37 citations · 63 across the 8 of their papers we have counts for
8 papers
Weight-Space Physics: Interpretable Hypernetworks for Lattice Quantum Field Theories
Tobias Göbel, Julian R. Ebelt, Zier Mensch +2
Lattice field theory is the workhorse of non-perturbative physics, used to simulate phenomena from the strong nuclear force to critical phenomena in materials. Its Boltzmann distri…
Analytic Bijections for Smooth and Interpretable Normalizing Flows
Mathis Gerdes, Miranda C. N. Cheng
A key challenge in normalizing flows is finding expressive invertible scalar bijections. Existing approaches face trade-offs: affine transformations are smooth and analytically inv…
GUD: Generation with Unified Diffusion
Mathis Gerdes, Max Welling, Miranda C. N. Cheng
Diffusion generative models transform noise into data by inverting a process that progressively adds noise to data samples. Inspired by concepts from the renormalization group in p…
Non-Perturbative Trivializing Flows for Lattice Gauge Theories
Mathis Gerdes, Pim de Haan, Roberto Bondesan +1
Continuous normalizing flows are known to be highly expressive and flexible, which allows for easier incorporation of large symmetries and makes them a powerful computational tool…
Albatross: A scalable simulation-based inference pipeline for analysing stellar streams in the Milky Way
James Alvey, Mathis Gerdes, Christoph Weniger
Stellar streams are potentially a very sensitive observational probe of galactic astrophysics, as well as the dark matter population in the Milky Way. On the other hand, performing…
CYJAX: A package for Calabi-Yau metrics with JAX
Mathis Gerdes, Sven Krippendorf
We present the first version of CYJAX, a package for machine learning Calabi-Yau metrics using JAX. It is meant to be accessible both as a top-level tool and as a library of modula…
Learning Lattice Quantum Field Theories with Equivariant Continuous Flows
Mathis Gerdes, Pim de Haan, Corrado Rainone +2
We propose a novel machine learning method for sampling from the high-dimensional probability distributions of Lattice Field Theories, which is based on a single neural ODE layer a…
Moduli-dependent Calabi-Yau and SU(3)-structure metrics from Machine Learning
Lara B. Anderson, Mathis Gerdes, James Gray +3
We use machine learning to approximate Calabi-Yau and SU(3)-structure metrics, including for the first time complex structure moduli dependence. Our new methods furthermore improve…