Pages that link to "Item:Q2699382"
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The following pages link to Greedy training algorithms for neural networks and applications to PDEs (Q2699382):
Displaying 11 items.
- Physics-informed neural networks for approximating dynamic (hyperbolic) PDEs of second order in time: error analysis and algorithms (Q6087958) (← links)
- Greedy Training Algorithms for Neural Networks and Applications to PDEs (Q6372409) (← links)
- Entropy-based convergence rates of greedy algorithms (Q6551364) (← links)
- Gauss Newton method for solving variational problems of PDEs with neural network discretizaitons (Q6569679) (← links)
- Solving Poisson problems in polygonal domains with singularity enriched physics informed neural networks (Q6585303) (← links)
- Randomized neural network methods for solving obstacle problems (Q6621121) (← links)
- An accurate and efficient continuity-preserved method based on randomized neural networks for elliptic interface problems (Q6623701) (← links)
- Approximation results for gradient flow trained shallow neural networks in \(1d\) (Q6648717) (← links)
- Training neural networks from an ergodic perspective (Q6655492) (← links)
- Homotopy relaxation training algorithms for infinite-width two-layer ReLU neural networks (Q6665313) (← links)
- Mini-workshop: Mathematics of entropic AI in the natural sciences. Abstracts from the mini-workshop held April 7--12, 2024 (Q6671615) (← links)
