rigid solids: statistical mechanics of density and contact angle
By making a simple approximation for the two-particle distribution function in a fluid, an approximate formula is obtained for the fluid density n(r) throughout the liquid-vapou...
The Electromagnetic Theory of Surface Enhanced Spectroscopy
Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for an ML revolution a...
Publication Info
- Year
- 1969
- Type
- article
- Volume
- 1
- Issue
- 1
- Pages
- 293-316
- Citations
- 444
- Access
- Closed
External Links
Social Impact
Social media, news, blog, policy document mentions
Citation Metrics
Cite This
Identifiers
- DOI
- 10.1146/annurev.fl.01.010169.001453