Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense | Paper Citation Record

Observation 4ec23928-f084-431d-aad7-5f0d8edee0ba · outbound

This paper cites Approximation properties of residual neural networks for Kolmogorov PDEs.Discrete Contin.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Approximation properties of residual neural networks for Kolmogorov PDEs.Discrete Contin

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.157162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0d16c880-ec1c-41ce-8116-6a0d21ba1bb3 · outbound

This paper cites Numerical solution of inverse problems by weak adversarial networks.Inverse Problems 36, 11 (2020), 115003, 31.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Numerical solution of inverse problems by weak adversarial networks.Inverse Problems 36, 11 (2020), 115003, 31

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.154042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fae4e0e5-8949-488c-bb71-27361f035603 · outbound

This paper cites Deep splitting method for parabolic PDEs.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Deep splitting method for parabolic PDEs

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.156364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 53d13773-929f-40ff-8ba3-469e39c674b8 · outbound

This paper cites Solving the Kolmogorov PDE by means of deep learning.J.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Solving the Kolmogorov PDE by means of deep learning.J

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.357265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 64275338-ac93-4d4d-b915-b87a178e3662 · outbound

This paper cites Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order back- ward stochastic differential equations.J.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order back- ward stochastic differential equations.J

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.353746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f080451d-5faa-429a-a77e-93a6b0022e05 · outbound

This paper cites On existence and uniqueness properties for solutions of stochastic fixed point equations.Discrete Contin.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense On existence and uniqueness properties for solutions of stochastic fixed point equations.Discrete Contin

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.350207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0dce92c1-ae1a-4037-8c1f-19247f790486 · outbound

This paper cites Overcoming the curse of dimensionality in the numerical approximation of high-dimensional semilinear elliptic partial differential equations.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Overcoming the curse of dimensionality in the numerical approximation of high-dimensional semilinear elliptic partial differential equations

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.346731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bb7c49b6-7477-498a-bb5c-f520a0d229db · outbound

This paper cites an unresolved cited work.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-05-24T06:56:03.342984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a0c91d28-73c2-4650-b4df-1dfaea47fb96 · outbound

This paper cites On nonlinear Feynman–Kac formulas for viscosity solutions of semilinear parabolic partial differential equations.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense On nonlinear Feynman–Kac formulas for viscosity solutions of semilinear parabolic partial differential equations

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.340114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 23fb148d-87f1-4753-a588-7d95f96c889e · outbound

This paper cites An overview on deep learning-based approximation methods for partial differential equations.Discrete Contin.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense An overview on deep learning-based approximation methods for partial differential equations.Discrete Contin

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.336699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b8e293de-cdf2-49fc-9800-cface9588b85 · outbound

This paper cites NumericalsimulationsforfullhistoryrecursivemultilevelPicard approximations for systems of high-dimensional partial differential equations.Commun.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense NumericalsimulationsforfullhistoryrecursivemultilevelPicard approximations for systems of high-dimensional partial differential equations.Commun

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.333466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 643bbbb1-4b00-485e-bbd3-d3da6cde7534 · outbound

This paper cites Dynamic programming.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Dynamic programming

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.329916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c13ceca4-f7b1-4dbd-9a0d-b97ee7abd08c · outbound

This paper cites A unified deep artificial neural network approach to partial differential equations in complex geometries.Neurocomputing 317 (2018), 28– 41.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense A unified deep artificial neural network approach to partial differential equations in complex geometries.Neurocomputing 317 (2018), 28– 41

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.326835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 17ec7910-51fd-4645-ac71-35250e001e3c · outbound

This paper cites an unresolved cited work.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-05-24T06:56:03.299417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 56f193b4-d0b9-4977-868b-ea2838548a72 · outbound

This paper cites an unresolved cited work.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-05-24T06:56:03.238108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 06398c33-7cef-4ecb-bd07-a7e93ab9ac18 · outbound

This paper cites Machine learning for semi linear PDEs.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Machine learning for semi linear PDEs

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.305557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 68bb84b0-2d0b-4c2c-b3e5-9785b1e0ae85 · outbound

This paper cites Deep Runge-Kutta schemes for BSDEs.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Deep Runge-Kutta schemes for BSDEs

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-24T06:54:03.220774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9e38a357-c355-4849-b86c-3cf1f1cecfc0 · outbound

This paper cites Deep neural networks overcome the curse of dimensionality in the numerical approximation of semilinear partial differential equations.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Deep neural networks overcome the curse of dimensionality in the numerical approximation of semilinear partial differential equations

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-24T06:54:03.215918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 895d6e78-8aff-42c1-9f2f-8c19375c9e11 · outbound

This paper cites Deep learning-based numerical methods for high- dimensional parabolic partial differential equations and backward stochastic differential equations.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Deep learning-based numerical methods for high- dimensional parabolic partial differential equations and backward stochastic differential equations

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.296223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ed9d5f52-c992-493f-bd19-6329cb228827 · outbound

This paper cites Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning.Nonlinearity 35, 1 (2022), 278–310.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning.Nonlinearity 35, 1 (2022), 278–310

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.293028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 14863f1b-a438-4231-a438-3fc205ca30b8 · outbound

This paper cites an unresolved cited work.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-05-24T06:56:03.289425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 56fc0120-ec91-4a26-b09f-2e8f6aecf089 · outbound

This paper cites Multilevel Picard itera- tions for solving smooth semilinear parabolic heat equations.Partial Differ.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Multilevel Picard itera- tions for solving smooth semilinear parabolic heat equations.Partial Differ

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.286531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ae3bacc5-8fff-41d3-ae53-f8e5c89ff17d · outbound

This paper cites The deep Ritz method: A deep learning-based numerical algorithm for solving variational problems.Commun.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense The deep Ritz method: A deep learning-based numerical algorithm for solving variational problems.Commun

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.311538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7312f469-fc82-454c-97f4-928a42222e44 · outbound

This paper cites DNN expression rate analysis of high-dimensional PDEs: Application to option pricing.Constr.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense DNN expression rate analysis of high-dimensional PDEs: Application to option pricing.Constr

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.283067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c2792a1b-01df-485b-8628-a599745a940d · outbound

This paper cites Fractional weak adversarial networks for the sta- tionary fractional advection dispersion equations.Z.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Fractional weak adversarial networks for the sta- tionary fractional advection dispersion equations.Z

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.278860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f6511de6-f738-49b5-8ef2-0ff8e4743205 · outbound

This paper cites Asymptotic expansion as prior knowledge in deep learning method for high dimensional BSDEs.Asia-Pacific Financial Markets (Mar 2019).

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Asymptotic expansion as prior knowledge in deep learning method for high dimensional BSDEs.Asia-Pacific Financial Markets (Mar 2019)

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.275788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation de8ff125-cd47-48fd-a4d1-1118fa37077f · outbound

This paper cites Approximation error analysis of some deep backward schemes for nonlinear PDEs.SIAM J.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Approximation error analysis of some deep backward schemes for nonlinear PDEs.SIAM J

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.331071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cd7ade47-26fd-4ce7-8aad-3e3cbad56bab · outbound

This paper cites Neural Networks–Based Algorithms for Stochastic Control and PDEs in Finance.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Neural Networks–Based Algorithms for Stochastic Control and PDEs in Finance

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.272092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9ee605ea-3269-4541-aba7-b5435b5ad0aa · outbound

This paper cites Generalised multilevel Picard approximations.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Generalised multilevel Picard approximations

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-24T06:54:03.258335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 13fb871d-9dd4-4c74-ade9-06130936f408 · outbound

This paper cites Uniform er- ror estimates for artificial neural network approximations for heat equations.IMA J.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Uniform er- ror estimates for artificial neural network approximations for heat equations.IMA J

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.268235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ff149eb5-8ab5-4ab6-ba8d-b2f0144393bc · outbound

This paper cites Deep ReLU network expression rates for option prices in high-dimensional, exponential Lévy models.Finance Stoch.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Deep ReLU network expression rates for option prices in high-dimensional, exponential Lévy models.Finance Stoch

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.319900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 732bee12-2590-4455-8720-96586c9a40c6 · outbound

This paper cites Deep neural network approximation for high-dimensional parabolic Hamilton-Jacobi-Bellman equations.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Deep neural network approximation for high-dimensional parabolic Hamilton-Jacobi-Bellman equations

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-24T06:54:03.226439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5e22a2d8-b963-4987-bbfe-40b4ebbeedc8 · outbound

This paper cites Deep neural network approximation for high- dimensional elliptic PDEs with boundary conditions.IMA J.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Deep neural network approximation for high- dimensional elliptic PDEs with boundary conditions.IMA J

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.306809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a3f9914b-10bf-4807-918d-d248afc8a240 · outbound

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

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations.Mem

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.310291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bddd26ba-7528-4098-b910-a73b81a15770 · outbound

This paper cites Space-time er- ror estimates for deep neural network approximations for differential equations.Adv.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Space-time er- ror estimates for deep neural network approximations for differential equations.Adv

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.254277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation af590aa6-ebc0-41b5-8c64-178d452ef232 · outbound

This paper cites Deep neural network approximations for solutions of PDEs based on Monte Carlo algorithms.Partial Differ.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Deep neural network approximations for solutions of PDEs based on Monte Carlo algorithms.Partial Differ

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.250570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 02315c04-9bdc-4a7d-a590-c0d2800c724a · outbound

This paper cites Solving high-dimensional partial differential equa- tions using deep learning.Proc.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Solving high-dimensional partial differential equa- tions using deep learning.Proc

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.302461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 82ed7122-a60d-45f4-8393-98402835fd2a · outbound

This paper cites Convergence of the deep BSDE method for coupled FBSDEs.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Convergence of the deep BSDE method for coupled FBSDEs

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.314852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5eedc035-eab1-419b-8a85-afb1afc61bbb · outbound

This paper cites Deep Primal-Dual Algorithm for BSDEs: Applications of Machine Learning to CVA and IM.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Deep Primal-Dual Algorithm for BSDEs: Applications of Machine Learning to CVA and IM

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.247342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 685dec29-1b67-4fef-8efc-d844fbff6c46 · outbound

This paper cites A., and Johnson, C.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense A., and Johnson, C

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.243806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d26b59ab-4d13-4ceb-8771-96d4b418567e · outbound

This paper cites Space-time deep neural network approximations for high-dimensional partial differential equations.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Space-time deep neural network approximations for high-dimensional partial differential equations

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-24T06:54:03.251931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 728722fa-5d33-4db4-9d91-468b19d94889 · outbound

This paper cites an unresolved cited work.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-05-24T06:56:03.240650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f9248e8a-a7c6-4a62-9af7-fa96a2def5e3 · outbound

This paper cites Deep backward schemes for high-dimensional nonlinear PDEs.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Deep backward schemes for high-dimensional nonlinear PDEs

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.317922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 13ed7b02-9ba8-4f5d-ace5-77b9b7691ac1 · outbound

This paper cites Overcoming the curse of dimen- sionality in the numerical approximation of parabolic partial differential equations with gradient-dependent nonlinearities.Found.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Overcoming the curse of dimen- sionality in the numerical approximation of parabolic partial differential equations with gradient-dependent nonlinearities.Found

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.313564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 20d56fa5-a80d-4b4e-80dd-cc387cf41bd4 · outbound

This paper cites Multilevel Picard approximations for high-dimensional semilinear second-order PDEs with Lipschitz nonlinearities.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Multilevel Picard approximations for high-dimensional semilinear second-order PDEs with Lipschitz nonlinearities

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-05-24T06:54:03.243985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6356b0a5-21bb-4b4b-8c6e-079467afbd04 · outbound

This paper cites an unresolved cited work.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Unresolved cited work

Reference 46

Resolution: unresolved; raw_fallback, observed 2026-05-24T06:56:03.316738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation aa98b937-d5bb-4e67-8fd1-1f93d936e477 · outbound

This paper cites A., and von Wurstemberger, P.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense A., and von Wurstemberger, P

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.323802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation eace4e74-ae99-4f35-982a-935508503166 · outbound

This paper cites Strong $L^p$-error analysis of nonlinear Monte Carlo approximations for high-dimensional semilinear partial differential equations.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Strong $L^p$-error analysis of nonlinear Monte Carlo approximations for high-dimensional semilinear partial differential equations

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-24T06:54:03.238604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b5b95684-9925-4c12-9423-2a835778410c · outbound

This paper cites Overcoming the curse of dimensionality in the approximative pricing of financial derivatives with default risks.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Overcoming the curse of dimensionality in the approximative pricing of financial derivatives with default risks

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.322336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fd6904cc-0954-4b0e-a459-9b604b7ac7b7 · outbound

This paper cites Multilevel Picard approximations of high- dimensional semilinear parabolic differential equations with gradient-dependent nonlin- earities.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Multilevel Picard approximations of high- dimensional semilinear parabolic differential equations with gradient-dependent nonlin- earities

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.303722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e728731a-68d7-4bda-be16-64d014f0423c · outbound

This paper cites an unresolved cited work.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-05-24T06:56:03.290373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f70498a3-7252-459b-8795-bd7ae2697ccf · outbound

This paper cites an unresolved cited work.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-05-24T06:56:03.279125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 434b9057-b140-4613-a134-cf55a1b607ac · outbound

This paper cites Deep curve-dependent PDEs for affine rough volatility.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Deep curve-dependent PDEs for affine rough volatility

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.283520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a1a753d4-fcf7-4266-869e-9a1c55c3b050 · outbound

This paper cites an unresolved cited work.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-05-24T06:56:03.286997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cf695a88-91b7-4de9-b078-ce789e8289d6 · outbound

This paper cites A theoretical analysis of deep neural networks and parametric PDEs.Constr.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense A theoretical analysis of deep neural networks and parametric PDEs.Constr

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.293593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a355d947-47dd-4a44-98de-d73896b39ec8 · outbound

This paper cites Multilevel Picard approximation algorithm for semilinear partial integro-differential equations and its complexity analysis.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Multilevel Picard approximation algorithm for semilinear partial integro-differential equations and its complexity analysis

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-24T06:54:03.231855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 03a149fc-dd4f-4f1f-b425-6a3a001be7b2 · outbound

This paper cites Tractability of multivariate problems.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Tractability of multivariate problems

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.263274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 507ff436-6b58-42a4-8ebe-89fd9705ca07 · outbound

This paper cites Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space.Partial Differ.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space.Partial Differ

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.325447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e0be56ad-a825-4c89-beef-8aac84dbf18e · outbound

This paper cites Neural networks-based backward scheme for fully nonlinear PDEs.Partial Differ.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Neural networks-based backward scheme for fully nonlinear PDEs.Partial Differ

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.275814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b17ea97d-d7da-438f-9bdf-8a750660918c · outbound

This paper cites an unresolved cited work.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-05-24T06:56:03.259701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f88682f1-f373-446a-b3e7-60428147c603 · outbound

This paper cites Rectified deep neural networks overcome the curse of dimensionality for nonsmooth value functions in zero-sum games of nonlinear stiff systems.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Rectified deep neural networks overcome the curse of dimensionality for nonsmooth value functions in zero-sum games of nonlinear stiff systems

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.247833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 673d7559-51fd-4e46-9669-5f463e01535b · outbound

This paper cites an unresolved cited work.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-05-24T06:56:03.328241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1f6a2ab4-af12-465d-af74-b4f5df3cc84d · outbound

This paper cites DGM: A deep learning algorithm for solving partial differential equations.J.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense DGM: A deep learning algorithm for solving partial differential equations.J

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.255917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9d5e325e-b928-413d-8b42-6ee3ad0ddb44 · outbound

This paper cites Towards fast weak adversarial trainingtosolvehighdimensionalparabolicpartialdifferentialequationsusingXNODE- WAN.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Towards fast weak adversarial trainingtosolvehighdimensionalparabolicpartialdifferentialequationsusingXNODE- WAN

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.271764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a4a17e5f-6103-4420-9316-d020d8d463b3 · outbound

This paper cites Weak adversarial networks for high- dimensional partial differential equations.J.

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Weak adversarial networks for high- dimensional partial differential equations.J

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T06:56:03.300523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers