1 citations · 1 across the 4 of their papers we have counts for
8 papers
Parameter conditioned interpretable U-Net surrogate model for data-driven predictions of convection-diffusion-reaction processes
Michael Urs Lars Kastor, Jan Rottmayer, Anna Hundertmark +1
We present a combined numerical and data-driven workflow for efficient prediction of nonlinear, instationary convection-diffusion-reaction dynamics on a two-dimensional phenotypic…
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…