Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions
arXiv:2606.10023v1 Announce Type: cross Abstract: Accurate posterior estimation is central to scientific inference, as uncertainties determine what can be reliably learned from observational data. While Markov chain Monte Carlo methods provide asymptotic convergence guarantees, they are computationally demanding in high-dimensional settings. Neural network-based generative models for entire discretized 3D fields enable fast amortized inference but often lack convergence guarantees and principled accuracy assessment. Using Hamiltonian Monte Carlo to obtain reference posterior samples, we conduc
The increasing computational power and development of advanced AI models make high-dimensional scientific inference tractable, while the need for faster, more reliable methods is growing.
This development proposes a method for more accurate and computationally efficient inference in complex scientific fields, potentially accelerating discovery and understanding of fundamental phenomena.
The ability to reliably use neural generative models for complex scientific data analysis will move from theoretical promise to practical application, reducing reliance on computationally intensive traditional methods.
- · Astrophysicists and Cosmologists
- · AI researchers in scientific computing
- · High-performance computing providers
- · Scientific instrument developers
- · Traditional statistical modeling software vendors
- · Researchers solely reliant on MCMC for high-dimensional problems
Faster and more accurate analysis of cosmic initial conditions and other high-dimensional scientific data.
Accelerated discovery of new physical laws or insights into the universe's origins due to improved data interpretation.
The methodology could generalize to other complex scientific domains, speeding up research in fields like materials science or drug discovery.
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Read at arXiv cs.LG