Pages that link to "Item:Q2697278"
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The following pages link to An overview on deep learning-based approximation methods for partial differential equations (Q2697278):
Displaying 15 items.
- Uniform convergence guarantees for the deep Ritz method for nonlinear problems (Q2110466) (← links)
- (Q5043153) (← links)
- Capturing the diffusive behavior of the multiscale linear transport equations by asymptotic-preserving convolutional deeponets (Q6118592) (← links)
- Lower bounds for artificial neural network approximations: a proof that shallow neural networks fail to overcome the curse of dimensionality (Q6155895) (← links)
- Numerical methods for backward stochastic differential equations: a survey (Q6158181) (← links)
- Deep Weak Approximation of SDEs: A Spatial Approximation Scheme for Solving Kolmogorov Equations (Q6173002) (← links)
- Solving Kolmogorov PDEs without the curse of dimensionality via deep learning and asymptotic expansion with Malliavin calculus (Q6176082) (← links)
- Learning the random variables in Monte Carlo simulations with stochastic gradient descent: Machine learning for parametric PDEs and financial derivative pricing (Q6178392) (← links)
- Pricing options on flow forwards by neural networks in a Hilbert space (Q6181517) (← links)
- The use of physics-informed neural network approach to image restoration via nonlinear PDE tools (Q6189287) (← links)
- Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions (Q6204733) (← links)
- An overview on deep learning-based approximation methods for partial differential equations (Q6356747) (← links)
- Neural network expression rates and applications of the deep parametric PDE method in counterparty credit risk (Q6549602) (← links)
- Deep neural network expressivity for optimal stopping problems (Q6565562) (← links)
- Approximation rates for deep calibration of (rough) stochastic volatility models (Q6606848) (← links)
