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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Mathematical Framework for Representing Cyber-Physical System Interdependencies and Resilience

U.S. critical infrastructure is increasingly composed of integrated cyber-physical systems (CPS) whose components are operationally interdependent. These interdependencies can create additional vulnerabilities beyond those that are typically evaluated through independent physical and cyber risk assessments. These additional vulnerabilities, previously undetected at the sub-system and/or component level, could cause cascading effects across both the physical and cyber domains that could lead to infrastructure disruption or failure. This paper will review an initial mathematical framework developed to help understand the dynamics of these interfacing physical and cyber components at a holistic system level. The resulting framework also provides a method for modeling and assessing the resilience of CPS in complex and adaptive systems. It frames the system in terms of mission performance and the potential effects to performance caused by attacks that are cyber, physical, or blended in nature. The framework utilizes a systems-based, state-space modeling approach to reason about risk, resilience, and interdependencies in CPS. Probabilities are applied to each CPS component regarding a threat (both man-made and natural) and the likelihood of that particular component being impacted, along with indirect impact to other interdependent components. These component-level impacts from threats then translate into mission-level impact and systemic state change.

Brigantic, Robert T.↗
DOE OSTI2022

Transforming U.S. Particle Physics Education: A Snowmass 2021 Study

The pursuit of knowledge in particle physics requires constant learning. As new tools become available, new theories are developed, and physicists search for new answers with ever-evolving methods. However, it is the case that formal educational systems serve as the primary training grounds for particle physicists. Graduate school (and undergraduate school to a lesser extent) is where researchers learn most of the technical skills required for research, develop scientific problem-solving abilities, learn how to establish themselves in their field, and begin developing their career. It is unfortunate, then, that the skills gained by physicists during their formal education are often mismatched with the skills actually required for a successful career in physics. We performed a survey of the U.S. particle physics community to determine the missing elements of graduate and undergraduate education and to gauge how to bridge these gaps. In this contributed paper, part of the 2021-22 Snowmass Community Planning Exercise, we report the results of this survey. We also recommend several specific community actions to improve the quality of particle physics education; the "community" here refers to physics departments, national labs, professional societies, funding agencies, and individual physicists.

Bitter, O.↗
DOE OSTI2022

How Community Agreements Can Improve Workplace Culture in Physics

Equity, Diversity, and Inclusion (EDI) committees and Codes of Conduct (CoC) have become common in laboratories and physics departments across the country. However, very often these EDI committees and CoC are not equipped to provide practical consequences for violations, and therefore are mostly performative in nature. A considerable effort has been devoted by various groups within APS units and beyond the APS in developing instead what are now called Community Guidelines. Community Guidelines help implement the core principles in CoC, by setting expectations for participation in in-person events and virtual communication. When further accompanied by accountability and enforcement processes, they develop into Community Agreements. This White Paper discusses the elements necessary to create and implement an effective Community Agreement, reviews examples of Community Agreements in physics, and argues that physics collaborations, physics departments, and ultimately as many physics organizations as possible, however large or small, should have a Community Agreement in place. We advocate that Community Agreements should become part of the bylaws of any entity that has bylaws.

Barzi, Emanuela↗
DOE OSTI2023

Physics-constrained fault diagnosis framework for monitoring a multi-component thermal hydraulic system

A method for diagnosing faults includes receiving a system description of a thermal hydraulic system, the system description indicating a plurality of components and sensors. The method also includes constructing, based on physical conservations laws and using the system description, a plurality of physics-based models for the plurality of components, each of the plurality of physics-based models including unknown parameters. The method further includes receiving historical measurements and calibrating the physics-based models by calculating the unknown parameters of each of the physics-based models using the historical measurements to produce calibrated models. The method also includes receiving sensor measurements of the sensors, and calculating residuals corresponding to differences between measurements predicted by the plurality of calibrated models and the sensor measurements. The method also includes determining, based on the calculated residuals, a fault of a component or a sensor, and generating an alert indicating the fault.

Nguyen, Tat Nghia↗
DOE OSTI2024

MFANS 2024 - Formally Proving Characteristics of Cyber-Physical Systems

Cyber-physical systems (CPS) are engineered systems that rely on the smooth integration of computational algorithms and physical elements. This integration presents new challenges for verifying that systems will behave as expected. The goal of this presentation is to present current challenges and potential solutions for the formal verification of cyber-physical systems. For cyber systems, formal methods refer to systematically rigorous mathematical techniques employed in the specification, development, analysis, and verification of both software and hardware systems. Recent advancements in computer science have yielded sophisticated tools specifically designed to address challenges associated with formal methods in complex systems. These tools leverage various foundational concepts such as logic, formal languages, program semantics, type systems, type theory, and automata theory. A notable achievement in the application of formal methods is the seL4 microkernel, claimed to be the first general-purpose operating-system kernel to be verified. Its proof implies the absence of bugs and guarantees that the kernel meets specifications. For physical systems, dynamic and control theory has a history of using rigorous analytic techniques to prove functional correctness. Lyapunov, optimal, classical, modern, and robust control theories all provide rigorous mathematical methods both to analyze system performance and to design controller that can be guaranteed to meet certain objectives. Recent computational techniques like level set theory and reachability analysis provide assertions that a system's state will avoid unsafe regions. Even though success has been independently achieved for cyber systems and physical systems, the integration of such systems creates new challenges. In particular, there is an obvious discrepancy between finite-state machines and infinite-state systems, resulting in different approaches for modeling and analyzing these system. While it is possible to simulate hybrid systems, this provides only a demonstration of a performance and not proof. For hybrid systems, current formal methods and system analysis approaches typically require a workarounds to work on hybrid systems like CPS. This paper will outline the state of the art and limits of current practice for formally verifying CPS and will identify possible research directions that require attention.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Artificial Intelligence for Event Reconstruction and Higgs Physics at CMS and Future Colliders

This dissertation charts a trajectory in which advances in artificial intelligence (AI) play a central role in pushing the high-energy physics frontier, complementing progress driven by higher collision energies and larger colliders. The discovery potential of the LHC and future colliders relies on accurate reconstruction of increasingly complex particle collision events. In the CMS experiment, this task is performed by the particle-flow (PF) algorithm. This dissertation presents the first implementation of a machine-learning-based particle-flow (MLPF) reconstruction in the CMS detector based on transformer architectures. In simulated top quark--antiquark pair (ttbar) events under LHC Run~3 (2023--2024) conditions, MLPF improves jet energy resolution by 10--20\% compared to standard PF for jets with transverse momentum between 30--100\GeV. Runtime performance is evaluated using simulated multijet events, with a median inference time of 20\unit{ms} per event on an NVIDIA L4 GPU, compa red to approximately 110\unit{ms} for standard PF. The MLPF algorithm is also validated on Run~3 collision data, representing the first data-validated ML-based reconstruction pipeline at any LHC experiment. We then extend MLPF toward future electron--positron colliders and introduce the first full-simulation cross-detector transfer learning workflow for PF reconstruction. The model is pre-trained on simulated events from the Compact Linear Collider detector (CLICdet) and fine-tuned on the CLIC-like detector (CLD) proposed for the Future Circular Collider (FCC). This approach achieves up to a 40\% improvement in jet energy resolution over rule-based reconstruction while reducing the required training dataset size by an order of magnitude, demonstrating the potential of AI to accelerate detector development and optimization. This dissertation also demonstrates how modern AI techniques enhance the sensitivity of LHC physics analyses. A CMS search for highly Lorentz-boosted Higgs bosons decaying to \textrm{W} boson pairs is presented, focusing on the single-lepton final state. A dedicated fine-tuning strategy for \ParT yields an approximately 70\% increase in expected sensitivity relative to the baseline model. The analysis uses proton--proton collision data at a center-of-mass energy of \ensuremath{\sqrt{s}=13\TeV} collected by CMS between 2016 and 2018, corresponding to an integrated luminosity of 138\ensuremath{\ \mathrm{fb}^{-1}}. The expected significance of the search is $1.86\sigma$, with an observed signal strength of $-0.19^{+0.48}_{-0.46}$. Finally, explainable AI techniques are applied to the MLPF and \ParticleNet algorithms using layerwise relevance propagation, showing that both models base their predictions on physically meaningful features consistent with our physics intuition. Together, these results demonstrate how advanced AI methods can enhance reconstruction, analysis sensitivity, and interpretability, shaping the next era of experimental parti cle physics.

Mokhtar, Farouk [UC, San Diego]↗
DOE OSTI2022

New Insights into Physical Aging-Induced Structure Evolution in Carbon Molecular Sieve Membranes

Carbon molecular sieve (CMS) membranes offer the best available combination of scalable economical processability with excellent separation performance. Physical aging of CMS membranes causes pore structure changes over time that affect CMS membrane performance. To provide fundamental insights into the structure evolution in CMS membranes during physical aging, a combined dual-mode sorption and transport model is used in this study to characterize the diffusion coefficients of gas molecules in fresh and 7-day vacuum aged CMS membranes. Further, the results show physical aging of CMS membrane is primarily “diffusion related” and such aging behavior simultaneously causes ultramicropore changes in the continuous phase and Langmuir phase of CMS membrane. The new insights offered in this study suggest strategies to control the physical aging of CMS membranes and even use it as a valuable tool to tune the separation performance of CMS membranes for demanding gas separations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Demonstrating Hierarchical System Development With the Common Community Physics Package Single‐Column Model: A Case Study Over the Southern Great Plains

This study demonstrates a specific application of the hierarchical system development (HSD) approach to investigate, analyze, and attribute model issues within the Unified Forecast System (UFS), with a focus on process isolation. By evaluating a non‐precipitating, shallow cumulus case at the Atmospheric Radiation Measurement Southern Great Plains site in the UFS global forecast against the observation, the investigation identifies a warmer and deeper daytime convective planetary boundary layer (PBL) and misrepresented nocturnal PBL transition. Hypothesis testing, which employs the Common Community Physics Package (CCPP) single‐column model (SCM) and uses the same physics as the UFS global model, confirms that these issues are attributed to the model physics and initialization. Specifically, misrepresented PBL processes are linked to problematic surface condition and a lack of cloud formation, which may stem from deficiencies in PBL and cloud microphysics parameterizations and their interactions. The UFS initial condition contributes to an earlier, excessively collapsed daytime convective boundary layer and a lack of decoupling between the stable boundary layer and residual layer late in the afternoon. This work introduces an avenue for the community to engage with the application of HSD, along with the CCPP and CCPP SCM, to understand the interplay of model physics, disentangle the roles of model components, as well as facilitate model and forecast improvement.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A statistical analysis of the COHERENT data and applications to new physics

The observation of coherent elastic neutrino nucleus scattering (CEvNS) by the COHERENT collaboration in 2017 has opened a new window to both test Standard Model predictions at relatively low energies and probe new physics scenarios. Our investigations show, however, that a careful treatment of the statistical methods used to analyze the data is essential to derive correct constraints and bounds on new physics parameters. In this manuscript we perform a detailed analysis of the publicly available COHERENT CsI data making use of all available background data. We point out that Wilks’ theorem is not fulfilled in general and a calculation of the confidence regions via Monte Carlo simulations following a Feldman-Cousins procedure is necessary. As an example for the necessity of this approach to test new physics scenarios we quantify the allowed ranges for several scenarios with neutrino non-standard interactions. Furthermore, we provide accompanying code to enable an easy implementation of other new physics scenarios as well as data files of our results.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Nuclear Forces for Precision Nuclear Physics: A Collection of Perspectives

This is a collection of perspective pieces contributed by the participants of the Institute for Nuclear Theory’s Program on Nuclear Physics for Precision Nuclear Physics which was held virtually from April 19 to May 7, 2021. The collection represents the reflections of a vibrant and engaged community of researchers on the status of theoretical research in low-energy nuclear physics, the challenges ahead, and new ideas and strategies to make progress in nuclear structure and reaction physics, effective field theory, lattice QCD, quantum information, and quantum computing. Please note the contributed pieces solely reflect the perspectives of the respective authors and do not represent the viewpoints of the Institute for Nuclear theory or the organizers of the program.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Bridging nuclear physics across energy scales: from neutrinoless double-beta decay to high-energy heavy-ion collisions

This paper exemplifies how connecting methods at disparate energy scales can illuminate fundamental questions. By demonstrating that nuclear wave function properties governing rare decay processes also influence collective behavior at extreme temperatures and densities, the authors have opened a new pathway for constraining physics beyond the Standard Model. With multiple ton-scale 0νββ experiments under construction, any method reducing NME uncertainties will directly impact our ability to interpret discoveries or constrain neutrino properties. The general principle—that collective phenomena in high-energy collisions can illuminate subtle features of many-body correlations in the colliding nuclei—may find applications across nuclear and particle physics. Furthermore, this intersection of nuclear structure theory, heavy-ion physics, and fundamental symmetry tests represents fertile ground for future discoveries in modern physics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks

Physics-informed deep learning has emerged as a promising alternative for solving partial differential equations. However, for complex problems, training these networks can still be challenging, often resulting in unsatisfactory accuracy and efficiency. In this work, we demonstrate that the failure of plain physics-informed neural networks arises from the significant discrepancy in the convergence rate of residuals at different training points, where the slowest convergence rate dominates the overall solution convergence. Based on these observations, we propose a pointwise adaptive weighting method that balances the residual decay rate across different training points. The performance of our proposed adaptive weighting method is compared with current state-of-the-art adaptive weighting methods on benchmark problems for both physics-informed neural networks and physics-informed deep operator networks. In conclusion, through extensive numerical results we demonstrate that our proposed approach of balanced residual decay rates offers several advantages, including bounded weights, high prediction accuracy, fast convergence rate, low training uncertainty, low computational cost, and ease of hyperparameter tuning.

Balanced convergence rate↗
DOE OSTI2023

1-D coupled surface flow and transport equations revisited via the physics-informed neural network approach

The de Saint-Venant equation (SVE) and advection–diffusion equation (ADE) are commonly employed to solve solute transport problems in surface water. In this work, we propose a mesh-free method based on the physics-informed neural network (PINN) to solve the one dimensional (1-D) SVE, ADE, and the coupled SVE and ADE (SVE-ADE) under various initial and boundary conditions. The PINN model extends the architecture of deep neural networks (DNNs) with implementation of loss function, which are additionally subject to constraints imposed by the physical laws of SVE and ADE, along with their initial and boundary conditions. In such a manner, PINNs can be quickly steered to the true solution while obeying the physical laws. The results of PINN model are compared with the analytical and/or numerical solutions under various conditions to investigate its accuracy and efficiency in solving the SVE, ADE, and SVE-ADE. Our results indicate PINN can accurately simulate the shock wave morphology and avoid numerical dissipation in unsteady flow condition. The PINN method outweighed traditional numerical methods in several aspects, including its ability to function with small amounts of data, no grid discretization, and random selection of sampling points, etc. Additionally, the PINN method is also suitable for solving inverse problems with sparse and noisy data. With 1% noise and 2000 initial and boundary condition points (N u ), the errors of the estimated flow rate (v) and diffusion coefficient (D) are 0.003% and 0.105%, respectively, which indicate the accuracy and robustness of the proposed method. Finally, our results indicate the capability and robustness of the proposed PINN methodology for solving multi-physics problems, irrespective of the presence of sparse and noisy data in the training dataset.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Exploring physics of ferroelectric domain walls via Bayesian analysis of atomically resolved STEM data

The physics of ferroelectric domain walls is explored using the Bayesian inference analysis of atomically resolved STEM data. We demonstrate that domain wall profile shapes are ultimately sensitive to the nature of the order parameter in the material, including the functional form of Ginzburg-Landau-Devonshire expansion, and numerical value of the corresponding parameters. The preexisting materials knowledge naturally folds in the Bayesian framework in the form of prior distributions, with the different order parameters forming competing (or hierarchical) models. Here, we explore the physics of the ferroelectric domain walls in BiFeO 3 using this method, and derive the posterior estimates of relevant parameters. More generally, this inference approach both allows learning materials physics from experimental data with associated uncertainty quantification, and establishing guidelines for instrumental development answering questions on what resolution and information limits are necessary for reliable observation of specific physical mechanisms of interest.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

High-energy-density-physics measurements in implosions using Bayesian inference

Convergent high-energy-density (HED) experimental platforms are used to study matter under some of the most extreme conditions that can be produced on Earth, comparable to the interior of stars. There are many challenges in using these systems for fundamental measurements currently being addressed by new analysis methods, such as the combination of a reduced physics model and Bayesian inference, allowing a self-consistent inference of physical quantities with a robust error analysis. These methods in combination with simple (as compared to inertial confinement fusion implosions) implosion platforms, which can be modified to show sensitivity to different physical mechanisms of interest, are used to study the physical properties of matter under extreme conditions. This work discusses a subset of implosion targets for studying opacity effects, electron-ion equilibration, and thermal conductivity and, as an example, a system consisting of a thick-shelled, gas-filled laser-direct-drive implosion is used to show how a reduced model and Bayesian inference can help inform experimental design decisions such as diagnostic choice. It is shown that for this system that a combination of neutron and x-ray self-emission diagnostics is critical for constraining the details of the thermodynamic states in the system and that the conductivity exponent in a Spitzer like framework can be constrained to the 30% level in deuterium at gigabar conditions. Furthermore, this process can be applied to many HED systems to make underlying model assumptions explicit and facilitate experimental design and analysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Model fusion with physics-guided machine learning: Projection-based reduced-order modeling

The unprecedented amount of data generated from experiments, field observations, and large-scale numerical simulations at a wide range of spatiotemporal scales has enabled the rapid advancement of data-driven and especially deep learning models in the field of fluid mechanics. Although these methods are proven successful for many applications, there is a grand challenge of improving their generalizability. This is particularly essential when data-driven models are employed within outer-loop applications like optimization. In this work, we put forth a physics-guided machine learning (PGML) framework that leverages the interpretable physics-based model with a deep learning model. Leveraging a concatenated neural network design from multi-modal data sources, the PGML framework is capable of enhancing the generalizability of data-driven models and effectively protects against or inform about the inaccurate predictions resulting from extrapolation. We apply the PGML framework as a novel model fusion approach combining the physics-based Galerkin projection model and long- to short-term memory (LSTM) network for parametric model order reduction of fluid flows. We demonstrate the improved generalizability of the PGML framework against a purely data-driven approach through the injection of physics features into intermediate LSTM layers. Our quantitative analysis shows that the overall model uncertainty can be reduced through the PGML approach, especially for test data coming from a distribution different than the training data. Moreover, we demonstrate that our approach can be used as an inverse diagnostic tool providing a confidence score associated with models and observations. The proposed framework also allows for multi-fidelity computing by making use of low-fidelity models in the online deployment of quantified data-driven models.

42 ENGINEERING↗
DOE OSTI2023

100th Anniversary of the Compton Effect: Its Discovery and Present-Day Impact on Nuclear Physics

This year is the 100th anniversary of the discovery of the Compton effect marked by A. H. Compton’s 1923 Physical Review article. On this occasion, we reflect on the effect’s impact on Nuclear Physics and celebrate the event a century ago and the eminent scientist who made the discovery. Much of the historical comments rely on the article by R. H. Stuewer. A recent Physics Today article presents a historical perspective of Compton’s work and provides more details. The various applications of the Compton effect and its “derivatives” (e.g. inverse Compton scattering) impact many areas of Physics and Chemistry. Here, this short article is restricted to examples taken from the authors' surroundings: the focus is on nuclear-structure experiments with γ-spectroscopic methods and nuclear cross-section measurements relevant for astrophysics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Roadmap for warm dense matter physics

This roadmap presents the state-of-the-art, current challenges and near future developments anticipated in the thriving field of warm dense matter (WDM) physics. Originating from strongly coupled plasma physics, high pressure physics and high energy density science, the WDM physics community has recently taken a giant leap forward. This is due to spectacular developments in laser technology, diagnostic capabilities, and computer simulation techniques. Only in the last decade has it become possible to perform accurate enough simulations & experiments to truly verify theoretical results as well as to reliably design experiments based on predictions. Consequently, this roadmap discusses recent developments of and contemporary challenges for theoretical methods and experimental techniques needed to describe, create and diagnose WDM. A large part of this roadmap is dedicated to specific WDM systems and applications in astrophysics, inertial confinement fusion and novel material synthesis.

dense astrophysical objects↗