Pages that link to "Item:Q2025321"
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The following pages link to Overcoming the curse of dimensionality in the numerical approximation of Allen-Cahn partial differential equations via truncated full-history recursive multilevel Picard approximations (Q2025321):
Displaying 21 items.
- Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space (Q825596) (← links)
- Results and questions on a nonlinear approximation approach for solving high-dimensional partial differential equations (Q843725) (← links)
- On existence and uniqueness properties for solutions of stochastic fixed point equations (Q2033965) (← links)
- Computing Lyapunov functions using deep neural networks (Q2043422) (← links)
- Multilevel Picard iterations for solving smooth semilinear parabolic heat equations (Q2063953) (← links)
- Comparative performance of time spectral methods for solving hyperchaotic finance and cryptocurrency systems (Q2131704) (← links)
- A new efficient approximation scheme for solving high-dimensional semilinear PDEs: control variate method for deep BSDE solver (Q2133701) (← links)
- Overcoming the curse of dimensionality in the numerical approximation of parabolic partial differential equations with gradient-dependent nonlinearities (Q2162115) (← links)
- On the speed of convergence of Picard iterations of backward stochastic differential equations (Q2165738) (← links)
- Multilevel Picard approximations of high-dimensional semilinear partial differential equations with locally monotone coefficient functions (Q2165859) (← links)
- Multilevel Picard approximations for McKean-Vlasov stochastic differential equations (Q2247730) (← links)
- Overcoming the curse of dimensionality in the numerical approximation of backward stochastic differential equations (Q2694433) (← links)
- An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
- Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning (Q5019943) (← links)
- On nonlinear Feynman–Kac formulas for viscosity solutions of semilinear parabolic partial differential equations (Q5021119) (← links)
- Overcoming the curse of dimensionality in the numerical approximation of semilinear parabolic partial differential equations (Q5161194) (← links)
- Numerical Simulations for Full History Recursive Multilevel Picard Approximations for Systems of High-Dimensional Partial Differential Equations (Q5162373) (← links)
- Three ways to solve partial differential equations with neural networks — A review (Q6068232) (← links)
- The Kolmogorov infinite dimensional equation in a Hilbert space via deep learning methods (Q6112485) (← links)
- The Calderón's problem via DeepONets (Q6570540) (← links)
- Overcoming the curse of dimensionality in the numerical approximation of high-dimensional semilinear elliptic partial differential equations (Q6645961) (← links)
