Analyzing and controlling large nanosystems with physics-trained neural networks

In dieser Arbeit wird untersucht, wie Neuronale Netze genutzt werden können, um die Auswertung von Experimenten durch Minimierung des Simulationsaufwandes beschleunigen zu können. Für die Rekonstruktion von Silber-Nanoclustern aus Einzelschuss-Weitwinkel-Streubildern können diese bereits aus kleinen Datenätzen allgemeine Rekonstruktionsregeln ableiten und ermöglichen durch direktes Training auf der Streuphysik unerreichte Detailtiefen. Für Giant-Dipole-Zustände von Rydbergexzitonen in Kupferoxydul wird mittels Deep Reinforcement Learning ein Anregungsschema aus Simulationen hergeleitet.This thesis investigates the possible application of neural networks in accelerating the evaluation of physical experiments while minimizing the required simulation effort. Neural networks are capable of inferring universal reconstruction rules for reconstructing silver nanoclusters from single wide-angle scattering patterns from a small set of simulated data and when trained directly on scattering theory reaching unmatched accuracy. A dynamic excitation for giant dipole states of Rydberg excitons in cuprous oxide is derived through deep reinforcement learning interacting and simulation data

MULTIDISCIPLINARY TECHNIQUES FOR THE SIMULATION OF THE CONTACT BETWEEN THE FOOT AND THE SHOE UPPER IN GAIT: VIRTUAL REALITY, COMPUTATIONAL BIOMECHANICS, AND ARTIFICIAL NEURAL NETWORKS

Esta Tesis propone el uso de técnicas multidisciplinares como una alternativa viable a los procedimientos actuales de evaluación del calzado los cuales, normalmente, consumen muchos recursos humanos y técnicos. Estas técnicas son Realidad Virtual, Biomecánica Computacional y Redes Neuronales Artificiales. El marco de esta tesis es el análisis virtual del confort mecánico en el calzado, es decir, el análisis de las presiones de confort en el calzado y su principal objetivo es predecir las presiones ejercidas por el zapato sobre la superficie del pie al caminar mediante la simulación del contacto en esta interfaz. En particular, en esta tesis se ha desarrollado una aplicación software que usa el Método de los Elementos Finitos para simular la deformación del calzado. Se ha desarrollado un modelo preliminar que describe el comportamiento del corte del calzado, se ha implementado un proceso automático para el ajuste pie-zapato y se ha presentado una metodología para obtener una animación genérica del paso de cada individuo. Además, y con el fin de mejorar la aplicación desarrollada, se han propuesto nuevos modelos para simular el comportamiento del corte del calzado al caminar. Por otro lado, las Redes Neuronales Artificiales han sido aplicadas en esta tesis a la predicción de la fuerza ejercida por una esfera, que simulando un hueso, empuja a una muestra de material. Además, también han sido utilizadas para predecir las presiones ejercidas por el corte del calzado sobre la superficie del pie (presiones dorsales) en un paso completo. Las principales contribuciones de esta tesis son: el desarrollo de un innovador simulador que permitirá a los fabricantes de calzado realizar evaluaciones virtuales de las características de sus diseños sin tener que construir el prototipo real, y el desarrollo de una también innovadora herramienta que les permitirá predecir las presiones dorsales ejercidas por el calzado sobre la superficie del pie al caminar.Rupérez Moreno, MJ. (2011). MULTIDISCIPLINARY TECHNIQUES FOR THE SIMULATION OF THE CONTACT BETWEEN THE FOOT AND THE SHOE UPPER IN GAIT: VIRTUAL REALITY, COMPUTATIONAL BIOMECHANICS, AND ARTIFICIAL NEURAL NETWORKS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/11235Palanci

Intrinsically Evolvable Artificial Neural Networks

Dedicated hardware implementations of neural networks promise to provide faster, lower power operation when compared to software implementations executing on processors. Unfortunately, most custom hardware implementations do not support intrinsic training of these networks on-chip. The training is typically done using offline software simulations and the obtained network is synthesized and targeted to the hardware offline. The FPGA design presented here facilitates on-chip intrinsic training of artificial neural networks. Block-based neural networks (BbNN), the type of artificial neural networks implemented here, are grid-based networks neuron blocks. These networks are trained using genetic algorithms to simultaneously optimize the network structure and the internal synaptic parameters. The design supports online structure and parameter updates, and is an intrinsically evolvable BbNN platform supporting functional-level hardware evolution. Functional-level evolvable hardware (EHW) uses evolutionary algorithms to evolve interconnections and internal parameters of functional modules in reconfigurable computing systems such as FPGAs. Functional modules can be any hardware modules such as multipliers, adders, and trigonometric functions. In the implementation presented, the functional module is a neuron block. The designed platform is suitable for applications in dynamic environments, and can be adapted and retrained online. The online training capability has been demonstrated using a case study. A performance characterization model for RC implementations of BbNNs has also been presented

Machine Learning-Based Event Generator

Monte Carlo-based event generators have been the primary source for simulating particle collision experiments for the study of interesting physics scenarios. Monte Carlo generators rely on theoretical assumptions, which limit their ability to capture the full range of possible correlations between particle’s momenta. In addition, the simulations of the complete pipeline often take minutes to generate a single event even with the help of supercomputers. In recent years, much attention has been devoted to the development of machine learning event generators. They demonstrate attractive advantages, including fast simulations, data compression, and being agnostic of theoretical assumptions. However, most of the efforts ignore faithful reproductions, and detector effects due to their complexity and rely on theories for detector simulations. In this work, we present a new machine learning-based event generator framework free of theoretical particle dynamics assumption. We first create a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN) that selects a set of transformed features to faithfully reproduce simulated and experimental data. Then, we extend FATGAN by conditioning the component neural networks according to the given reaction energy and develop a Conditional FAT-GAN (cFAT-GAN) that can generate events at unrelated beam energies. Next, we implement a conditional folding model that learns the correlations between vertex-level and detector-level events and simulates the distortions produced by the detector machines. The folding model is then integrated into a generator to reconstruct vertex-level events. This serves as a practical framework in a real experimental analysis where such effects must be incorporated. We finally evaluate different Neural Network architectures and use machine learning techniques for model interpretation and evaluation. In addition, we analyze the GANs latent variables to extract physics resonance regions, illustrating the ability of the developed model to distinguish between the underlying physics mechanisms. This framework has been validated on simulated inclusive deep-inelastic scattering data along with the existing parametrizations for detector simulation. The generated results provide a realistic proof of concept for designing a machine learning-based event generator that will be a valuable tool in nuclear and particle physics programs to facilitate the studies of high-energy scattering reactions and understand different physical mechanisms

Physics-Informed Deep Neural Operator Networks

Standard neural networks can approximate general nonlinear operators, represented either explicitly by a combination of mathematical operators, e.g., in an advection-diffusion-reaction partial differential equation, or simply as a black box, e.g., a system-of-systems. The first neural operator was the Deep Operator Network (DeepONet), proposed in 2019 based on rigorous approximation theory. Since then, a few other less general operators have been published, e.g., based on graph neural networks or Fourier transforms. For black box systems, training of neural operators is data-driven only but if the governing equations are known they can be incorporated into the loss function during training to develop physics-informed neural operators. Neural operators can be used as surrogates in design problems, uncertainty quantification, autonomous systems, and almost in any application requiring real-time inference. Moreover, independently pre-trained DeepONets can be used as components of a complex multi-physics system by coupling them together with relatively light training. Here, we present a review of DeepONet, the Fourier neural operator, and the graph neural operator, as well as appropriate extensions with feature expansions, and highlight their usefulness in diverse applications in computational mechanics, including porous media, fluid mechanics, and solid mechanics.Comment: 33 pages, 14 figures. arXiv admin note: text overlap with arXiv:2204.00997 by other author

Performance based diagnostics of a twin shaft aeroderivative gas turbine: water wash scheduling

Aeroderivative gas turbines are used all over the world for different applications as Combined Heat and Power (CHP), Oil and Gas, ship propulsion and others. They combine flexibility with high efficiencies, low weight and small footprint, making them attractive where power density is paramount as off shore Oil and Gas or ship propulsion. In Western Europe they are widely used in CHP small and medium applications thanks to their maintainability and efficiency. Reliability, Availability and Performance are key parameters when considering plant operation and maintenance. The accurate diagnose of Performance is fundamental for the plant economics and maintenance planning. There has been a lot of work around units like the LM2500® , a gas generator with an aerodynamically coupled gas turbine, but nothing has been found by the author for the LM6000® . Water wash, both on line or off line, is an important maintenance practice impacting Reliability, Availability and Performance. This Thesis aims to select and apply a suitable diagnostic technique to help establishing the schedule for off line water wash on a specific model of this engine type. After a revision of Diagnostic Methods Artificial Neural Network (ANN) has been chosen as diagnostic tool. There was no WebEngine model available of the unit under study so the first step of setting the tool has been creating it. The last step has been testing of ANN as a suitable diagnostic tool. Several have been configured, trained and tested and one has been chosen based on its slightly better response. Finally, conclusions are discussed and recommendations for further work laid out

Implementation of 2D turn-based strategy game with AI

Práce se zabývá tvorbou strategické hry a umělé inteligence, která se ji naučíhrát. Je prozkoumán žánr strategických her, ale i důležité části pro umělouinteligenci. Dále práce analyzuje několik profesionálně vytvořených her a pro-gramů hrající tahové strategie. Závěrem hodnotí kvalitu jak implementovanéumělé inteligence, tak implementované hry a navrhuje možná zlepšení.The thesis deals with creating a strategy game and artificial intelligence learn-ing the game via self-play. It looks into a strategy game genre as well as im-portant components of artificial intelligence. It analyzes several examples ofreal-world games and programs playing strategy games. In the end, the thesisdiscusses the quality of implemented artificial intelligence but also the gameand suggests possible improvements

Machine Learning for Multi-Layer Open and Disaggregated Optical Networks

L'abstract è presente nell'allegato / the abstract is in the attachmen