Projection-based hyper-reduced order modeling of stress and reaction fields, and application of static condensation for multibody problems

Computational Mechanics' problems are often solved using the Finite Element Method (FEM). The resulting systems of equations may lead to large data and therefore, the solution requires high memory and time to be computed. This situation can be surpassed by applying Reduced Order Modeling (ROM) techniques, allowing the user to capture the system's dominant effects to build a high-fidelity reduced model that gives the possibility to predict and analyze the behaviour of a complex model using low computational resources within a micro time-step. This paper aims to enrich the already implemented Kratos' Rom Application with a reconstruction of the reaction and 2nd Piola Kirkchhoff stress fields. The applied methodology is a projection-based strategy using the Proper Orthogonal Decomposition together with a Gappy Data reconstruction technique. The gappy data comes from building a hyper-reduced order model (HROM). A surrogate model application using static condensation and HROM techniques is proposed to show the possibility of solving multibody systems interfacing Kratos' ROM framework with Mathworks control capabilities in a fast and accurate way. The validation of the applied methodology is given by 3D complex models

Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

The use of machine learning in mechanics is booming. Algorithms inspired by developments in the field of artificial intelligence today cover increasingly varied fields of application. This book illustrates recent results on coupling machine learning with computational mechanics, particularly for the construction of surrogate models or reduced order models. The articles contained in this compilation were presented at the EUROMECH Colloquium 597, « Reduced Order Modeling in Mechanics of Materials », held in Bad Herrenalb, Germany, from August 28th to August 31th 2018. In this book, Artificial Neural Networks are coupled to physics-based models. The tensor format of simulation data is exploited in surrogate models or for data pruning. Various reduced order models are proposed via machine learning strategies applied to simulation data. Since reduced order models have specific approximation errors, error estimators are also proposed in this book. The proposed numerical examples are very close to engineering problems. The reader would find this book to be a useful reference in identifying progress in machine learning and reduced order modeling for computational mechanics

Sparse Approximation and Dictionary Learning with Applications to Audio Signals

PhDOver-complete transforms have recently become the focus of a wide wealth of research in signal processing, machine learning, statistics and related fields. Their great modelling flexibility allows to find sparse representations and approximations of data that in turn prove to be very efficient in a wide range of applications. Sparse models express signals as linear combinations of a few basis functions called atoms taken from a so-called dictionary. Finding the optimal dictionary from a set of training signals of a given class is the objective of dictionary learning and the main focus of this thesis. The experimental evidence presented here focuses on the processing of audio signals, and the role of sparse algorithms in audio applications is accordingly highlighted. The first main contribution of this thesis is the development of a pitch-synchronous transform where the frame-by-frame analysis of audio data is adapted so that each frame analysing periodic signals contains an integer number of periods. This algorithm presents a technique for adapting transform parameters to the audio signal to be analysed, it is shown to improve the sparsity of the representation if compared to a non pitchsynchronous approach and further evaluated in the context of source separation by binary masking. A second main contribution is the development of a novel model and relative algorithm for dictionary learning of convolved signals, where the observed variables are sparsely approximated by the atoms contained in a convolved dictionary. An algorithm is devised to learn the impulse response applied to the dictionary and experimental results on synthetic data show the superior approximation performance of the proposed method compared to a state-of-the-art dictionary learning algorithm. Finally, a third main contribution is the development of methods for learning dictionaries that are both well adapted to a training set of data and mutually incoherent. Two novel algorithms namely the incoherent k-svd and the iterative projections and rotations (ipr) algorithm are introduced and compared to different techniques published in the literature in a sparse approximation context. The ipr algorithm in particular is shown to outperform the benchmark techniques in learning very incoherent dictionaries while maintaining a good signal-to-noise ratio of the representation

Eigenvalues and Pseudospectra of Rectangular Matrices

Pseudospectra of rectangular matrices vary continuously with the matrix entries, a feature that eigenvalues of rectangular matrices do not have. Some properties of eigenvalues and pseudospectra of rectangular matrices are explored, and an efficient algorithm for the computation of pseudospectra is given. Applications are given in (square) eigenvalue computation (Lanczos and Arnoldi iteration), square pseudospectra approximation, control theory (nearest uncontrollable system) and game theory