Pavlos Protopapas

Statistical and Nonlinear Physics · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 20 citations into it
• AI for Physical Sciences — 8 citations into it
• Atmospheric Science — 5 citations into it

Whom Pavlos Protopapas trusts overall

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

1. Justin A. Sirignano
2. Julia Ling
3. Maizar Raissi
4. Giuseppe Carleo
5. Konstantinos Spiliopoulos
6. Kurt Hornik

Papers from Pavlos Protopapas's perspective

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

1. Automatic differentiation in PyTorch
2. Neural Ordinary Differential Equations
3. Artificial neural networks for solving ordinary and partial differential equations
4. Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
5. Solving high-dimensional partial differential equations using deep learning
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. Deep learning for universal linear embeddings of nonlinear dynamics
10. Solving the quantum many-body problem with artificial neural networks

Their papers

• Hamiltonian Neural Networks for solving differential equations (2020) — 41 citations
• Physical Symmetries Embedded in Neural Networks (2019) — 86 citations