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DOE OSTI2023

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to purely data-driven methods, {PIML} models can be trained from additional information obtained by enforcing physical laws such as energy and mass conservation. More broadly, {PIML} models can include abstract properties and conditions such as stability, convexity, or invariance. The basic premise of {PIML} is that the integration of ML and physics can yield more effective, physically consistent, and data-efficient models. This paper aims to provide a tutorial-like overview of the recent advances in {PIML} for dynamical system modeling and control. Specifically, the paper covers an overview of the theory, fundamental concepts and methods, tools, and applications on topics of: 1) physics-informed learning for system identification; 2) physics-informed learning for control; 3) analysis and verification of {PIML} models; and 4) physics-informed digital twins. The paper is concluded with a perspective on open challenges and future research opportunities.

Nghiem, Truong↗

NASA NTRS1991

Interactive access and management for four-dimensional environmental data sets using McIDAS

Significant accomplishments in the following areas are presented: (1) enhancements to the visualization of 5-D data sets (VIS-5D); (2) development of the visualization of global images (VIS-GI) application; (3) design of the Visualization for Algorithm Development (VIS-AD) System; and (4) numerical modeling applications. The focus of current research and future research plans is presented and the following topics are addressed: (1) further enhancements to VIS-5D; (2) generalization and enhancement of the VIS-GI application; (3) the implementation of the VIS-AD System; and (4) plans for modeling applications.

Hibbard, William L.↗

DOE OSTI2017

A thermal conductivity model for U-Si compounds

U 3 Si 2 is a candidate for accident tolerant nuclear fuel being developed as an alternative to UO 2 in commercial light water reactors (LWRs). One of its main benefits compared to UO 2 is higher thermal conductivity that increases with temperature. This increase is contrary to UO 2 , for which the thermal conductivity decreases with temperature. The reason for the difference is the electronic origin of thermal conductivity in U 3 Si 2 , as compared to the phonon mechanism responsible for thermal transport in UO 2 . The phonon thermal conductivity in UO 2 is unusually low for a fluorite oxide due to the strong interaction with the spins in the paramagnetic phase. The thermal conductivity of U 3 Si 2 as well as other U-Si compounds has been measured experimentally [1-4]. However, for fuel performance simulations it is also critical to model the degradation of the thermal conductivity due to damage and microstructure evolution caused by the reactor environment (irradiation and high temperature). For UO 2 this reduction is substantial and it has been the topic of extensive NEAMS research resulting in several publications [5, 6]. There are no data or models for the evolution of the U 3 Si 2 thermal conductivity under irradiation. We know that the intrinsic thermal conductivities of UO 2 (semi-conductor) and U 3 Si 2 (metal) are very different, and we do not necessarily expect the dependence on damage to be the same either, which could present another advantage for the silicide fuel. In this report we summarize the first step in developing a model for the thermal conductivity of U-Si compounds with the goal of capturing the effect of damage in U 3 Si 2 . Next year, we will focus on lattice damage. We will also attempt to assess the impact of fission gas bubbles.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS2000

JANNAF 25th Airbreathing Propulsion Subcommittee, 37th Combustion Subcommittee and 1st Modeling and Simulation Subcommittee Joint Meeting

Volume I, the first of three volumes, is a compilation of 24 unclassified/unlimited-distribution technical papers presented at the Joint Army-Navy-NASA-Air Force (JANNAF) 25th Airbreathing Propulsion Subcommittee, 37th Combustion Subcommittee and 1st Modeling and Simulation Subcommittee (MSS) meeting held jointly with the 19th Propulsion Systems Hazards Subcommittee. The meeting was held 13-17 November 2000 at the Naval Postgraduate School and Hyatt Regency Hotel, Monterey, California. Topics covered include: a Keynote Address on Future Combat Systems, a review of the new JANNAF Modeling and Simulation Subcommittee, and technical papers on Hyper-X propulsion development and verification; GTX airbreathing launch vehicles; Hypersonic technology development, including program overviews, fuels for advanced propulsion, ramjet and scramjet research, hypersonic test medium effects; and RBCC engine design and performance, and PDE and UCAV advanced and combined cycle engine technologies.

Fry, Ronald S.↗

DOE OSTI2021

Towards Predictive Plasma Science and Engineering through Revolutionary Multi-Scale Algorithms and Models (Final Report)

This report describes the high-level accomplishments from the Plasma Science and Engineering Grand Challenge LDRD at Sandia National Laboratories. The Laboratory has a need to demonstrate predictive capabilities to model plasma phenomena in order to rapidly accelerate engineering development in several mission areas. The purpose of this Grand Challenge LDRD was to advance the fundamental models, methods, and algorithms along with supporting electrode science foundation to enable a revolutionary shift towards predictive plasma engineering design principles. This project integrated the SNL knowledge base in computer science, plasma physics, materials science, applied mathematics, and relevant application engineering to establish new cross-laboratory collaborations on these topics. As an initial exemplar, this project focused efforts on improving multi-scale modeling capabilities that are utilized to predict the electrical power delivery on large-scale pulsed power accelerators. Specifically, this LDRD was structured into three primary research thrusts that, when integrated, enable complex simulations of these devices: (1) the exploration of multi-scale models describing the desorption of contaminants from pulsed power electrodes, (2) the development of improved algorithms and code technologies to treat the multi-physics phenomena required to predict device performance, and (3) the creation of a rigorous verification and validation infrastructure to evaluate the codes and models across a range of challenge problems. These components were integrated into initial demonstrations of the largest simulations of multi-level vacuum power flow completed to-date, executed on the leading HPC computing machines available in the NNSA complex today. These preliminary studies indicate relevant pulsed power engineering design simulations can now be completed in (of order) several days, a significant improvement over pre-LDRD levels of performance.

42 ENGINEERING↗

NASA NTRS1984

International Symposium on Remote Sensing of Environment, 17th, University of Michigan, Ann Arbor, MI, May 9-13, 1983, Proceedings. Volumes 1, 2 & 3

The collection, processing, and analysis of remote-sensing data from ground-based, airborne, and spaceborne instruments for application to the monitoring and management of the earth and environment and resources are examined in reviews and reports, some in summary form. Subject areas covered include US policy and directions on remote sensing (RS); the future of terrestrial RS from space; RS of land, oceans, and atmosphere from a global perspective; RS in hydrological modeling; microprocessing technology; array processors; geobased information systems; artificial intelligence; the Shuttle imaging radar; and current results from Landsat-4. Among the specific topics discussed are RS application to hydrocarbon exploration, airborne gamma-radiation assessment of snow water equivalent, surface-vegetation-biomass modeling from AVHRR and Landsat data, Landsat imagery of Mediterranean pollution, fast two-dimensional filtering of thermal-scanner data, RS of severe convective storms, registration of rotated images by invariant moments, and the geometric accuracy of Landsat-4 Thematic-Mapper P-tapes.

Source record↗

DOE OSTI2020

On negative induced polarization in frequency domain measurements

SUMMARY Induced polarization (IP) has been widely used to non-invasively characterize electrical conduction and polarization in the subsurface resulting from an applied electric field. Earth materials exhibit a lossy capacitance defined by a negative intrinsic phase in frequency-domain IP (FDIP) or a positive intrinsic chargeability in time-domain IP (TDIP). However, error-free positive apparent phase or negative apparent chargeability (i.e. negative IP effects) can occur in IP measurements over heterogeneous media. While negative IP effects in TDIP data sets have been discussed, no studies have addressed this topic in detail for FDIP measurements. We describe theory and numerical modelling to explain the origin of negative IP effects in FDIP measurements. A positive apparent phase may occur when a relatively high polarizability feature falls into negative sensitivity zones of complex resistivity measurements. The polarity of the apparent phase is determined by the distribution of subsurface intrinsic phase and resistivity, with the resistivity impacting the apparent phase polarity via its control on the sensitivity distribution. A physical explanation for the occurrence of positive apparent phase data is provided by an electric circuit model representing a four-electrode measurement. We also show that the apparent phase polarity will be frequency dependent when resistivity changes significantly with frequency (i.e. in the presence of significant IP effects). Consequently, negative IP effects manifest themselves in the shape of apparent phase spectra recorded with multifrequency (spectral IP) data sets. Our results imply that positive apparent phase measurements should be anticipated and should be retained during inversion and interpretation of single frequency and spectral IP data sets.

Geochemistry & Geophysics↗

DOE OSTI2022

Master equation emulation and coherence preservation with classical control of a superconducting qubit

Open quantum systems are a topic of intense theoretical research. The use of master equations to model a system's evolution subject to an interaction with an external environment is one of the most successful theoretical paradigms. General experimental tools to study different open system realizations have been limited, and so it is highly desirable to develop experimental tools which emulate diverse master equation dynamics and give a way to test open systems theories. In this paper we demonstrate a systematic method for engineering specific system-environment interactions and emulating master equations of a particular form using classical stochastic noise in a superconducting transmon qubit. We also demonstrate that non-Markovian noise can be used as a resource to extend the coherence of a quantum system and counteract the adversarial effects of Markovian environments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Post-hoc reweighting of hadron production in the Lund string model

We present a method for reweighting flavor selection in the Lund string fragmentation model. This is the process of calculating and applying event weights enabling fast and exact variation of hadronization parameters on pre-generated event samples. The procedure is post hoc, requiring only a small amount of additional information stored per event, and allowing for efficient estimation of hadronization uncertainties without repeated simulation. Weight expressions are derived from the hadronization algorithm itself, and validated against direct simulation for a wide range of observables and parameter shifts. The hadronization algorithm can be viewed as a hierarchical Markov process with stochastic rejections, a structure common to many complex simulations outside of high-energy physics. This perspective makes the method modular, extensible, and potentially transferable to other domains. We demonstrate the approach in Pythia, including both coverage considerations and timing benefits. For the purpose of this paper, our goal is to develop and demonstrate the the formalism, and we therefore exclude several model variations for baryon production (popcorn model, junction production) needed for proton collisions. These will be the topic of a future paper.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2013

A comparison of methods for representing sparsely sampled random quantities.

This report discusses the treatment of uncertainties stemming from relatively few samples of random quantities. The importance of this topic extends beyond experimental data uncertainty to situations involving uncertainty in model calibration, validation, and prediction. With very sparse data samples it is not practical to have a goal of accurately estimating the underlying probability density function (PDF). Rather, a pragmatic goal is that the uncertainty representation should be conservative so as to bound a specified percentile range of the actual PDF, say the range between 0.025 and .975 percentiles, with reasonable reliability. A second, opposing objective is that the representation not be overly conservative; that it minimally over-estimate the desired percentile range of the actual PDF. The presence of the two opposing objectives makes the sparse-data uncertainty representation problem interesting and difficult. In this report, five uncertainty representation techniques are characterized for their performance on twenty-one test problems (over thousands of trials for each problem) according to these two opposing objectives and other performance measures. Two of the methods, statistical Tolerance Intervals and a kernel density approach specifically developed for handling sparse data, exhibit significantly better overall performance than the others.

Romero, Vicente Jose↗

DOE OSTI2015

Connecting Performance Analysis and Visualization to Advance Extreme Scale Computing

The characterization, modeling, analysis, and tuning of software performance has been a central topic in High Performance Computing (HPC) since its early beginnings. The overall goal is to make HPC software run faster on particular hardware, either through better scheduling, on-node resource utilization, or more efficient distributed communication.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

I. Final Report for DOE SBIR Phase I Project DE-SC0013795 Final Report for DOE SBIR Phase I Project DE-SC0013795 Microtron-based Compact, Portable Gamma-Ray Source

Microtron-based Compact, Portable Gamma-Ray Source. The objective of Phase I of this project was to produce a conceptual design of a prototype compact microtron electron accelerator, which could be designed, built, and demonstrated in Phase II of the project. The conceptual design study included an analysis of the parameters of the microtron and its components, and the expected performance of the prototype microtron as a source of x-rays and/or RF neutrons in the MeV energy range. The major components of the microtron are the magnet, the accelerating system, the power system, the vacuum system, the control system, the beam extraction system and the targets to produce x-rays (and/or neutrons). Our objectives for the design of the prototype were for it to be compact, cost-effective, capable of producing high intensity x-ray (an/or neutron) fluxes. In addition, the prototype was to be easily assembled and disassembled so that components could be easily replaced. The main parameters for the prototype are the following: the range of electron kinetic energies, the output power, the RF frequency band (X-band, C-band, or S-Band), the type of injection (Type I or Type II), the magnet type, i.e. permanent magnet, electromagnet, or a hybrid combination of permanent and electromagnet. The results of the Phase I study and analysis for a prototype microtron are the following: The electron energy range can be varied from below 6 MeV to 9 MeV, the optimal frequency range is S-Band (2-4 GHz) RF frequency, Type II injection (described below), and the magnet type is the hybrid version. The prototype version will be capable of producing gamma ray doses of ~1800 R/min-m and neutron fluxes of up to ~6 x 10 10 n/s with appropriate targets. The results of the Phase I study and analysis are provided below. The proposed Phase II plan was to demonstrate the prototype at low beam power. In the subsequent Phase III, high power tests would be performed, and the design of commercial versions of microtrons with various energies, sizes and types would be produced and marketed, including a more compact and more portable 6 MeV battery-powered model that more closely meets the requirements in the original FOA topic description. In the course of the Phase I study, we also identified another microtron version, one that was larger (not compact) and more powerful than that of the Phase II prototype, which could serve as an intense source of photo- neutrons, up to 4 x 10 12 n/s for use in nuclear medicine, short-lived isotope production, or other applications. In addition, it could produce gamma dose rates up to 130 kR/min-m with a heavy metal bremsstrahlung target. The results and specifications of this were submitted to IPAC16 (Reference [12]) the paper is included in Addendum B. Because this version was beyond the scope of the Phase I project, there is no additional description in the Final Report.

43 PARTICLE ACCELERATORS↗

DOE OSTI2026

Quantum-Inspired Bayesian Sampling for Uncertainty Quantification and Machine Learning (Final Technical Report)

With increasing simulation and measurement data, machine learning and artificial intelligence have been widely used in computational decision-making of complex engineering systems. The resulting tools, such as uncertainty quantification solvers, reinforcement learning, and physics-informed machine learning, have achieved great success in critical DOE tasks such as material discovery and design, energy system modeling and control, and numerical weather and climate prediction. A core topic in scientific machine learning and artificial intelligence is Bayesian inference: given an observed data set, people want to estimate the posterior distribution of a (possibly large) number of hidden parameters. Due to the flexibility and weak assumptions, Bayesian sampling has been the mainstream Bayesian inference solvers despite the rapid progress of approximate Bayesian inference. Classical Bayesian sampling methods such as Markov-chain Monte Carlo suffer from a low-acceptance rate due to the random walk nature, therefore state-of-the-art techniques use Hamiltonian Monte Carlo and its variants to efficiently draw posterior samples in a high dimension. The key idea of Hamiltonian Monte Carlo and its variants is to simulate the Hamiltonian dynamics of a classical particle with a fixed mass, and their performance significantly degrades when the posterior distribution is highly spiky or has multiple modes. Leveraging the idea of quantum physics, this project has investigated new theory, algorithms and applications of Bayesian inference (especially Bayesian sampling). The main results include: (1) novel quantum-inspired Bayesian sampling methods that can lead to better accuracy for challenging multi-modal or spiky distributions, (2) more scalable machine learning framework leveraging tensor-compressed Bayesian inference, and (3) Bayesian and sampling approaches for verifying the robustness of continuous and binary neural networks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Proceedings of the second annual deep brain stimulation think tank: What's in the pipeline

Here the proceedings of the 2nd Annual Deep Brain Stimulation Think Tank summarize the most contemporary clinical, electrophysiological, and computational work on DBS for the treatment of neurological and neuropsychiatric disease and represent the insights of a unique multidisciplinary ensemble of expert neurologists, neurosurgeons, neuropsychologists, psychiatrists, scientists, engineers and members of industry. Presentations and discussions covered a broad range of topics, including advocacy for DBS, improving clinical outcomes, innovations in computational models of DBS, understanding of the neurophysiology of Parkinson's disease (PD) and Tourette syndrome (TS) and evolving sensor and device technologies.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

Campaign datasets for Biomass Burning Observation Project (BBOP)

This field campaign will address multiple uncertainties in aerosol intensive properties, which are poorly represented in climate models, by means of aircraft measurements in biomass burning plumes. Key topics to be investigated are: 1. Aerosol mixing state and morphology 2. Mass absorption coefficients (MACs) 3. Chemical composition of non-refractory material associated with light-absorbing carbon (LAC) 4. Production rate of secondary organic aerosol (SOA) 5. Microphysical processes relevant to determining aerosol size distributions and single scattering albedo (SSA) 6. CCN activity. These topics will be investigated through measurements near active fires (0-5 hours downwind), where limited observations indicate rapid changes in aerosol properties, and in biomass burning plumes aged >5 hours. Aerosol properties and their time evolution will be determined as a function of fire type, defined according to fuel and the mix of flaming and smoldering combustion at the source.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Nephelometer PSAP, BBOP

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1973

The influence of polarization on millimeter wave propagation through rain

The measurement and analysis of the depolarization and attenuation that occur when millimeter wave radio signals propagate through rain are described. Progress was made in three major areas: the processing of recorded 1972 data, acquisition and processing of a large amount of 1973 data, and the development of a new theoretical model to predict rain cross polarization and attenuation. Each of these topics is described in detail along with radio frequency system design for cross polarization measurements.

Bostian, C. W.↗

NASA NTRS1975

Some aspects of core formation in Mercury

An evaluation of existing theories on the existence of the planet's metallic core is presented. Topics considered are: (1) magnetic fields; (2) surface geology; (3) cosmochemical models.

Solomon, S. C.↗