3 papers
cs.LG2025
AMStraMGRAM: Adaptive Multi-cutoff Strategy Modification for ANaGRAM
Nilo Schwencke, Cyriaque Rousselot, Alena Shilova +1
Recent works have shown that natural gradient methods can significantly outperform standard optimizers when training physics-informed neural networks (PINNs). In this paper, we ana…
cond-mat.stat-mech2025
Building causation links in stochastic nonlinear systems from data
Sergio Chibbaro, Cyril Furtlehner, Théo Marchetta +2
Causal relationships play a fundamental role in understanding the world around us. The ability to identify and understand cause-effect relationships is critical to making informed…
cs.LG2025
ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient PINNs learning
Nilo Schwencke, Cyril Furtlehner
In the recent years, Physics Informed Neural Networks (PINNs) have received strong interest as a method to solve PDE driven systems, in particular for data assimilation purpose. Th…