Publikationsliste des Fachgebietes Theoretische Physik 2

Anzahl der Treffer: 224
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Hannappel, Thomas; Shekarabi, Sahar; Jaegermann, Wolfram; Runge, Erich; Hofmann, Jan Philipp; Krol, Roel van de; May, Matthias M.; Paszuk, Agnieszka; Hess, Franziska; Bergmann, Arno; Bund, Andreas; Cierpka, Christian; Dreßler, Christian; Dionigi, Fabio; Friedrich, Dennis; Favaro, Marco; Krischok, Stefan; Kurniawan, Mario; Lüdge, Kathy; Lei, Yong; Roldán Cuenya, Beatriz; Schaaf, Peter; Schmidt-Grund, Rüdiger; Schmidt, W. Gero; Strasser, Peter; Unger, Eva; Montoya, Manuel Vasquez; Wang, Dong; Zhang, Hongbin
Integration of multijunction absorbers and catalysts for efficient solar-driven artificial leaf structures: a physical and materials science perspective. - In: Solar RRL, ISSN 2367-198X, Bd. 8 (2024), 11, 2301047, S. 1-49

Artificial leaves could be the breakthrough technology to overcome the limitations of storage and mobility through the synthesis of chemical fuels from sunlight, which will be an essential component of a sustainable future energy system. However, the realization of efficient solar-driven artificial leaf structures requires integrated specialized materials such as semiconductor absorbers, catalysts, interfacial passivation, and contact layers. To date, no competitive system has emerged due to a lack of scientific understanding, knowledge-based design rules, and scalable engineering strategies. Here, we will discuss competitive artificial leaf devices for water splitting, focusing on multi-absorber structures to achieve solar-to-hydrogen conversion efficiencies exceeding 15%. A key challenge is integrating photovoltaic and electrochemical functionalities in a single device. Additionally, optimal electrocatalysts for intermittent operation at photocurrent densities of 10-20 mA cm^-2 must be immobilized on the absorbers with specifically designed interfacial passivation and contact layers, so-called buried junctions. This minimizes voltage and current losses and prevents corrosive side reactions. Key challenges include understanding elementary steps, identifying suitable materials, and developing synthesis and processing techniques for all integrated components. This is crucial for efficient, robust, and scalable devices. Here, we discuss and report on corresponding research efforts to produce green hydrogen with unassisted solar-driven (photo-)electrochemical devices. This article is protected by copyright. All rights reserved.
Jaurigue, Lina; Lüdge, Kathy
Reducing reservoir computer hyperparameter dependence by external timescale tailoring. - In: Neuromorphic computing and engineering, ISSN 2634-4386, Bd. 4 (2024), 1, 014001, S. 1-16

Task specific hyperparameter tuning in reservoir computing is an open issue, and is of particular relevance for hardware implemented reservoirs. We investigate the influence of directly including externally controllable task specific timescales on the performance and hyperparameter sensitivity of reservoir computing approaches. We show that the need for hyperparameter optimisation can be reduced if timescales of the reservoir are tailored to the specific task. Our results are mainly relevant for temporal tasks requiring memory of past inputs, for example chaotic timeseries prediction. We consider various methods of including task specific timescales in the reservoir computing approach and demonstrate the universality of our message by looking at both time-multiplexed and spatially-multiplexed reservoir computing.
Čindrak, Saud; Donvil, Brecht; Lüdge, Kathy; Jaurigue, Lina
Enhancing the performance of quantum reservoir computing and solving the time-complexity problem by artificial memory restriction. - In: Physical review research, ISSN 2643-1564, Bd. 6 (2024), 1, 013051, S. 013051-1-013051-11

We propose a scheme that can enhance the performance and reduce the computational cost of quantum reservoir computing. Quantum reservoir computing is a computing approach which aims at utilizing the complexity and high dimensionality of small quantum systems, together with the fast trainability of reservoir computing, in order to solve complex tasks. The suitability of quantum reservoir computing for solving temporal tasks is hindered by the collapse of the quantum system when measurements are made. This leads to the erasure of the memory of the reservoir. Hence, for every output, the entire input signal is needed to reinitialize the reservoir, leading to quadratic time complexity. Another critical issue for the hardware implementation of quantum reservoir computing is the need for an experimentally accessible means of tuning the nonlinearity of the quantum reservoir. We present an approach which addresses both of these issues. We propose artificially restricting the memory of the quantum reservoir by only using a small number inputs to reinitialize the reservoir after measurements are performed. This strongly influences the nonlinearity of the reservoir response due to the influence of the initial reservoir state, while also substantially reducing the number of quantum operations needed to perform time-series prediction tasks due to the linear rather than quadratic time complexity. The reinitialization length therefore provides an experimental accessible means of tuning the nonlinearity of the response of the reservoir, which can lead to significant task-specific performance improvement. We numerically study the linear and quadratic algorithms for a fully connected transverse Ising model and a quantum processor model.
Köster, Felix; Yanchuk, Serhiy; Lüdge, Kathy
Master memory function for delay-based reservoir computers with single-variable dynamics. - In: IEEE transactions on neural networks and learning systems, ISSN 2162-2388, Bd. 35 (2024), 6, S. 7712-7725

We show that many delay-based reservoir computers considered in the literature can be characterized by a universal master memory function (MMF). Once computed for two independent parameters, this function provides linear memory capacity for any delay-based single-variable reservoir with small inputs. Moreover, we propose an analytical description of the MMF that enables its efficient and fast computation. Our approach can be applied not only to single-variable delay-based reservoirs governed by known dynamical rules, such as the Mackey-Glass or Stuart-Landau-like systems, but also to reservoirs whose dynamical model is not available.
Lüdge, Kathy;
Photonic reservoir computing for energy efficient and versatile machine learning application. - In: Proceedings of the Royal Society of Victoria, Bd. 135 (2023), 2, S. 38-40

Time-multiplexed reservoir computing is a machine learning concept which can be realised in photonic hardware systems using only one physical node. The concept can be used for various problems, ranging from classification problems to time-series prediction tasks, while being fast and energy efficient. Here, a theoretical analysis of a reservoir computer realised via delay-coupled semiconductor lasers is presented and the role of the internal system time-scales and the bifurcation structure is discussed. It is further shown that optimal performance can be reached by tailoring the coupling delays to the specific memory requirements of the given task.
Mühlnickel, Lukas; Jaurigue, Lina; Lüdge, Kathy
Delay-based reservoir computing with spin-VCSELs: interplay between internal dynamics and performance. - In: 2023 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC), (2023), insges. 1 S.

Machine learning setups that are able to process data in the optical domain are ideal for on -chip hardware implementations [1]. Due to the fact that the training of hardware based solutions is complicated, a delay-based reservoir computing (RC) realization, where only the output weights need to be trained via linear regression, is very promising [2]. In this paper we investigate vertical cavity surface emitting laser with two mode emission (spin-VCSEL) as the nonlinear node for a delay-based RC setup. These lasers have the ability to exibit reprodicible and high speed dynamics [3] and are thus ideal candidates to increase the data injection rates which are limited by the clocktime [4], [5]. The focus of our numerical investigations is on the interplay between the internal charge carrier dynamics of the spin-VCSEL and its performance when operated in a delay-based RC setup with optically-injected phase-modulated data injection.
Mijalkov, Mite; Gerboles, Blanca Zufiria; Vereb, Daniel; Lüdge, Kathy; Brunner, Daniel; Volpe, Giovanni; Pereira, Joana B.
Uncovering vulnerable connections in the aging brain using reservoir computing. - In: Emerging Topics in Artificial Intelligence (ETAI) 2023, (2023), PC1265508

We used reservoir computing to explore the changes in the connectivity patterns of whole-brain anatomical networks derived by diffusion-weighted imaging, and their impact on cognition during aging. The networks showed optimal performance at small densities. This performance decreased with increasing density, with the rate of decrease being strongly associated with age and performance on behavioural tasks measuring cognitive function. This suggests that a network core of anatomical hubs is crucial for optimal functioning, while weaker connections are more susceptible to aging effects. This study highlights the potential utility of reservoir computing in understanding age-related changes in cognitive function.
Thurn, Andreas; Bissinger, Jochen; Meinecke, Stefan; Schmiedeke, Paul; Oh, Sang Soon; Chow, Weng W.; Lüdge, Kathy; Koblmüller, Gregor; Finley, Jonathan
Self-induced ultrafast electron-hole-plasma temperature oscillations in nanowire lasers. - In: Physical review applied, ISSN 2331-7019, Bd. 20 (2023), 3, S. 034045-1-034045-12

Nanowire lasers can be monolithically and site-selectively integrated onto silicon photonic circuits. To assess their full potential for ultrafast optoelectronic devices, a detailed understanding of their lasing dynamics is crucial. However, the roles played by their resonator geometry and the microscopic processes that mediate energy exchange between the photonic, electronic, and phononic subsystems are largely unexplored. Here, we study the dynamics of GaAs-AlGaAs core-shell nanowire lasers at cryogenic temperatures using a combined experimental and theoretical approach. Our results indicate that these NW lasers exhibit sustained intensity oscillations with frequencies ranging from 160GHz to 260GHz. As the underlying physical mechanism, we have identified self-induced electron-hole plasma temperature oscillations resulting from a dynamic competition between photoinduced carrier heating and cooling via phonon scattering. These dynamics are intimately linked to the strong interaction between the lasing mode and the gain material, which arises from the wavelength-scale dimensions of these lasers. We anticipate that our results could lead to optimised approaches for ultrafast intensity and phase modulation of chip-integrated semiconductor lasers at the nanoscale.
Köster, Felix; Patel, Dhruvit; Wikner, Alexander; Jaurigue, Lina; Lüdge, Kathy
Data-informed reservoir computing for efficient time-series prediction. - In: Chaos, ISSN 1089-7682, Bd. 33 (2023), 7, 073109, S. 073109-1-073109-11

We propose a new approach to dynamical system forecasting called data-informed-reservoir computing (DI-RC) that, while solely being based on data, yields increased accuracy, reduced computational cost, and mitigates tedious hyper-parameter optimization of the reservoir computer (RC). Our DI-RC approach is based on the recently proposed hybrid setup where a knowledge-based model is combined with a machine learning prediction system, but it replaces the knowledge-based component by a data-driven model discovery technique. As a result, our approach can be chosen when a suitable knowledge-based model is not available. We demonstrate our approach using a delay-based RC as the machine learning component in conjunction with sparse identification of nonlinear dynamical systems for the data-driven model component. We test the performance on two example systems: the Lorenz system and the Kuramoto-Sivashinsky system. Our results indicate that our proposed technique can yield an improvement in the time-series forecasting capabilities compared with both approaches applied individually, while remaining computationally cheap. The benefit of our proposed approach, compared with pure RC, is most pronounced when the reservoir parameters are not optimized, thereby reducing the need for hyperparameter optimization.
Meinecke, Stefan; Köster, Felix; Christiansen, Dominik; Lüdge, Kathy; Knorr, Andreas; Selig, Malte
Data-driven forecasting of nonequilibrium solid-state dynamics. - In: Physical review, ISSN 2469-9969, Bd. 107 (2023), 18, 184306, S. 184306-1-184306-18

We present a data-driven approach to efficiently approximate nonlinear transient dynamics in solid-state systems. Our proposed machine-learning model combines a dimensionality reduction stage with a nonlinear vector autoregression scheme. We report an outstanding time-series forecasting performance combined with an easy-to-deploy model and an inexpensive training routine. Our results are of great relevance as they have the potential to massively accelerate multiphysics simulation software and thereby guide the future development of solid-state-based technologies.