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Neural ODE

model2 events
papersTODAY 04:00 UTC

Gradient Repair Method Aimed at Stabilizing Neural ODE Training

A new arXiv paper introduces GradRepair-ODE, a technique that certifies and repairs gradients when neural ordinary differential equations are trained. Because neural ODEs embed a numerical solver in the training loop, solver choices shape both the forward trajectory and the gradients sent to the optimizer, which the authors flag as a reliability issue for scientific machine learning. The approach is presented as a way to keep those gradients trustworthy during training.

neural-ode/training/research/scientific-ml
papersSEP 10 04:00 UTC

Convolutional autoencoder and neural ODE framework for transient counterflow flame modeling

Researchers propose a reduced-order modeling framework that combines a convolutional autoencoder with a neural ordinary differential equation to serve as a surrogate for simulating transient two-dimensional counterflow flames. The approach extends autoencoder–neural ODE techniques, previously applied to homogeneous reactive systems, to spatially resolved combustion problems. Such surrogates can cut the computational cost of modeling reactive flows.

scientific-ml/neural-ode/combustion/surrogate-models