4 papers
Quantifying constraint hierarchies in Bayesian PINNs via per-constraint Hessian decomposition
Filip Landgren
Bayesian physics-informed neural networks (B-PINNs) merge data with governing equations to solve differential equations under uncertainty. However, interpreting uncertainty and ove…
Predictions with limited data: Bayesian (X)PINNs, entanglement surfaces and overconfidence
Filip Landgren, Marika Taylor
Solving differential equations from limited or noisy data remains a key challenge for physics-informed neural networks (PINNs), which are typically applied to already known and smo…
Islands and entanglement entropy in -dimensional curved backgrounds
Filip Landgren, Arvind Shekar
A large part of the discussion on entanglement islands has explored the specific setup of JT gravity with a flat heatbath coupled to a CFT. In this paper, we consider a m…
A multiverse model in dS wedge holography
Sergio E. Aguilar-Gutierrez, Filip Landgren
We construct a multiverse model where empty AdS space is cut off by a pair of accelerated dS space universes, at a finite AdS boundary cutoff which we treat as a …