Ariel Neufeld

Nanyang Technological University — Singapore

Economics, Econometrics and Finance · Social Sciences

10h-index629citations116works1.02yr avg
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Research Topics

Stochastic processes and financial applications(58), Risk and Portfolio Optimization(28), Model Reduction and Neural Networks(19), Economic theories and models(14), Financial Risk and Volatility Modeling(11)

Publications116 total

Scaling limits of multi-period distributionally robust optimization problems
ArXiv.org·2025Open Access
Robust mean-field control under common noise uncertainty
arXiv (Cornell University)·2025Open Access
Non-asymptotic estimates for accelerated high order Langevin Monte Carlo algorithms
Analysis and Applications·2025
Nonconcave Stochastic Optimal Control in Finite Discrete Time Under Model Uncertainty
Mathematical Finance·2025
Generative Neural Operators of Log-Complexity Can Simultaneously Solve Infinitely Many Convex Programs
arXiv (Cornell University)·2025Open Access
Multilevel Picard algorithm for general semilinear parabolic PDEs with gradient-dependent nonlinearities
Journal of Numerical Mathematics·2025
Universal approximation results for neural networks with non-polynomial activation function over non-compact domains
Analysis and Applications·2025· 2 cited
Multilevel Picard approximations overcome the curse of dimensionality in the numerical approximation of general semilinear PDEs with gradient-dependent nonlinearities
Journal of Complexity·2025· 1 cited
Multilevel Picard approximation algorithm for semilinear partial integro-differential equations and its complexity analysis
Stochastic Partial Differential Equations Analysis and Computations·2025· 2 cited
Multilevel Picard approximations for McKean-Vlasov stochastic differential equations with nonconstant diffusion
arXiv (Cornell University)·2025Open Access
Deep ReLU neural networks overcome the curse of dimensionality when approximating semilinear partial integro-differential equations
Analysis and Applications·2025
QuLTSF: Long-Term Time Series Forecasting with Quantum Machine Learning
2025· 4 citedOpen Access
Rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of gradient-dependent semilinear heat equations
Communications in Mathematical Sciences·2025
Full error analysis of the random deep splitting method for nonlinear parabolic PDEs and PIDEs
Communications in Nonlinear Science and Numerical Simulation·2024· 3 cited
QuLTSF: Long-Term Time Series Forecasting with Quantum Machine Learning
arXiv (Cornell University)·2024Open Access
Bounding the difference between the values of robust and non-robust Markov decision problems
Journal of Applied Probability·2024· 3 citedOpen Access
Solving stochastic partial differential equations using neural networks in the Wiener chaos expansion
arXiv (Cornell University)·2024Open Access
Universal approximation results for neural networks with non-polynomial activation function over non-compact domains
arXiv (Cornell University)·2024· 1 citedOpen Access
Markov-Nash equilibria in mean-field games under model uncertainty
arXiv (Cornell University)·2024Open Access
Multilevel Picard approximations and deep neural networks with ReLU, leaky ReLU, and softplus activation overcome the curse of dimensionality when approximating semilinear parabolic partial differential equations in $L^p$-sense
arXiv (Cornell University)·2024Open Access
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Frequent Co-authors

Julian Sester(8), Marcel Nutz(7), Sizhou Wu(5), Arnulf Jentzen(4), Qikun Xiang(4), Philipp Schmocker(4), Ying Zhang(4), S. Becker(3), Patrick Cheridito(3), Christian Beck(2), Pushpendu Ghosh(2), Jajati Keshari Sahoo(2), Mario Šikić(2), Michel Baes(2), Calypso Herrera(2), Pierre Ruyssen(2), Dong‐Young Lim(2), Sotirios Sabanis(2), Kai Du(2), Gareth W. Peters(2)