Racecar Sampling high-dimensional problems using efficient, on-the-fly estimations of the stochastic gradient noise AdLaLa Efficient training of neural networks using Adaptive Langevin in Layers Cascade failure in power networks Simulating the non-equilibrium dynamics of large electrical power networks Umbrella sampling for cosmology Performing MCMC in parallel for Bayesian Inverse problems in cosmology ACWind A package using transferable neural networks to classify wind turbine performance NOGIN The NOisy Gradient INtegrator ~ A sampling scheme for high-dimensional problems with stochastic gradients Ensemble Quasi-Newton Scale-invariant, Langevin dynamics-based MCMC Constrained Molecular Dynamics A novel integration method for efficient sampling of biomolecules
