4 citations · 18 across the 21 of their papers we have counts for
22 papers
Image Generation Techniques for Urban Planning
Katharina Roth, Eva Hagen, Alexander Bartscher +2
In the context of urban planning, architects are normally instructed with creating presentation images that visualize proposed buildings within their urban context. This work aims…
Exploring the Boundaries of Differentiable Radiation Transport and Detector Simulation
Jeffrey Krupa, Yiyang Zhao, Mihaly Novak +9
We present an application of automatic differentiation for particle transport through matter using a Geant4-like radiation transport simulation with a full electromagnetic physics…
On the Codesign of Scientific Experiments and Industrial Systems
Tommaso Dorigo, Pietro Vischia, Shahzaib Abbas +84
The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for in…
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…
Boundary treatment algorithms for meshfree RANS turbulence modeling
Mohan Padmanabha, Jörg Kuhnert, Nicolas R. Gauger +1
In this paper, we propose improved wall-treatment strategies for meshfree methods applied to turbulent flows. The goal is to enhance wall-function handling in simulations of high-R…
Differentiable Surrogate for Detector Simulation and Design with Diffusion Models
Xuan Tung Nguyen, Long Chen, Tommaso Dorigo +7
In this work, we present a conditional denoising-diffusion surrogate for electromagnetic calorimeter showers that is trained to generate high-fidelity energy-deposition maps condit…
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…
SFFT-Based Homogenization: Using Tensor Trains to Enhance FFT-Based Homogenization
Sascha H. Hauck, Matthias Kabel, Mazen Ali +1
Homogenization is a fundamental technique for estimating the macroscopic properties of materials with microscale heterogeneity. Among Homogenization methods, the FFT-based Homogeni…
Efficient Forward-Mode Algorithmic Derivatives of Geant4
Max Aehle, Xuan Tung Nguyen, Mihály Novák +5
We have applied an operator-overloading forward-mode algorithmic differentiation tool to the Monte-Carlo particle simulation toolkit Geant4. Our differentiated version of Geant4 al…
Exploring End-to-end Differentiable Neural Charged Particle Tracking -- A Loss Landscape Perspective
Tobias Kortus, Ralf Keidel, Nicolas R. Gauger
Measurement and analysis of high energetic particles for scientific, medical or industrial applications is a complex procedure, requiring the design of sophisticated detector and d…
Optimization Using Pathwise Algorithmic Derivatives of Electromagnetic Shower Simulations
Max Aehle, Mihály Novák, Vassil Vassilev +4
Among the well-known methods to approximate derivatives of expectancies computed by Monte-Carlo simulations, averages of pathwise derivatives are often the easiest one to apply. Co…
Local Adjoints for Simultaneous Preaccumulations with Shared Inputs
Johannes Blühdorn, Nicolas R. Gauger
In shared-memory parallel automatic differentiation, inputs that are shared among simultaneous thread-local preaccumulations lead to data races if Jacobians are accumulated with a…
Hybrid parallel discrete adjoints in SU2
Johannes Blühdorn, Pedro Gomes, Max Aehle +1
The open-source multiphysics suite SU2 features discrete adjoints by means of operator overloading automatic differentiation (AD). While both primal and discrete adjoint solvers su…
Data-driven aerodynamic shape design with distributionally robust optimization approaches
Long Chen, Jan Rottmayer, Lisa Kusch +2
We formulate and solve data-driven aerodynamic shape design problems with distributionally robust optimization (DRO) approaches. Building on the findings of the work \cite{gotoh201…
Toward the End-To-End Optimization of the SWGO Array Layout
Tommaso Dorigo, Max Aehle, Cornelia Arcaro +13
In this document we consider the problem of finding the optimal layout for the array of water Cherenkov detectors proposed by the SWGO collaboration to study very-high-energy gamma…
Progress in End-to-End Optimization of Detectors for Fundamental Physics with Differentiable Programming
Max Aehle, Lorenzo Arsini, R. Belén Barreiro +27
In this article we examine recent developments in the research area concerning the creation of end-to-end models for the complete optimization of measuring instruments. The models…