Machine Learning Guided Discovery and Design for Inertial Confinement Fusion

Inertial confinement fusion (ICF) experiments at the National Ignition Facility (NIF) and their corresponding computer simulations produce an immense amount of rich data. However, quantitatively interpreting that data remains a grand challenge. Design spaces are vast, data volumes are large, and the relationship between models and experiments may be uncertain. We propose using machine learning to aid in the design and understanding of ICF implosions by integrating simulation and experimental data into a common frame-work. We begin by illustrating an early success of this data-driven design approach which resulted in the discovery of a new class of high performing ovoid-shaped implosion simulations. The ovoids achieve robust performance from the generation of zonal flows within the hotspot, revealing physics that had not previously been observed in ICF capsules. The ovoid discovery also revealed deficiencies in common machine learning algorithms for modeling ICF data. To overcome these inadequacies, we developed a novel algorithm, deep jointly-informed neural networks (DJINN), which enables non-data scientists to quickly train neural networks on their own datasets. DJINN is routinely used for modeling data ICF data and for a variety of other applications (uncertainty quantification; climate, nuclear, and atomic physics data). We demonstrate how DJINN is used to perform parameter inference tasks for NIF data, and how transfer learning with DJINN enables us to create predictive models of direct drive experiments at the Omega laser facility. Much of this work focuses on scalar or modest-size vector data, however many ICF diagnostics produce a variety of images, spectra, and sequential data. We end with a brief exploration of sequence-to-sequence models for emulating time-dependent multiphysics systems of varying complexity. This is a first step toward incorporating multimodal time-dependent data into our analyses to better constrain our predictive models

Learning Optimal Deep Projection of 18^{18}F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes

Several diseases of parkinsonian syndromes present similar symptoms at early stage and no objective widely used diagnostic methods have been approved until now. Positron emission tomography (PET) with $^{18}$F-FDG was shown to be able to assess early neuronal dysfunction of synucleinopathies and tauopathies. Tensor factorization (TF) based approaches have been applied to identify characteristic metabolic patterns for differential diagnosis. However, these conventional dimension-reduction strategies assume linear or multi-linear relationships inside data, and are therefore insufficient to distinguish nonlinear metabolic differences between various parkinsonian syndromes. In this paper, we propose a Deep Projection Neural Network (DPNN) to identify characteristic metabolic pattern for early differential diagnosis of parkinsonian syndromes. We draw our inspiration from the existing TF methods. The network consists of a (i) compression part: which uses a deep network to learn optimal 2D projections of 3D scans, and a (ii) classification part: which maps the 2D projections to labels. The compression part can be pre-trained using surplus unlabelled datasets. Also, as the classification part operates on these 2D projections, it can be trained end-to-end effectively with limited labelled data, in contrast to 3D approaches. We show that DPNN is more effective in comparison to existing state-of-the-art and plausible baselines.Comment: 8 pages, 3 figures, conference, MICCAI DLMIA, 201

Biologically plausible deep learning -- but how far can we go with shallow networks?

Training deep neural networks with the error backpropagation algorithm is considered implausible from a biological perspective. Numerous recent publications suggest elaborate models for biologically plausible variants of deep learning, typically defining success as reaching around 98% test accuracy on the MNIST data set. Here, we investigate how far we can go on digit (MNIST) and object (CIFAR10) classification with biologically plausible, local learning rules in a network with one hidden layer and a single readout layer. The hidden layer weights are either fixed (random or random Gabor filters) or trained with unsupervised methods (PCA, ICA or Sparse Coding) that can be implemented by local learning rules. The readout layer is trained with a supervised, local learning rule. We first implement these models with rate neurons. This comparison reveals, first, that unsupervised learning does not lead to better performance than fixed random projections or Gabor filters for large hidden layers. Second, networks with localized receptive fields perform significantly better than networks with all-to-all connectivity and can reach backpropagation performance on MNIST. We then implement two of the networks - fixed, localized, random & random Gabor filters in the hidden layer - with spiking leaky integrate-and-fire neurons and spike timing dependent plasticity to train the readout layer. These spiking models achieve > 98.2% test accuracy on MNIST, which is close to the performance of rate networks with one hidden layer trained with backpropagation. The performance of our shallow network models is comparable to most current biologically plausible models of deep learning. Furthermore, our results with a shallow spiking network provide an important reference and suggest the use of datasets other than MNIST for testing the performance of future models of biologically plausible deep learning.Comment: 14 pages, 4 figure

Target-adaptive CNN-based pansharpening

We recently proposed a convolutional neural network (CNN) for remote sensing image pansharpening obtaining a significant performance gain over the state of the art. In this paper, we explore a number of architectural and training variations to this baseline, achieving further performance gains with a lightweight network which trains very fast. Leveraging on this latter property, we propose a target-adaptive usage modality which ensures a very good performance also in the presence of a mismatch w.r.t. the training set, and even across different sensors. The proposed method, published online as an off-the-shelf software tool, allows users to perform fast and high-quality CNN-based pansharpening of their own target images on general-purpose hardware