ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1809.02362
  4. Cited By
A proof that artificial neural networks overcome the curse of
  dimensionality in the numerical approximation of Black-Scholes partial
  differential equations
v1v2v3 (latest)

A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations

7 September 2018
Philipp Grohs
F. Hornung
Arnulf Jentzen
Philippe von Wurstemberger
ArXiv (abs)PDFHTML

Papers citing "A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations"

13 / 13 papers shown
Title
A brief review of the Deep BSDE method for solving high-dimensional partial differential equations
A brief review of the Deep BSDE method for solving high-dimensional partial differential equations
Jiequn Han
Arnulf Jentzen
Weinan E
AI4CE
68
1
0
07 May 2025
Full error analysis of the random deep splitting method for nonlinear parabolic PDEs and PIDEs
Full error analysis of the random deep splitting method for nonlinear parabolic PDEs and PIDEs
Ariel Neufeld
Philipp Schmocker
Sizhou Wu
97
7
0
08 May 2024
Computing excited states of molecules using normalizing flows
Computing excited states of molecules using normalizing flows
Yahya Saleh
Álvaro Fernández Corral
Emil Vogt
Armin Iske
J. Küpper
A. Yachmenev
84
7
0
31 Aug 2023
Rectified deep neural networks overcome the curse of dimensionality for
  nonsmooth value functions in zero-sum games of nonlinear stiff systems
Rectified deep neural networks overcome the curse of dimensionality for nonsmooth value functions in zero-sum games of nonlinear stiff systems
C. Reisinger
Yufei Zhang
53
70
0
15 Mar 2019
Solving the Kolmogorov PDE by means of deep learning
Solving the Kolmogorov PDE by means of deep learning
C. Beck
S. Becker
Philipp Grohs
Nor Jaafari
Arnulf Jentzen
73
95
0
01 Jun 2018
Universal approximations of invariant maps by neural networks
Universal approximations of invariant maps by neural networks
Dmitry Yarotsky
126
213
0
26 Apr 2018
Forward-Backward Stochastic Neural Networks: Deep Learning of
  High-dimensional Partial Differential Equations
Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations
M. Raissi
114
188
0
19 Apr 2018
Mastering Chess and Shogi by Self-Play with a General Reinforcement
  Learning Algorithm
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
David Silver
Thomas Hubert
Julian Schrittwieser
Ioannis Antonoglou
Matthew Lai
...
D. Kumaran
T. Graepel
Timothy Lillicrap
Karen Simonyan
Demis Hassabis
164
1,785
0
05 Dec 2017
The Deep Ritz method: A deep learning-based numerical algorithm for
  solving variational problems
The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems
E. Weinan
Ting Yu
130
1,393
0
30 Sep 2017
Optimal Approximation with Sparsely Connected Deep Neural Networks
Optimal Approximation with Sparsely Connected Deep Neural Networks
Helmut Bölcskei
Philipp Grohs
Gitta Kutyniok
P. Petersen
202
256
0
04 May 2017
The Power of Depth for Feedforward Neural Networks
The Power of Depth for Feedforward Neural Networks
Ronen Eldan
Ohad Shamir
230
732
0
12 Dec 2015
Provable approximation properties for deep neural networks
Provable approximation properties for deep neural networks
Uri Shaham
A. Cloninger
Ronald R. Coifman
188
231
0
24 Sep 2015
Speech Recognition with Deep Recurrent Neural Networks
Speech Recognition with Deep Recurrent Neural Networks
Alex Graves
Abdel-rahman Mohamed
Geoffrey E. Hinton
232
8,529
0
22 Mar 2013
1