Dr. Benjamin Jurgelucks

Dr. Jurgelucks entwickelt Optimierungsmethoden zur Charakterisierung piezoelektrischer Materialien, insbesondere zur Bestimmung vollständiger Materialparameter aus elektrischen Messdaten. Sein aktueller Fokus liegt auf der Kombination ableitungsbasierter Optimierung mit maschinellem Lernen, um inverse Probleme robuster und effizienter zu lösen. Dies ermöglicht es Unternehmen, piezoelektrische Sensoren und Ultraschallwandler schneller und präziser zu entwickeln und zu validieren — ein kritischer Schritt in der computergestützten Produktentwicklung. Die Methoden sind relevant für Hersteller von Ultraschalltechnik, zerstörungsfreier Prüfung, medizinischen Sensoren und intelligenten Materialien.

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• FOR 5208/1: Angepasste ableitungsbasierte Optimierung zur Charakterisierung des thermopiezoelektrischen Materialverhaltens (TP OPT)

  Quelle ↗

  Förderer: DFG Forschungsgruppe Zeitraum: 06/2024 - 09/2026 Projektleitung: Prof. Dr. Andrea Walther, Dr. Benjamin Jurgelucks

• Autonomous navigation of ships by combining optimal trajectory planning with informed graph search

  2022

  Mathematical and Computer Modelling of Dynamical Systems · DOI

  Autonomous trajectory generation plays an essential role in the navigation of vehicles in space as well as in terrestrial scenarios, i.e. in the air, on solid ground, or water. For the latter, the navigation of ships in ports has specific challenges since ship dynamics are highly nonlinear with limited agility, while the manoeuvre space in ports is limited. Nevertheless, for providing support to humanly designed control strategies, autonomously generated trajectories have not only to be feasible, i.e. collision-free but shall also be optimal with respect to manoeuvre time and control effort. This article presents a novel approach to autonomous trajectory planning on the basis of precomputed and connectable trajectory segments, the so-called motion primitives, and an A*-search algorithm. Sequences of motion primitives provide an initial guess for a subsequent optimization by which optimal trajectories are found even in terrains with many obstacles. We illustrate the approach with different navigation scenarios.

• A usability case study of algorithmic differentiation tools on the ISSM ice sheet model

  2017

  Optimization methods & software · DOI

  Algorithmic differentiation (AD) based on operator overloading is often the only feasible approach for applying AD in complex C++ software environments. Challenges pertaining to the introduction of an AD tool based on operator overloading have been studied in the past. However, in order to assess possible performance gains or to verify derivative values, it is advantageous to be able to apply more than one AD tool to a given code. Hence, in this work, we investigate usability issues when exchanging AD tools. Our study is based on the NASA/JPL/UCI Ice Sheet System Model (ISSM) which currently employs the AD tool ADOL-C. We introduce CoDiPack to ISSM, a more recent AD tool offering a similar set of features while promising performance improvements. In addition to the obvious type change for the AD-augmented float type, this transition requires the change to a different adjoint MPI library, adaptation of the MUMPS solver wrapper, and changes to the derivative seeding and extraction routines. We believe that these issues are fairly generic for numerical simulation software, and the issues we report on provide a blueprint for similar undertakings. We also believe that our experiences provide guidance towards the development of AD interfaces that support AD tool interoperability. In addition, we improve upon the memory management of the existing ADOL-C instrumentation, which exhibited considerable runtime problems for higher mesh resolutions. We conduct serial and parallel ISSM model runs on a 2D mass transport benchmark as well as a model of the Pine Island Glacier to verify the derivatives computed by both tools and report on runtime performance and memory usage. In comparison, the CoDiPack AD variant of ISSM runs faster with less memory overhead than the ADOL-C variant and, thus, enables future model runs with an increased number of mesh elements. But the existence of two different AD implementations provides added confidence in the correctness of derivatives, in particular for future AD tool versions.