Arnulf Jentzen

Finance · 2009–2019

A researcher in the Science corpus: 17 papers; trusts 84 researchers and is trusted by 51 researchers here, as inferred from the citation graph.

Subfields

• Finance — 139 citations into it; #1,039 of 10,518 by trust here, #1,148 by citations
• Computational Theory and Mathematics — 50 citations into it; #7,795 of 12,182 by trust here, #7,851 by citations
• Computational Mechanics — 41 citations into it; #1,928 of 10,001 by trust here, #1,536 by citations
• Numerical Analysis — 41 citations into it
• Demography — 35 citations into it; #604 of 4,236 by trust here, #1,005 by citations
• Mathematical Physics — 35 citations into it; #6,581 of 8,562 by trust here, #5,794 by citations
• Artificial Intelligence — 28 citations into it; #10,503 of 35,345 by trust here, #11,921 by citations
• Management Science and Operations Research — 27 citations into it; #3,425 of 11,946 by trust here, #5,787 by citations

Whom Arnulf Jentzen trusts overall

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

1. Peter E. Kloeden
2. Erika Hausenblas
3. István Gyöngy
4. D. Crisan
5. Desmond John Higham
6. Pierre Henry-Labordère
7. Giuseppe Da Prato
8. Jean-François Chassagneux
9. Claudia Prévôt
10. Nicolai V. Krylov

Who trusts Arnulf Jentzen overall

1. Peter E. Kloeden
2. Xuerong Mao
3. Sotirios Sabanis
4. Giuseppe Da Prato
5. Hongwei Guo
6. Xiaoying Zhuang
7. Martin Hutzenthaler
8. Charles-Edouard Br'ehier
9. Jiequn Han
10. Tony Shardlow

Papers from Arnulf Jentzen's perspective

Ordered by how strongly the researchers Arnulf Jentzen 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. Gradient-based learning applied to document recognition
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Deep Learning-Based Numerical Methods for High-Dimensional Parabolic Partial Differential Equations and Backward Stochastic Differential Equations (2017) — 932 citations
• The Numerical Approximation of Stochastic Partial Differential Equations (2009) — 174 citations
• Strong convergence of an explicit numerical method for SDEs with nonglobally Lipschitz continuous coefficients (2010) — 490 citations
• Weak convergence rates of spectral Galerkin approximations for SPDEs with nonlinear diffusion coefficients (2014) — 48 citations
• Overcoming the curse of dimensionality in the approximative pricing of financial derivatives with default risks (2019) — 59 citations
• Solving high-dimensional partial differential equations using deep learning (2017) — 2,069 citations
• Machine Learning Approximation Algorithms for High-Dimensional Fully Nonlinear Partial Differential Equations and Second-order Backward Stochastic Differential Equations (2017) — 364 citations
• Strong convergence rates for an explicit numerical approximation method for stochastic evolution equations with non-globally Lipschitz continuous nonlinearities (2015) — 64 citations
• Local Lipschitz Continuity in the Initial Value and Strong Completeness for Nonlinear Stochastic Differential Equations (2013) — 54 citations
• Loss of regularity for Kolmogorov equations (2012) — 101 citations
• Taylor Approximations for Stochastic Partial Differential Equations (2011) — 152 citations
• A mild Itô formula for SPDEs (2010) — 65 citations
• TAYLOR EXPANSIONS OF SOLUTIONS OF STOCHASTIC PARTIAL DIFFERENTIAL EQUATIONS WITH ADDITIVE NOISE (2010) — 67 citations
• Strong and weak divergence in finite time of Euler's method for stochastic differential equations with non-globally Lipschitz continuous coefficients (2009) — 504 citations
• Pathwise Numerical Approximations of SPDEs with Additive Noise under Non-global Lipschitz Coefficients (2009) — 65 citations
• A random Euler scheme for Carathéodory differential equations (2009) — 48 citations
• Overcoming the order barrier in the numerical approximation of stochastic partial differential equations with additive space–time noise (2009) — 155 citations