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DOE OSTI2023

Scalable Bayesian optimization with randomized prior networks

Several fundamental problems in science and engineering consist of global optimization tasks involving unknown high-dimensional (black-box) functions that map a set of controllable variables to the outcomes of an expensive experiment. Bayesian Optimization (BO) techniques are known to be effective in tackling global optimization problems using a relatively small number objective function evaluations, but their performance suffers when dealing with high-dimensional outputs. To overcome the major challenge of dimensionality, here we propose a deep learning framework for BO and sequential decision making based on bootstrapped ensembles of neural architectures with randomized priors. Using appropriate architecture choices, we show that the proposed framework can approximate functional relationships between design variables and quantities of interest, even in cases where the latter take values in high-dimensional vector spaces or even infinite-dimensional function spaces. In the context of BO, we augmented the proposed probabilistic surrogates with re-parameterized Monte Carlo approximations of multiple-point (parallel) acquisition functions, as well as methodological extensions for accommodating black-box constraints and multi-fidelity information sources. We test the proposed framework against state-of-the-art methods for BO and demonstrate superior performance across several challenging tasks with high-dimensional outputs, including a constrained multi-fidelity optimization task involving shape optimization of rotor blades in turbo-machinery.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

The Modern Research Data Portal: A Design Pattern for Networked, Data-Intensive Science

Here we describe best practices for providing convenient, high-speed, secure access to large data via research data portals. We capture these best practices in a new design pattern, the Modern Research Data Portal, that disaggregates the traditional monolithic web-based data portal to achieve orders-of-magnitude increases in data transfer performance, support new deployment architectures that decouple control logic from data storage, and reduce development and operations costs. We introduce the design pattern; explain how it leverages high-performance Science DMZs and cloud-based data management services; review representative examples at research laboratories and universities, including both experimental facilities and supercomputer sites; describe how to leverage Python APIs for authentication, authorization, data transfer, and data sharing; and use coding examples to demonstrate how these APIs can be used to implement a range of research data portal capabilities. Sample code at a companion web site, https://docs.globus.org/mrdp, provides application skeletons that readers can adapt to realize their own research data portals.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2025

Optimizing Non-Terrestrial Hybrid RF/FSO Links With Reinforcement Learning: Navigating Through Clouds

In the pursuit of ubiquitous broadband connectivity, there has been a significant shift towards the vertical expansion of communication networks into space, particularly through the exploitation of low Earth orbit (LEO) satellite constellations, which are favored for their relatively low latency. However, this approach faces many challenges that need to be addressed, including atmospheric turbulence, high path loss, and dynamic cloud formations. High-altitude pseudo-satellites (HAPS) have emerged as promising relaying layers between LEO satellites and ground stations, enhancing coverage, latency, and direct terrestrial user connectivity. While radio frequency (RF) bands suffer from congestion and limited bandwidth, free space optical (FSO) communications offer higher data rates, but are susceptible to misalignment and weather-induced signal degradation. To address these challenges, a hybrid RF/FSO approach has been proposed to take advantage of both technologies by dynamic switching between RF and FSO based on propagation channel conditions. This paper introduces a reinforcement learning-based algorithm designed to optimize the trajectory of HAPS, maneuver around cloudy areas, and seamlessly switch between the RF and FSO communication modes to maximize the achievable capacity. The proposed approach aims to maximize system performance by intelligently adapting to environmental conditions and offering a promising solution for next-generation space communication networks.

actor-critic algorithm↗

DOE OSTI2025

Polypropylene Composites Reinforced With Recycled Waste Cellulosic Fiber/Fine Mixture: The Impact of Cellulose Sieving on Performance

This study explores how a sieving step of waste cellulosic fiber and fine (WCFF) mixture affects the performance of WCFF‐loaded polypropylene (PP) composites and whether the separation of fines from fibers offers an added benefit. The WCFF samples were downsized, and four different filler size ranges were sieved using a series of mesh sizes from 4 to 0.85 mm. The WCFF/PP composites were then compounded at 20 wt.% loading of WCFF using a twin‐screw extruder. Incorporating WCFF increased the tensile strength to 41.28 MPa and the modulus to 3207 MPa, accounting for 28% and 38% enhancements, respectively. Interestingly, the greatest improvements were associated with the nonsieved WCFF case, and the sieved WCFF fibers provided only marginal enhancements over virgin PP. The outperformance of nonsieved WCFF was attributed to the synergistic reinforcement of hybrid fibers and fines as well as the maintenance of longer fibers in the system. However, the strain at break and impact strength of PP decreased after introducing WCFF. Moreover, the complex viscosity and storage modulus increased with an increase in the filler size, due to the formation of a more effective percolative network. The PP's crystallinity exhibited a relatively strong dependency on the sieving, where WCFF samples with short‐aspect‐ratio fillers promoted the crystallinity significantly. It was also found that the WCFF degradation onset temperature increased once it was incorporated into PP. This study suggests that waste cellulosic feedstocks can be utilized as a reinforcement without additional sieving to manufacture high‐performance and cost‐effective composites.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Systems Level Regulation of Rhythmic Growth Rate and Biomass Accumulation in Grasses

Objectives: Several breakthroughs have been recently made in our understanding of plant growth and biomass accumulation. It was found that plant growth is rhythmically controlled throughout the day by the circadian clock through a complex interplay of light and phytohormone signaling pathways. While plants such as the C4 energy crop sorghum (Sorghum bicolor (L.) Moench) and possibly the C3 grass Brachypodium distachyon also exhibit daily rhythms in growth rate, the molecular details of its regulation remain to be explored. A better understanding of diurnally regulated growth behavior in grasses may lead to species-specific mechanisms highly relevant to future strategies to optimize energy crop biomass yield. Here we propose to devise a systems approach to identify, in parallel, regulatory hubs associated with rhythmic growth in C3 and C4 plants. We propose to use rhythmicity in daily growth patterns to drive the discovery of regulatory network modules controlling biomass accumulation. Description: The project is divided in three main parts: 1) Performing time-lapse imaging and growth measurement in B. distachyon and S. bicolor to determine growth rate dynamic during the day/night cycle. Identifying growth-associated genes whose expression patterns follow the observed growth dynamics using deep sequencing technology, 2) identifying regulators of these genes by screening for DNA-binding proteins interacting with the growth-associated gene promoters identified in Aim 1. Screens will be performed using a validated yeast-one hybrid strategy paired with a specifically designed B. distachyon and S. bicolor transcription factor libraries (1000 clones each), and 3) Selecting 50 potential growth regulators from the screen for downstream characterization. The selection will be made by using a sytems biology approach by calculating the connectivity between growth rate, rhythmic gene expression profiles and TF expression profile and determine which TF is likely part of a hub and a potential regulator of plant growth. We will confirme the 50 DNA-protein interactions using transient transcriptional assays in B. distachyon and S. bicolor, and perform further testing in vivo by measuring growth parameters in transgenic loss- or gain-of-function lines for the selected transcription factors (25 B. distachyon and 3 S. bicolor lines). In 2016 the Principal Investigator relocated the laboratory to The Scripps Research Institute (TSRI) where the project has continued with an end date of 08/31/17. Accomplishments: We successfully collected large datasets of gene expression form the model grass Brachypodium. We used and developed bioinformatics analysis tools to investigate the structure, dynamics and robustness of circadian regulated gene expression in Brachypodium. Relevant Discoveries: We were able to determine that the endogenous circadian clock appears to play a much more subdued role in growth regulation in Brachypodium, that has been demonstrated in either Arabidopsis, or crop plants like Rice, Corn and Soybean. This led to our conclusion that Brachypodium unfortunately is unlikely to serve as an informative model for understanding how growth regulation in plants is under the control of circadian network circuitry. However, we were able to leverage our datasets in Brachypodium to inform us and reinforce a large collaborative study on gene networks governing cell wall deposition in Arabidopsis. This led to a major and highly cited publication relevant to improving biomass production.

09 BIOMASS FUELS↗

DOE OSTI2021

Flow visualisation in real-size optical injectors of conventional, additised, and renewable gasoline blends

Research on renewable and alternative fuels is crucial for improving the energy and environmental efficiency of modern gasoline internal combustion engines. To highlight the influence of fuel rheological and thermodynamic properties on phase change and atomisation processes, three types of gasoline blends were tested. More specifically, the campaign comprised a reference gasoline, an ethanol/gasoline blend (10% v/v) representative of renewable fuels, and an additised gasoline sample treated with viscoelasticity-inducing agents. High-speed imaging of the transient two-phase flow field arising in the internal geometry and the near-nozzle spray region of gasoline injectors was performed employing Diffuse Backlight Illumination. The metallic body of a commercial injector was modified to fit transparent tips realising two nozzle layouts, namely a two-hole real size model resembling the Engine Combustion Network spray G injector and an enraged replica with an offset hole. Experiments were conducted at realistic operating conditions comprising an injection pressure of 100 bar and ambient pressures in the range of 0.1–6.0 bar to cover the entire range of chamber pressures prevailing in Gasoline Direct Injection engines. The action of viscoelastic additives was verified to have a suppressive effect on in-nozzle cavitation (6% reduction in cavitation extent) , while also enhancing spray atomisation at flash-boing conditions, in a manner resembling the more volatile gasoline/ethanol blends. Finally, persisting liquid ligaments were found to form after the end of injection for the additised sample, owing to the surfactant nature of the additives.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2022

Cross Inference of Throughput Profiles Using Micro Kernel Network Method

Dedicated network connections are being increasingly deployed in cloud, centralized and edge computing and data infrastructures, whose throughput profiles are critical indicators of the underlying data transfer performance. Due to the cost and disruptions to physical infrastructures, network emulators, such as Mininet, are often used to generate measurements needed to estimate throughput profiles, typically expressed as a function of the connection round trip time. The profiles estimated using measurements from such emulated networks are usually inaccurate for high bandwidth and high latency connections, since they do not accurately reflect the critical network transport dynamics mainly due to computing and memory constraints of the host. We present a machine learning (ML) method to estimate the throughput profiles using emulation measurements to closely match the testbed and production network profiles. In particular, we propose a micro Kernel Network (mKN) that provides baseline throughput measurements on the host running Mininet emulations, which are used to learn a regression map that converts them to the corresponding testbed measurement estimates. Once initially learned, this map is applied to measurements from subsequent network emulations on the same host. We present experimental measurements to illustrate this approach, and derive generalization equations for the proposed mKN-ML method. Using a four-site scenario emulation, we show the effectiveness of this method in providing accurate concave throughput profiles from inaccurate convex or non-smooth ones indicated by Mininet emulation.

Rao, Nageswara↗

DOE OSTI2026

Architecting the Third Dimension of Electrochemical Energy Storage

Three-dimensional (3D) architectural design has emerged as a powerful strategy to push electrochemical energy storage (EES) devices beyond the intrinsic limitations of conventional two-dimensional (2D) electrodes. While planar architectures enable high packing density and mature manufacturing, they suffer from limited ion transport and low active-material loading. In contrast, 3D architectures introduce low-tortuosity networks and high surface area that enhance charge and mass transport while supporting thick, high mass-loading electrodes. However, their practicality remains hindered by challenges in volumetric density, mechanical stability, and large-scale manufacturability. Here, this Perspective examines the key evaluation and design principles that govern 3D device performance. We discuss the fundamental trade-offs between porosity, volumetric density, and mechanical stability that shape 3D design and highlight emerging strategies for integrating materials engineering, structural optimization, device integration, computational modeling, and scalable manufacturing. By aligning structural functionality with manufacturability, 3D architectures can evolve from laboratory prototypes to commercially viable energy storage systems.

25 ENERGY STORAGE↗

DOE OSTI2015

Single walled carbon nanotube network—Tetrahedral amorphous carbon composite film

Single walled carbon nanotube network (SWCNTN) was coated by tetrahedral amorphous carbon (ta-C) using a pulsed Filtered Cathodic Vacuum Arc system to form a SWCNTN—ta-C composite film. The effects of SWCNTN areal coverage density and ta-C coating thickness on the composite film properties were investigated. X-Ray photoelectron spectroscopy measurements prove the presence of high quality sp{sup 3} bonded ta-C coating on the SWCNTN. Raman spectroscopy suggests that the single wall carbon nanotubes (SWCNTs) forming the network survived encapsulation in the ta-C coating. Nano-mechanical testing suggests that the ta-C coated SWCNTN has superior wear performance compared to uncoated SWCNTN.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2020

Union: An Automatic Workload Manager for Accelerating Network Simulation

With the rapid growth of the machine learning applications, the workloads of future HPC systems are anticipated to be a mix of scientific simulation, big data analytics, and machine learning applications. Simulation is a great research vehicle to understand the performance implications of co-running scientific applications with big data and machine learning workloads on large-scale systems. In this paper, we present Union, a workload manager that provides an automatic framework to facilitate hybrid workload simulation in CODES. Furthermore, we use Union, along with CODES, to investigate various hybrid workloads composed of traditional simulation applications and emerging learning applications on two dragonfly systems. The experiment results show that both message latency and communication time are important performance metrics to evaluate network interference. Network interference on HPC applications is more reflected by the message latency variation, whereas ML application performance depends more on the communication time.

heterogeneous workloads↗

DOE OSTI2018

Modular Spiking Neural Circuits for Mapping Long Short-Term Memory on a Neurosynaptic Processor

Due to the distributed and asynchronous nature of neural computation through low-energy spikes, brain-inspired hardware systems offer high energy efficiency and massive parallelism. One such platform is the IBM TrueNorth neurosynaptic system. Recently, TrueNorth compatible representation learning algorithms have emerged, achieving close to the state-of-the-art performance in various data sets. However, its application in temporal sequence processing models, such as recurrent neural networks (RNNs), is still only at the proof of concept level. There is an inherent difficulty in capturing temporal dynamics of an RNN using spiking neurons, which is only exasperated by the hardware constraints in connectivity and synaptic weight resolution. This paper presents a design flow that overcomes these difficulties and maps a special case of recurrent networks called long short-term memory (LSTM) onto a spike-based platform. The framework utilizes various approximation techniques, such as activation discretization, weight quantization, and scaling and rounding, spiking neural circuits that implement the complex gating mechanisms, and a store-and-release technique to enable neuron synchronization and faithful storage. While the presented techniques can be applied to map LSTM to any spiking neural network (SNN) simulator/emulator, here we choose the TrueNorth chip as the target platform by adhering to its hardware constraints. Three LSTM applications, parity check, extended Reber grammar, and question classification, are evaluated. The tradeoffs among accuracy, performance, and energy tradeoffs achieved on TrueNorth are demonstrated. Finally, this is compared with the performance on an SNN platform without hardware constraints, which represents the upper bound of the achievable accuracy.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Characterizing Quantum Classifier Utility in Natural Language Processing Workflows

Quantum Natural Language Processing (QNLP) develops natural language processing (NLP) models for deployment on quantum computers. We explore feature and data prototype selection techniques to address challenges posed by encoding high dimensional features. Our study builds quantum circuit classifiers that includes classical feature pre-processing, quantum embedding and quantum model training. The quantum models are built on 4 or 6 qubits and the quantum neural network (QNN) uses the established bricklayer design. We compare the dependence of model performance (in terms of accuracy and F1 scores) on feature length, embedding gates and parameterized unitary design. We compare the performance of quantum machine learning models to classical convolution neural network model (CNN) on binary and multi-class classification tasks using two datasets of synthetic features and labels. The first is the ECP-CANDLE P3B3 dataset a corpus of synthetically generated cancer pathology reports. The second dataset is extracted from well-known benchmark dataset (MADELON) - features are generated with a combination of informative, repeated and uninformative features. Both datasets are used for binary classification and multi-class classification with 3 classes. We observe robust, accurate performance from all models on the binary classification tasks, but multiclass classification is a challenge for the quantum models-there is a notable decrease in accuracy when using 3 classes. Overall the performance is comparable in terms of recall and accuracy between QNNs and CNNs, even with large datasets. These results provide a point of comparison between quantum and classical models on real-world datasets.

Hamilton, Kathleen↗

DOE OSTI2022

A scalable solid-state nanoporous network with atomic-level interaction design for carbon dioxide capture

Carbon capture and sequestration reduces carbon dioxide emissions and is critical in accomplishing carbon neutrality targets. Here, we demonstrate new sustainable, solid-state, polyamine-appended, cyanuric acid–stabilized melamine nanoporous networks (MNNs) via dynamic combinatorial chemistry (DCC) at the kilogram scale toward effective and high-capacity carbon dioxide capture. Polyamine-appended MNNs reaction mechanisms with carbon dioxide were elucidated with double-level DCC where two-dimensional heteronuclear chemical shift correlation nuclear magnetic resonance spectroscopy was performed to demonstrate the interatomic interactions. We distinguished ammonium carbamate pairs and a mix of ammonium carbamate and carbamic acid during carbon dioxide chemisorption. The coordination of polyamine and cyanuric acid modification endows MNNs with high adsorption capacity (1.82 millimoles per gram at 1 bar), fast adsorption time (less than 1 minute), low price, and extraordinary stability to cycling by flue gas. This work creates a general industrialization method toward carbon dioxide capture via DCC atomic-level design strategies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

ASCENDS: Advanced Data SCiENce Toolkit for Non-Data Scientists

Data science capabilities are becoming important more and more. Even though there exist many data science and machine learning tools, they are not intuitive for many traditional scientists who are not familiar with programming. ASCENDS provides easy and intuitive interfaces (command-line interface, graphical user interface) to users so they can use data science capabilities without having to understand programming language. ASCENDS performs data visualization, machine learning model training, storing and prediction (regression problem), automatic hyperparameter tuning, and supports various machine learning models including random forest, kernel ridge, nearest neighbor, neural network, etc. Users can use standard CSV (comma separated values) format data set and investigate the feature columns that are highly correlated to a target column; further, they can learn ML models, then store and use them for predictions. The tool is supposed to be generically applicable so that it can be used for many different applications.

Sangkeun, MattLee↗

DOE OSTI2020

How Low Can You Go? Using Synthetic 3D Imagery to Drastically Reduce Real-World Training Data for Object Detection

Deep convolutional neural networks (DCNNs) currently provide state-of-the-art performance on image classification and object detection tasks, and there are many global security mission areas where such models could be extremely useful. Crucially, the success of these models is driven in large part by the widespread availability of high-quality open source data sets such as Image Net, Common Objects in Context (COCO), and KITTI, which contain millions of images with thousands of unique labels. However, global security relevant objects-of-interest can be difficult to obtain: relevant events are low frequency and high consequence; the content of relevant images is sensitive; and adversaries and proliferators seek to obscure their activities. For these cases where exemplar data is hard to come-by, even fine-tuning an existing model with available data can be effectively impossible. Recent work demonstrated that models can be trained using a combination of real-world and synthetic images generated from 3D representations; that such models can exceed the performance of models trained using real-world data alone; and that the generated images need not be perfectly realistic (Tremblay, et al., 2018). However, this approach still required hundreds to thousands of real-world images for training and fine tuning, which for sparse, global security-relevant datasets can be an unrealistic hurdle. In this research, we validate the performance and behavior of DCNN models as we drive the number of real-world images used for training object detection tasks down to a minimal set. We perform multiple experiments to identify the best approach to train DCNNs from an extremely small set of real-world images. In doing so, we: Develop state-of-the-art, parameterized 3D models based on real-world images and sample from their parameters to increase the variance in synthetic image training data; Use machine learning explainability techniques to highlight and correct through targeted training the biases that result from training using completely synthetic images; and Validate our results by comparing the performance of the models trained on synthetic data to one another, and to a control model created by fine-tuning an existing ImageNet-trained model with a limited number (hundreds) of real-world images.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Verification and Validation of Performance with Dissemination of Best Practices in District Energy and CHP for Enhanced Resiliency, Energy Efficiency, and Cybersecurity

This report contains the results of the International District Energy Association’s work to analyze, validate, and verify performance data of existing district energy systems and identify industy best practices for the purpose of improving system reliability, resiliency, and efficiency, and to accellerate decarbonization. In addition to a technical evaluation of the surveyed systems and identification of a series of technical performance metrics, the report illustrates the accompanying operations and financial best practices employed by surveyed systems to fully serve their customer base. Additionally, the third chapter of the report describes the current landscape of cybersecurity threats and counteracting measures, and recommends a series of steps for effectively guarding highly networked district energy systems against cybersecurity attacks.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2022

Fed-DeepONet: Stochastic Gradient-Based Federated Training of Deep Operator Networks

The Deep Operator Network (DeepONet) framework is a different class of neural network architecture that one trains to learn nonlinear operators, i.e., mappings between infinite-dimensional spaces. Traditionally, DeepONets are trained using a centralized strategy that requires transferring the training data to a centralized location. Such a strategy, however, limits our ability to secure data privacy or use high-performance distributed/parallel computing platforms. To alleviate such limitations, in this paper, we study the federated training of DeepONets for the first time. That is, we develop a framework, which we refer to as Fed-DeepONet, that allows multiple clients to train DeepONets collaboratively under the coordination of a centralized server. To achieve Fed-DeepONets, we propose an efficient stochastic gradient-based algorithm that enables the distributed optimization of the DeepONet parameters by averaging first-order estimates of the DeepONet loss gradient. Then, to accelerate the training convergence of Fed-DeepONets, we propose a moment-enhanced (i.e., adaptive) stochastic gradient-based strategy. Finally, we verify the performance of Fed-DeepONet by learning, for different configurations of the number of clients and fractions of available clients, (i) the solution operator of a gravity pendulum and (ii) the dynamic response of a parametric library of pendulums.

Moya, Christian↗

DOE OSTI2013

Network support for system initiated checkpoints

A system, method and computer program product for supporting system initiated checkpoints in parallel computing systems. The system and method generates selective control signals to perform checkpointing of system related data in presence of messaging activity associated with a user application running at the node. The checkpointing is initiated by the system such that checkpoint data of a plurality of network nodes may be obtained even in the presence of user applications running on highly parallel computers that include ongoing user messaging activity.

97 MATHEMATICS AND COMPUTING↗