نسخة أولية وصول مفتوح
Uncovering and Fixing Collider Bias in Bayesian PINNs
Bayesian physics-informed neural networks (B-PINNs) are a popular framework for parameter and state inference from sparse or noisy observations. They are commonly formulated via a collider structure, in which physical and trajectory parameters are assumed to be a priori independent and become coupled through virtual li …