C. Gray

Computational Mechanics · with Cai · 2021

A researcher in the Science corpus: 1 paper; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computational Mechanics — 12 citations into it
• Statistical and Nonlinear Physics — 10 citations into it

Whom C. Gray trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Markus Raffel

Papers from C. Gray's perspective

Ordered by how strongly the researchers C. Gray trusts endorse each paper. Same corpus, a different order for every researcher.

1. Automatic differentiation in machine learning: a survey
2. Particle Image Velocimetry: A Practical Guide
3. Solving high-dimensional partial differential equations using deep learning
4. Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
5. Turbulence Modeling in the Age of Data (Annual Review of Fluid Mechanics)
6. B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data
7. Machine Learning for Fluid Mechanics (Annual Review of Fluid Mechanics)
8. Planar laser induced fluorescence in aqueous flows
9. NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations
10. Background-oriented schlieren (BOS) techniques

Their papers

• Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks (2021) — 344 citations