Min Zhu

Statistical and Nonlinear Physics · with Kartha · 2022

A researcher in the Science corpus: 1 paper; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Min Zhu trusts overall

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

  1. Maizar Raissi
  2. Lu Lu
  3. G. Em Karniadakis
  4. Sifan Wang
  5. Michael L. Stein
  6. Ilya M. Sobol
  7. Rémi Bardenet
  8. Melissa E. O'Neill
  9. Paris Perdikaris
  10. Alireza Yazdani

Papers from Min Zhu's perspective

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

  1. fPINNs: Fractional Physics-Informed Neural Networks
  2. GENERATIVE ADVERSARIAL NETS
  3. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
  4. Physics-informed neural networks for inverse problems in nano-optics and metamaterials.
  5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  6. DeepXDE: A Deep Learning Library for Solving Differential Equations
  7. Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
  8. Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
  9. Physics-informed neural networks for high-speed flows
  10. Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism (AAAI Spring Symposium: MLPS)

Their papers