David R. Hatch

Statistical and Nonlinear Physics · 2019

A researcher in the Science corpus: 1 paper; trusts 8 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 12 citations into it
• Artificial Intelligence — 8 citations into it
• Computational Mechanics — 8 citations into it
• AI for Physical Sciences — 5 citations into it

Whom David R. Hatch trusts overall

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

1. Xiaolin Li

Papers from David R. Hatch's perspective

Ordered by how strongly the researchers David R. Hatch 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. Data-driven discovery of partial differential equations
4. Optimization by Simulated Annealing
5. On robust estimation of the location parameter
6. Engineering Design via Surrogate Modelling - A Practical Guide
7. Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
8. On the Spectral Bias of Neural Networks
9. DGM: A deep learning algorithm for solving partial differential equations
10. The Expressive Power of Neural Networks: A View from the Width

Their papers

• Solving Irregular and Data-enriched Differential Equations using Deep Neural Networks (2019) — 23 citations