On-the-fly adaptivity for nonlinear twoscale simulations using artificial neural networks and reduced order modeling

A multi-fidelity surrogate model for highly nonlinear multiscale problems is proposed. It is based on the introduction of two different surrogate models and an adaptive on-the-fly switching. The two concurrent surrogates are built incrementally starting from a moderate set of evaluations of the full order model. Therefore, a reduced order model (ROM) is generated. Using a hybrid ROM-preconditioned FE solver, additional effective stress-strain data is simulated while the number of samples is kept to a moderate level by using a dedicated and physics-guided sampling technique. Machine learning (ML) is subsequently used to build the second surrogate by means of artificial neural networks (ANN). Different ANN architectures are explored and the features used as inputs of the ANN are fine tuned in order to improve the overall quality of the ML model. Additional ANN surrogates for the stress errors are generated. Therefore, conservative design guidelines for error surrogates are presented by adapting the loss functions of the ANN training in pure regression or pure classification settings. The error surrogates can be used as quality indicators in order to adaptively select the appropriate -- i.e. efficient yet accurate -- surrogate. Two strategies for the on-the-fly switching are investigated and a practicable and robust algorithm is proposed that eliminates relevant technical difficulties attributed to model switching. The provided algorithms and ANN design guidelines can easily be adopted for different problem settings and, thereby, they enable generalization of the used machine learning techniques for a wide range of applications. The resulting hybrid surrogate is employed in challenging multilevel FE simulations for a three-phase composite with pseudo-plastic micro-constituents. Numerical examples highlight the performance of the proposed approach

A modelling approach to the evalution of computer system performance

Imperial Users onl

Identification and adaptive control of a high-contrast focal plane wavefront correction system

All coronagraphic instruments for exoplanet high-contrast imaging need wavefront correction systems to reject optical aberrations and create sufficiently dark holes. Since the most efficient wavefront correction algorithms (controllers and estimators) are usually model-based, the modeling accuracy of the system influences the ultimate wavefront correction performance. Currently, wavefront correction systems are typically approximated as linear systems using Fourier optics. However, the Fourier optics model is usually biased due to inaccuracies in the layout measurements, the imperfect diagnoses of inherent optical aberrations, and a lack of knowledge of the deformable mirrors (actuator gains and influence functions). Moreover, the telescope optical system varies over time because of instrument instabilities and environmental effects. In this paper, we present an expectation-maximization (E-M) approach for identifying and real-time adapting the linear telescope model from data. By iterating between the E-step (a Kalman filter and a Rauch smoother) and the M-step (analytical or gradient-based optimization), the algorithm is able to recover the system even if the model depends on the electric fields, which are unmeasurable hidden variables. Simulations and experiments in Princeton's High Contrast Imaging Lab demonstrate that this algorithm improves the model accuracy and increases the efficiency and speed of the wavefront correction

A machine learning approach for efficient uncertainty quantification using multiscale methods

Several multiscale methods account for sub-grid scale features using coarse scale basis functions. For example, in the Multiscale Finite Volume method the coarse scale basis functions are obtained by solving a set of local problems over dual-grid cells. We introduce a data-driven approach for the estimation of these coarse scale basis functions. Specifically, we employ a neural network predictor fitted using a set of solution samples from which it learns to generate subsequent basis functions at a lower computational cost than solving the local problems. The computational advantage of this approach is realized for uncertainty quantification tasks where a large number of realizations has to be evaluated. We attribute the ability to learn these basis functions to the modularity of the local problems and the redundancy of the permeability patches between samples. The proposed method is evaluated on elliptic problems yielding very promising results.Comment: Journal of Computational Physics (2017

Predictive macroscopic modeling of Chinese hamster ovary cells in fed-batch processes

This thesis focuses on developing a systematic modeling method that can capture the essential features for prediction of cell metabolism, growth and monoclonal antibody (mAb) production in Chinese Hamster Ovary (CHO) cells. In a first step all specific consumption rates are calculated based on time courses of extracellular metabolites, viable cell density and mAb. Then the metabolic phases within which the metabolic pseudo-steady state approximation is verified are identified. In a third step, all metabolic rates are expressed as a function of the specific growth rate within each metabolic phase. We have applied this method to a set of small bioreactor data and have shown that the model obtained can predict specific conversion rates both small and also at large scale. In the second part of this thesis, a kinetic model of the cell growth has been developed. Together with previously described methodology, this kinetic model results in a predictive metabolic model for each experimental cell growth data are not required. The kinetic model is based on Monod kinetics with a few modifications such as a varying the maximum specific growth rate as a function of the integral viable cell density. The full kinetic model can be used off line to design optimal feeding profiles. The results of this thesis demonstrate that rich knowledge can be derived from macroscopic data that can then be used to predict new production conditions in an industrial environment at small and large scale.Der Schwerpunkt dieser Dissertation liegt auf der systematischen Entwicklung Modellen für die Vorhersage des zellulären Stoffwechsels, des Wachstums und der Produktion von monoklonalen Antikörpern (mAb) in Kulturen von Chinesischen Hamster-Ovarzellen (CHO). Zunächst wurden mit segmentierter linearer Regression metabolischer Phasen identifiziert. Diese Identifizierung beruht auf der Annahme eines pseudo-stationären Zustands und somit, dass in einer Phase alle Raten linear miteinander korreliert waren. Die spezifischen Raten wurden aus den Zeitverläufen der Konzentrationen der Metabolite und des mAb sowie der Lebendzellzahl bestimmt. Durch die Korrelation konnten alle Raten über die Wachstumsrate im 2 L und im 2000 L Maßstab berechnet werden. Danach wurde ein kinetisches Modell des Wachstums der Zellen etabliert, was die Vorhersage aller Raten auch in fed-batch Kulturen erlaubt. Die Kinetik basiert auf der Monod-Kinetik modifiziert mit einer variablen maximalen spezifischen Wachstumsrate. Das kinetische Modell erlaubt eine rechnerische Optimierung der Substratzuführung für eine maximale Produktion. Damit wurde gezeigt, dass aus makroskopischen Daten, d.h. ohne intrazelluläre Messungen, wesentliche Informationen erhalten werden können, mit denen neue Experimente in einem industriellen Umfeld vorhergesagt werden können. Diese innovative und systematische Vorgangsweise eröffnet neue Perspektiven für die Reduzierung von Kosten und für eine Beschleunigung der Prozessentwicklung

Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings

Conventional feature-based and model-based gaze estimation methods have proven to perform well in settings with controlled illumination and specialized cameras. In unconstrained real-world settings, however, such methods are surpassed by recent appearance-based methods due to difficulties in modeling factors such as illumination changes and other visual artifacts. We present a novel learning-based method for eye region landmark localization that enables conventional methods to be competitive to latest appearance-based methods. Despite having been trained exclusively on synthetic data, our method exceeds the state of the art for iris localization and eye shape registration on real-world imagery. We then use the detected landmarks as input to iterative model-fitting and lightweight learning-based gaze estimation methods. Our approach outperforms existing model-fitting and appearance-based methods in the context of person-independent and personalized gaze estimation

Novel strategies for process control based on hybrid semi-parametric mathematical systems

Tese de doutoramento. Engenharia Química. Universidade do Porto. Faculdade de Engenharia. 201