AI RESEARCH
Unbiased and Second-Order-Free Training for High-Dimensional PDEs
arXiv CS.LG
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ArXi:2605.14643v1 Announce Type: new Deep learning methods based on backward stochastic differential equations (BSDEs) have emerged as competitive alternatives to physics-informed neural networks (PINNs) for solving high-dimensional partial differential equations (PDEs). By leveraging probabilistic representations, BSDE approaches can avoid the curse of dimensionality and often admit second-order-free