4 citations · 18 across the 21 of their papers we have counts for
7 papers · 1 filter
Performance Benchmarking of Tensor Trains for accelerated Quantum-Inspired Homogenization on TPU, GPU and CPU architectures
Sascha H. Hauck, Matthias Kabel, Nicolas R. Gauger
Recent advances in high-resolution CT-imaging technology are creating a new class of ultra-high resolved microstructural datasets that challenge the limits of traditional homogeniz…
Adjoint-Based Aerodynamic Shape Optimization with a Manifold Constraint Learned by Diffusion Models
Long Chen, Emre Oezkaya, Jan Rottmayer +3
We introduce an adjoint-based aerodynamic shape optimization framework that integrates a diffusion model trained on existing designs to learn a smooth manifold of aerodynamically v…
End-to-End Detector Optimization with Diffusion models: A Case Study in Sampling Calorimeters
Kylian Schmidt, Nikhil Kota, Jan Kieseler +16
Recent advances in machine learning have opened new avenues for optimizing detector designs in high-energy physics, where the complex interplay of geometry, materials, and physics…
Neuromorphic Readout for Hadron Calorimeters
Enrico Lupi, Abhishek, Max Aehle +17
We simulate hadrons impinging on a homogeneous lead-tungstate (PbWO4) calorimeter to investigate how the resulting light yield and its temporal structure, as detected by an array o…
Hadron Identification Prospects With Granular Calorimeters
Andrea De Vita, Abhishek, Max Aehle +15
In this work we consider the problem of determining the identity of hadrons at high energies based on the topology of their energy depositions in dense matter, along with the time…
On the Utility Function of Experiments in Fundamental Science
Tommaso Dorigo, Michele Doro, Max Aehle +7
The majority of experiments in fundamental science today are designed to be multi-purpose: their aim is not simply to measure a single physical quantity or process, but rather to e…
Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning
Tobias Kortus, Ralf Keidel, Nicolas R. Gauger +1
Reinforcement learning demonstrated immense success in modelling complex physics-driven systems, providing end-to-end trainable solutions by interacting with a simulated or real en…