3 citations · 3 across the 3 of their papers we have counts for
4 papers
Hybrid Physical-Neural Simulator for Fast Cosmological Hydrodynamics
Arne Thomsen, Tilman Tröster, François Lanusse
Cosmological field-level inference requires differentiable forward models that solve the challenging dynamics of gas and dark matter under hydrodynamics and gravity. We propose a h…
Bridging Simulators with Conditional Optimal Transport
Justine Zeghal, Benjamin Remy, Yashar Hezaveh +2
We propose a new field-level emulator that bridges two simulators using unpaired simulation datasets. Our method leverages a flow-based approach to learn the likelihood transport f…
Automating Inference of Binary Microlensing Events with Neural Density Estimation
Keming Zhang, Joshua S. Bloom, B. Scott Gaudi +3
Automated inference of binary microlensing events with traditional sampling-based algorithms such as MCMC has been hampered by the slowness of the physical forward model and the pa…
Bayesian Neural Networks
Tom Charnock, Laurence Perreault-Levasseur, François Lanusse
In recent times, neural networks have become a powerful tool for the analysis of complex and abstract data models. However, their introduction intrinsically increases our uncertain…