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Boltzmann generators can now robustly sample rare events in complex physical systems, thanks to a new training method that avoids catastrophic mode collapse.
Forget retraining for every new physics problem – pADAM learns a single generative model that handles forward prediction, inverse inference, and even identifies governing laws across different PDEs.
By tuning step sizes based on the spectral properties of gradients, SpecMuon offers a more stable and faster alternative to Adam and Muon for training physics-informed neural networks.
Forget deep quantum circuits: this new tensor network encoding slashes circuit depth by 96% while scaling to high-resolution images and running on real hardware.
Neural-POD lets you build resolution-invariant, nonlinear basis functions for complex spatiotemporal systems, outperforming classical POD and bridging the gap between traditional ROM and operator learning.
