Terry S. Haut
Materials Chemistry · 2013–2016
A researcher in the Science corpus: 3 papers; trusts 9 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Materials Chemistry — 19 citations into it; #7,620 of 11,513 by trust here, #10,685 by citations
- Computational Theory and Mathematics — 12 citations into it; #9,635 of 12,182 by trust here, #8,756 by citations
- Computational Mechanics — 8 citations into it
- Numerical Analysis — 6 citations into it
- Physical and Theoretical Chemistry — 5 citations into it
Whom Terry S. Haut trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yvon Maday
- Paul F. Fischer
- Frédéric Legoll
- Stephen M. Cox
- Frédéric Hecht
- Tony Lelièvre
- J. Behler
- Aly-Khan Kassam
- Giovanni Samaey
- Paul Matthews
Who trusts Terry S. Haut overall
Papers from Terry S. Haut's perspective
Ordered by how strongly the researchers Terry S. Haut trusts endorse each paper. Same corpus, a different order for every researcher.
- A Parareal in Time Semi-implicit Approximation of the Navier-Stokes Equations
- Parareal in time algorithm for kinetic systems based on model reduction
- Heterogeneous Multiscale Methods: A Review (Communications in Computational Physics)
- Exponential Time Differencing for Stiff Systems
- Fourth-Order Time-Stepping for Stiff PDEs
- A Micro-Macro Parareal Algorithm: Application to Singularly Perturbed Ordinary Differential Equations
- Analysis of the Parareal Time-Parallel Time-Integration Method (SIAM Journal on Scientific Computing)
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
Their papers
- Learning molecular energies using localized graph kernels. (2016) — 55 citations
- Learning Potential Energy Landscapes using Graph Kernels (2016)
- An Asymptotic Parallel-in-Time Method for Highly Oscillatory PDEs (2013) — 72 citations