FCHL revisited:Faster and more accurate quantum machine learning

We introduce the FCHL19 representation for atomic environments in molecules or condensed-phase systems. Machine learning models based on FCHL19 are able to yield predictions of atomic forces and energies of query compounds with chemical accuracy on the scale of milliseconds. FCHL19 is a revision of our previous work [Faber et al. 2018] where the representation is discretized and the individual features are rigorously optimized using Monte Carlo optimization. Combined with a Gaussian kernel function that incorporates elemental screening, chemical accuracy is reached for energy learning on the QM7b and QM9 datasets after training for minutes and hours, respectively. The model also shows good performance for non-bonded interactions in the condensed phase for a set of water clusters with an MAE binding energy error of less than 0.1 kcal/mol/molecule after training on 3,200 samples. For force learning on the MD17 dataset, our optimized model similarly displays state-of-the-art accuracy with a regressor based on Gaussian process regression. When the revised FCHL19 representation is combined with the operator quantum machine learning regressor, forces and energies can be predicted in only a few milliseconds per atom. The model presented herein is fast and lightweight enough for use in general chemistry problems as well as molecular dynamics simulations

Software for Exascale Computing - SPPEXA 2016-2019

This open access book summarizes the research done and results obtained in the second funding phase of the Priority Program 1648 "Software for Exascale Computing" (SPPEXA) of the German Research Foundation (DFG) presented at the SPPEXA Symposium in Dresden during October 21-23, 2019. In that respect, it both represents a continuation of Vol. 113 in Springer’s series Lecture Notes in Computational Science and Engineering, the corresponding report of SPPEXA’s first funding phase, and provides an overview of SPPEXA’s contributions towards exascale computing in today's sumpercomputer technology. The individual chapters address one or more of the research directions (1) computational algorithms, (2) system software, (3) application software, (4) data management and exploration, (5) programming, and (6) software tools. The book has an interdisciplinary appeal: scholars from computational sub-fields in computer science, mathematics, physics, or engineering will find it of particular interest

Sparse models for positive definite matrices

University of Minnesota Ph.D. dissertation. Febrauary 2015. Major: Electrical Engineering. Advisor: Nikolaos P. Papanikolopoulos. 1 computer file (PDF); ix, 141 pages.Sparse models have proven to be extremely successful in image processing, computer vision and machine learning. However, a majority of the effort has been focused on vector-valued signals. Higher-order signals like matrices are usually vectorized as a pre-processing step, and treated like vectors thereafter for sparse modeling. Symmetric positive definite (SPD) matrices arise in probability and statistics and the many domains built upon them. In computer vision, a certain type of feature descriptor called the region covariance descriptor, used to characterize an object or image region, belongs to this class of matrices. Region covariances are immensely popular in object detection, tracking, and classification. Human detection and recognition, texture classification, face recognition, and action recognition are some of the problems tackled using this powerful class of descriptors. They have also caught on as useful features for speech processing and recognition.Due to the popularity of sparse modeling in the vector domain, it is enticing to apply sparse representation techniques to SPD matrices as well. However, SPD matrices cannot be directly vectorized for sparse modeling, since their implicit structure is lost in the process, and the resulting vectors do not adhere to the positive definite manifold geometry. Therefore, to extend the benefits of sparse modeling to the space of positive definite matrices, we must develop dedicated sparse algorithms that respect the positive definite structure and the geometry of the manifold. The primary goal of this thesis is to develop sparse modeling techniques for symmetric positive definite matrices. First, we propose a novel sparse coding technique for representing SPD matrices using sparse linear combinations of a dictionary of atomic SPD matrices. Next, we present a dictionary learning approach wherein these atoms are themselves learned from the given data, in a task-driven manner. The sparse coding and dictionary learning approaches are then specialized to the case of rank-1 positive semi-definite matrices. A discriminative dictionary learning approach from vector sparse modeling is extended to the scenario of positive definite dictionaries. We present efficient algorithms and implementations, with practical applications in image processing and computer vision for the proposed techniques

Automated Evaluation of One-Loop Six-Point Processes for the LHC

In the very near future the first data from LHC will be available. The searches for the Higgs boson and for new physics will require precise predictions both for the signal and the background processes. Tree level calculations typically suffer from large renormalization scale uncertainties. I present an efficient implementation of an algorithm for the automated, Feynman diagram based calculation of one-loop corrections to processes with many external particles. This algorithm has been successfully applied to compute the virtual corrections of the process $u\bar{u}\to b\bar{b}b\bar{b}$ in massless QCD and can easily be adapted for other processes which are required for the LHC.Comment: 232 pages, PhD thesi

Design and HPC implementation of unsupervised Kernel methods in the context of molecular dynamics

The thesis represents an extensive research in the multidisciplinary domain formed by the cross contamination of unsupervised learning and molecular dynamics, two research elds that are coming close creating a breeding ground for valuable new concepts and methods. In this context, at rst, we describe a novel engine to perform large scale kernel k-means clustering. We introduce a two-fold approximation strategy to minimize the kernel k-means cost function in which the trade-off between accuracy and execution time is automatically ruled by the available system memory

Modeling, Characterizing and Reconstructing Mesoscale Microstructural Evolution in Particulate Processing and Solid-State Sintering

abstract: In material science, microstructure plays a key role in determining properties, which further determine utility of the material. However, effectively measuring microstructure evolution in real time remains an challenge. To date, a wide range of advanced experimental techniques have been developed and applied to characterize material microstructure and structural evolution on different length and time scales. Most of these methods can only resolve 2D structural features within a narrow range of length scale and for a single or a series of snapshots. The currently available 3D microstructure characterization techniques are usually destructive and require slicing and polishing the samples each time a picture is taken. Simulation methods, on the other hand, are cheap, sample-free and versatile without the special necessity of taking care of the physical limitations, such as extreme temperature or pressure, which are prominent issues for experimental methods. Yet the majority of simulation methods are limited to specific circumstances, for example, first principle computation can only handle several thousands of atoms, molecular dynamics can only efficiently simulate a few seconds of evolution of a system with several millions particles, and finite element method can only be used in continuous medium, etc. Such limitations make these individual methods far from satisfaction to simulate macroscopic processes that a material sample undergoes up to experimental level accuracy. Therefore, it is highly desirable to develop a framework that integrate different simulation schemes from various scales to model complicated microstructure evolution and corresponding properties. Guided by such an objective, we have made our efforts towards incorporating a collection of simulation methods, including finite element method (FEM), cellular automata (CA), kinetic Monte Carlo (kMC), stochastic reconstruction method, Discrete Element Method (DEM), etc, to generate an integrated computational material engineering platform (ICMEP), which could enable us to effectively model microstructure evolution and use the simulated microstructure to do subsequent performance analysis. In this thesis, we will introduce some cases of building coupled modeling schemes and present the preliminary results in solid-state sintering. For example, we use coupled DEM and kinetic Monte Carlo method to simulate solid state sintering, and use coupled FEM and cellular automata method to model microstrucutre evolution during selective laser sintering of titanium alloy. Current results indicate that joining models from different length and time scales is fruitful in terms of understanding and describing microstructure evolution of a macroscopic physical process from various perspectives.Dissertation/ThesisDoctoral Dissertation Materials Science and Engineering 201

SCALABALE AND DISTRIBUTED METHODS FOR LARGE-SCALE VISUAL COMPUTING

The objective of this research work is to develop efficient, scalable, and distributed methods to meet the challenges associated with the processing of immense growth in visual data like images, videos, etc. The motivation stems from the fact that the existing computer vision approaches are computation intensive and cannot scale-up to carry out analysis on the large collection of data as well as to perform the real-time inference on the resourceconstrained devices. Some of the issues encountered are: 1) increased computation time for high-level representation from low-level features, 2) increased training time for classification methods, and 3) carry out analysis in real-time on the live video streams in a city-scale surveillance network. The issue of scalability can be addressed by model approximation and distributed implementation of computer vision algorithms. But existing scalable approaches suffer from the high loss in model approximation and communication overhead. In this thesis, our aim is to address some of the issues by proposing efficient methods for reducing the training time over large datasets in a distributed environment, and for real-time inference on resource-constrained devices by scaling-up computation-intensive methods using the model approximation. A scalable method Fast-BoW is presented for reducing the computation time of bagof-visual-words (BoW) feature generation for both hard and soft vector-quantization with time complexities O(|h| log2 k) and O(|h| k), respectively, where |h| is the size of the hash table used in the proposed approach and k is the vocabulary size. We replace the process of finding the closest cluster center with a softmax classifier which improves the cluster boundaries over k-means and can also be used for both hard and soft BoW encoding. To make the model compact and faster, the real weights are quantized into integer weights which can be represented using few bits (2 − 8) only. Also, on the quantized weights, the hashing is applied to reduce the number of multiplications which accelerate the entire process. Further the effectiveness of the video representation is improved by exploiting the structural information among the various entities or same entity over the time which is generally ignored by BoW representation. The interactions of the entities in a video are formulated as a graph of geometric relations among space-time interest points. The activities represented as graphs are recognized using a SVM with low complexity graph kernels, namely, random walk kernel (O(n3)) and Weisfeiler-Lehman kernel (O(n)). The use of graph kernel provides robustness to slight topological deformations, which may occur due to the presence of noise and viewpoint variation in data. The further issues such as computation and storage of the large kernel matrix are addressed using the Nystrom method for kernel linearization. The second major contribution is in reducing the time taken in learning of kernel supvi port vector machine (SVM) from large datasets using distributed implementation while sustaining classification performance. We propose Genetic-SVM which makes use of the distributed genetic algorithm to reduce the time taken in solving the SVM objective function. Further, the data partitioning approaches achieve better speed-up than distributed algorithm approaches but invariably leads to the loss in classification accuracy as global support vectors may not have been chosen as local support vectors in their respective partitions. Hence, we propose DiP-SVM, a distribution preserving kernel SVM where the first and second order statistics of the entire dataset are retained in each of the partitions. This helps in obtaining local decision boundaries which are in agreement with the global decision boundary thereby reducing the chance of missing important global support vectors. Further, the task of combining the local SVMs hinder the training speed. To address this issue, we propose Projection-SVM, using subspace partitioning where a decision tree is constructed on a projection of data along the direction of maximum variance to obtain smaller partitions of the dataset. On each of these partitions, a kernel SVM is trained independently, thereby reducing the overall training time. Also, it results in reducing the prediction time significantly. Another issue addressed is the recognition of traffic violations and incidents in real-time in a city-scale surveillance scenario. The major issues are accurate detection and real-time inference. The central computing infrastructures are unable to perform in real-time due to large network delay from video sensor to the central computing server. We propose an efficient framework using edge computing for deploying large-scale visual computing applications which reduces the latency and the communication overhead in a camera network. This framework is implemented for two surveillance applications, namely, motorcyclists without a helmet and accident incident detection. An efficient cascade of convolutional neural networks (CNNs) is proposed for incrementally detecting motorcyclists and their helmets in both sparse and dense traffic. This cascade of CNNs shares common representation in order to avoid extra computation and over-fitting. The accidents of the vehicles are modeled as an unusual incident. The deep representation is extracted using denoising stacked auto-encoders trained from the spatio-temporal video volumes of normal traffic videos. The possibility of an accident is determined based on the reconstruction error and the likelihood of the deep representation. For the likelihood of the deep representation, an unsupervised model is trained using one class SVM. Also, the intersection points of the vehicle’s trajectories are used to reduce the false alarm rate and increase the reliability of the overall system. Both the approaches are evaluated on the real traffic videos collected from the video surveillance network of Hyderabad city in India. The experiments on the real traffic videos demonstrate the efficacy of the proposed approache

Process Monitoring and Uncertainty Quantification for Laser Powder Bed Fusion Additive Manufacturing

Metal Additive manufacturing (AM) such as Laser Powder-Bed Fusion (LPBF) processes offer new opportunities for building parts with geometries and features that other traditional processes cannot match. At the same time, LPBF imposes new challenges on practitioners. These challenges include high complexity of simulating the AM process, anisotropic mechanical properties, need for new monitoring methods. Part of this Dissertation develops a new method for layerwise anomaly detection during for LPBF. The method uses high-speed thermal imaging to capture melt pool temperature and is composed of a procedure utilizing spatial statistics and machine learning. Another parts of this Dissertation solves problems for efficient use of computer simulation models. Simulation models are vital for accelerated development of LPBF because we can integrate multiple computer simulation models at different scales to optimize the process prior to the part fabrication. This integration of computer models often happens in a hierarchical fashion and final model predicts the behavior of the most important Quantity of Interest (QoI). Once all the models are coupled, a system of models is created for which a formal Uncertainty Quantification (UQ) is needed to calibrate the unknown model parameters and analyze the discrepancy between the models and the real-world in order to identify regions of missing physics. This dissertation presents a framework for UQ of LPBF models with the following features: (1) models have multiple outputs instead of a single output, (2) models are coupled using the input and output variables that they share, and (3) models can have partially unobservable outputs for which no experimental data are present. This work proposes using Gaussian process (GP) and Bayesian networks (BN) as the main tool for handling UQ for a system of computer models with the aforementioned properties. For each of our methodologies, we present a case study of a specific alloy system. Experimental data are captured by additively manufacturing parts and single tracks to evaluate the proposed method. Our results show that the combination of GP and BN is a powerful and flexible tool to answer UQ problems for LPBF