David Sondak

Statistical and Nonlinear Physics · 2020

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

• Statistical and Nonlinear Physics — 12 citations into it
• AI for Physical Sciences — 5 citations into it

Whom David Sondak trusts overall

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

1. Justin A. Sirignano
2. Isaac E. Lagaris
3. Jiequn Han

Papers from David Sondak's perspective

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

1. Automatic differentiation in PyTorch
2. Artificial neural networks for solving ordinary and partial differential equations
3. Solving high-dimensional partial differential equations using deep learning
4. Deep learning for universal linear embeddings of nonlinear dynamics
5. Learning data-driven discretizations for partial differential equations (Proceedings of the National Academy of Sciences of the United States of America)
6. Inferring solutions of differential equations using noisy multi-fidelity data
7. DGM: A deep learning algorithm for solving partial differential equations
8. Approximation capabilities of multilayer feedforward networks
9. Python for Scientific Computing
10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers

• Hamiltonian Neural Networks for solving differential equations (2020) — 41 citations