Nicolas R. Gauger

Professor

TU Kaiserslautern

Germany

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Professor Nicolas R. Gauger is a faculty member at TU Kaiserslautern in Germany. His research areas encompass artificial intelligence, automatic differentiation, and detector optimization, with recent publications addressing topics such as hadron identification and neuromorphic readout technologies. Professor Gauger's work integrates advanced computational techniques and machine learning applications in various scientific domains.

Articles (16)

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2025

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2025

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2025

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2025

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2024

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2023

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2023

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2022

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2022

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2021

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2021

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2021

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2021

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2020

Collaborators (5)

Steffen Wendzel

Privatdozent (Lecturer)

FernUniversität in Hagen

GERMANY

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Joseph Ciarrochi

Research Professor

Australian Catholic University

AUSTRALIA

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Alexander Wiebel

Professor

GERMANY

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Rauno Cavallaro

Professor Visitante

Universidad Carlos III de Madrid

SPAIN

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Steven Hayes

Foundation Professor

University of Nevada Reno

UNITED STATES

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Nicolas R. Gauger

Research Interests

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Articles (16)

Artificial Intelligence in Science and Society: The Vision of USERN

Forward-Mode Automatic Differentiation of Compiled Programs

Hadron Identification Prospects with Granular Calorimeters

Neuromorphic Readout for Hadron Calorimeters

End-to-End Detector Optimization with Diffusion Models: A Case Study in Sampling Calorimeters

Hybrid parallel discrete adjoints in SU2

Nonlinear Surrogate Model Design for Aerodynamic Dataset Generation Based on Artificial Neural Networks

Event-Based Automatic Differentiation of OpenMP with OpDiLib

Towards Neural Charged Particle Tracking in Digital Tracking Calorimeters With Reinforcement Learning

AutoMat: automatic differentiation for generalized standard materials on GPUs

Discrete adjoint methodology for general multiphysics problems: A modular and efficient algorithmic outline with implementation in an open-source simulation software

Investigating particle track topology for range telescopes in particle radiography using convolutional neural networks

Aerostructural wing shape optimization assisted by algorithmic differentiation

Efficient airframe noise reduction framework via adjoint-based shape optimization

Regularization for adjoint-based unsteady aerodynamic optimization using windowing techniques

Efficient design optimization of acoustic liners for engine noise reduction

Collaborators (5)

Steffen Wendzel

Joseph Ciarrochi

Alexander Wiebel

Rauno Cavallaro

Steven Hayes

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