David Jin

Statistical and Nonlinear Physics · 2021

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

Subfields

Whom David Jin trusts

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

  1. G. Em Karniadakis (in Statistical and Nonlinear Physics)

Papers from David Jin's perspective

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

  1. Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
  2. DeepXDE: A Deep Learning Library for Solving Differential Equations
  3. PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
  4. Neural Operator: Graph Kernel Network for Partial Differential Equations (International Conference on Learning Representations)
  5. PDE-Net: Learning PDEs from Data
  6. Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism (AAAI Spring Symposium: MLPS)
  7. Machine Learning for Fluid Mechanics (Annual Review of Fluid Mechanics)
  8. A Multiscale Neural Network Based on Hierarchical Matrices
  9. Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers (Neural Information Processing Systems)
  10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers