L. Yuan

Statistical and Nonlinear Physics · with Deng · 2022

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 L. Yuan trusts overall

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

  1. G. Em Karniadakis
  2. Ameya Dilip Jagtap

Papers from L. Yuan's perspective

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

  1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  2. Understanding the difficulty of training deep feedforward neural networks
  3. Automatic differentiation in machine learning: a survey
  4. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
  5. Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations
  6. fPINNs: Fractional Physics-Informed Neural Networks
  7. Learning Multiple Layers of Features from Tiny Images
  8. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
  9. TensorFlow: A system for large-scale machine learning
  10. Long Short-Term Memory

Their papers