79 citations · 136 across the 10 of their papers we have counts for
13 papers
Faithful Uncertainties in ML-Based Inverse Problems: GPs, NNs and NTKs for the Triplet PDF
Nina Elmer, Sven Krippendorf, Maria Ubiali
Machine-learning methods are increasingly used to solve inverse problems in physics, where a faithful estimate of the uncertainty on the reconstructed function is as important as i…
Spectral Reach: Understanding Neural Scaling as Progress into the Spectral Tail
Konstantin Nikolaou, Jonas Scheunemann, Sven Krippendorf +2
Neural scaling laws describe predictable power-law relationships between model size, dataset size, compute, and performance. While these laws guide the development of modern founda…
Open Source Planning & Control System with Language Agents for Autonomous Scientific Discovery
Licong Xu, Milind Sarkar, Anto I. Lonappan +23
We present a multi-agent system for automation of scientific research tasks, cmbagent (https://github.com/CMBAgents/cmbagent). The system is formed by about 30 Large Language Model…
Beyond Scaling Curves: Internal Dynamics of Neural Networks Through the NTK Lens
Konstantin Nikolaou, Sven Krippendorf, Samuel Tovey +1
Scaling laws offer valuable insights into the relationship between neural network performance and computational cost, yet their underlying mechanisms remain poorly understood. In t…
Solving inverse problems of Type IIB flux vacua with conditional generative models
Sven Krippendorf, Zhimei Liu
We address the inverse problem in Type IIB flux compactifications of identifying flux vacua with targeted phenomenological properties such as specific superpotential values or tadp…
Towards a Phenomenological Understanding of Neural Networks: Data
Samuel Tovey, Sven Krippendorf, Konstantin Nikolaou +1
A theory of neural networks (NNs) built upon collective variables would provide scientists with the tools to better understand the learning process at every stage. In this work, we…
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…
A duality connecting neural network and cosmological dynamics
Sven Krippendorf, Michael Spannowsky
We demonstrate that the dynamics of neural networks trained with gradient descent and the dynamics of scalar fields in a flat, vacuum energy dominated Universe are structurally pro…
Revealing systematics in phenomenologically viable flux vacua with reinforcement learning
Sven Krippendorf, Rene Kroepsch, Marc Syvaeri
The organising principles underlying the structure of phenomenologically viable string vacua can be accessed by sampling such vacua. In many cases this is prohibited by the computa…
Improving Simulations with Symmetry Control Neural Networks
Marc Syvaeri, Sven Krippendorf
The dynamics of physical systems is often constrained to lower dimensional sub-spaces due to the presence of conserved quantities. Here we propose a method to learn and exploit suc…
Integrability ex machina
Sven Krippendorf, Dieter Lust, Marc Syvaeri
Determining whether a dynamical system is integrable is generally a difficult task which is currently done on a case by case basis requiring large human input. Here we propose and…
Detecting Symmetries with Neural Networks
Sven Krippendorf, Marc Syvaeri
Identifying symmetries in data sets is generally difficult, but knowledge about them is crucial for efficient data handling. Here we present a method how neural networks can be use…
Constraints on Axion-Like Particles from X-ray Observations of NGC1275
Marcus Berg, Joseph P. Conlon, Francesca Day +4
Axion-like particles (ALPs) can induce localised oscillatory modulations in the spectra of photon sources passing through astrophysical magnetic fields. Ultra-deep Chandra observat…