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DOE OSTI2021

Higgs coupling measurements and the scale of new physics

A primary goal of present and future colliders is measuring the Higgs couplings to Standard Model (SM) particles. Any observed deviation from the SM predictions for these couplings is a sign of new physics whose energy scale can be bounded from above by requiring tree-level unitarity. In this paper, we extend previous work on unitarity bounds from the Higgs cubic coupling to Higgs couplings to vector bosons and top quarks. We find that HL-LHC measurements of these couplings compatible with current experimental bounds may point to a scale that can be explored at the HL-LHC or a next-generation collider. Our approach is completely model-independent: we assume only that there are no light degrees of freedom below the scale of new physics, and allow arbitrary values for the infinitely many couplings beyond the SM as long as they are in agreement with current measurements. We also extend and clarify the methodology of this analysis, and show that if the scale of new physics is above the TeV scale, then the deviations can be described by the leading higher-dimension gauge invariant operator, as in the SM effective field theory.

43 PARTICLE ACCELERATORS↗
DOE OSTI2017

Classification without labels: learning from mixed samples in high energy physics

Modern machine learning techniques can be used to construct powerful models for difficult collider physics problems. In many applications, however, these models are trained on imperfect simulations due to a lack of truth-level information in the data, which risks the model learning artifacts of the simulation. In this paper, we introduce the paradigm of classification without labels (CWoLa) in which a classifier is trained to distinguish statistical mixtures of classes, which are common in collider physics. Crucially, neither individual labels nor class proportions are required, yet we prove that the optimal classifier in the CWoLa paradigm is also the optimal classifier in the traditional fully-supervised case where all label information is available. After demonstrating the power of this method in an analytical toy example, we consider a realistic benchmark for collider physics: distinguishing quark- versus gluon-initiated jets using mixed quark/gluon training samples. More generally, CWoLa can be applied to any classification problem where labels or class proportions are unknown or simulations are unreliable, but statistical mixtures of the classes are available.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Approximating the linear response of physical chaos

Abstract Parametric derivatives of statistics are highly desired quantities in prediction, design optimization and uncertainty quantification. In the presence of chaos, the rigorous computation of these quantities is certainly possible, but mathematically complicated and computationally expensive. Based on Ruelle’s formalism, this paper shows that the sophisticated linear response algorithm can be dramatically simplified in higher-dimensional systems featuring statistical homogeneity in the physical space. We argue that the contribution of the SRB (Sinai–Ruelle–Bowen) measure gradient, which is an integral yet the most cumbersome part of the full algorithm, is negligible if the objective function is appropriately aligned with unstable manifolds. This abstract condition could potentially be satisfied by a vast family of real-world chaotic systems, regardless of the physical meaning and mathematical form of the objective function and perturbed parameter. We demonstrate several numerical examples that support these conclusions and that present the use and performance of a simplified linear response algorithm. In the numerical experiments, we consider physical models described by differential equations, including Lorenz 96 and Kuramoto–Sivashinsky.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Universal Method of Modeling Linear Stationary Physical Fields

The aim of this paper is to develop a method, described earlier, of solving quantum-mechanical problems into a universal numerical method of modeling fields of various physical nature. This method is based on reducing the initial equation of mathematical physics describing a given physical field to a simpler inhomogeneous equation with a known fundamental solution. This equation is then transformed into an inhomogeneous integral equation with a kernel expressed in terms of the known fundamental solution. The obtained integral equation with boundary conditions is solved numerically. To confirm the efficiency of the proposed numerical method, a two-dimensional and a three-dimensional boundary value problem with known solutions have been solved. Another important illustration of the efficiency of the proposed method is the solution of quantum-mechanical problems for one-dimensional and two-dimensional quantum oscillators. It is shown that the considered method allows one to find the energy eigenvalues and the eigenfunctions with acceptable accuracy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Lattice Physics Calculations Using the Embedded Self-Shielding Method in Polaris, Part I: Methods and Implementation

Polaris is a 2-dimensional multigroup lattice physics capability in the SCALE code system for the analysis of light water reactor fuel designs. The goal of light water reactor lattice physics codes is to generate few-group homogenized cross sections for downstream full-core nodal diffusion calculations. Additionally, lattice physics calculations contain three primary components: the cross section processing calculation, the 2D transport calculation, and the depletion calculation. This paper summarizes the calculational methods and their implementation into Polaris, with an emphasis on implementation of the embedded self-shielding method. The accuracy of the embedded self-shielding method depends on the procedure used to generate self-shielding factors on the multigroup library. Numerical benchmarks calculations reveal that the accuracy of Polaris eigenvalue predictions is enhanced by (1) using heterogeneous unit cell models to generate the self-shielding factors on the library and (2) using self-shielding factors for within-group scattering cross section.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

Inherent limits on optimization and discovery in physical systems

Topological mapping of a large physical system on a graph, and its decomposition using universal measures are proposed. We find inherent limits to the potential for optimization of a given system and its approximate representations by motifs, and the ability to reconstruct the full system given approximate representations. The approximate representation of the system most suited for optimization may be different from that which most accurately describes the full system. - Highlights: • Graph-based approach to analyze full and partial representations of physical systems. • Direct link between a change in the physical system and complexity of the graph. • Inherent limits to the potential for optimization of a general system. • Inherent limits to the reconstruction of full system from partial representations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Physics and the choice of regulators in functional renormalisation group flows

The Renormalisation Group is a versatile tool for the study of many systems where scale-dependent behaviour is important. Its functional formulation can be cast into the form of an exact flow equation for the scale-dependent effective action in the presence of an infrared regularisation. The functional RG flow for the scale-dependent effective action depends explicitly on the choice of regulator, while the physics does not. In this work, we systematically investigate three key aspects of how the regulator choice affects RG flows: (i) We study flow trajectories along closed loops in the space of action functionals varying both, the regulator scale and shape function. Such a flow does not vanish in the presence of truncations. Based on a definition of the length of an RG trajectory, we suggest a constructive procedure for devising optimised regularisation schemes within a truncation. (ii) In systems with various field variables, a choice of relative cutoff scales is required. At the example of relativistic bosonic two-field models, we study the impact of this choice as well as its truncation dependence. We show that a crossover between different universality classes can be induced and conclude that the relative cutoff scale has to be chosen carefully for a reliable description of a physical system. (iii) Non-relativistic continuum models of coupled fermionic and bosonic fields exhibit also dependencies on relative cutoff scales and regulator shapes. At the example of the Fermi polaron problem in three spatial dimensions, we illustrate such dependencies and show how they can be interpreted in physical terms. - Highlights: • We show the regulator dependence of functional renormalisation (FRG) group equations. • Solving FRG equations along closed loops provides a distance between two regulators. • We suggest a practical procedure for devising optimal regulators. • A universality class crossover can be induced by changing relative cutoff scales. • We examine the regulator dependence explicitly in the Fermi polaron problem.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Comment on “The Mössbauer rotor experiment and the general theory of relativity” [Ann. Physics 368 (2016) 258–266]

Highlights: •Our approach is considered from the viewpoint of arbitrary moving continuum. •Continuum characteristics are determined by a deformation tensor, a stress tensor, a strain velocity tensor, a spin tensor (Sing (1963)) and the first curvature vectors of the world lines of medium particles. •All these physical values are located on the hypersurfaces orthogonal to the world lines of medium particles and specify the “physical” space. •The time counting from the “physical” space permits to obtain the red shift of the frequency at the end of the tube that coincides with Corda result (Corda (2016)).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Physics of the Inverted Harmonic Oscillator: From the lowest Landau level to event horizons

In this work, we present the inverted harmonic oscillator (IHO) Hamiltonian as a paradigm to understand the quantum mechanics of scattering and time-decay in a diverse set of physical systems. As one of the generators of area preserving transformations, the IHO Hamiltonian can be studied as a dilatation generator, squeeze generator, a Lorentz boost generator, or a scattering potential. In establishing these different forms, we demonstrate the physics of the IHO that underlies phenomena as disparate as the Hawking–Unruh effect and scattering in the lowest Landau level (LLL) in quantum Hall systems. We derive the emergence of the IHO Hamiltonian in the LLL in a gauge invariant way and show its exact parallels with the Rindler Hamiltonian that describes quantum mechanics near event horizons. This approach of studying distinct physical systems with symmetries described by isomorphic Lie algebras through the emergent IHO Hamiltonian enables us to reinterpret geometric response in the lowest Landau level in terms of relativistic effects such as Wigner rotation. Further, the analytic scattering matrix of the IHO points to the existence of quasinormal modes (QNMs) in the spectrum, which have quantized time-decay rates. We present a way to access these QNMs through wave packet scattering, thus proposing a novel effect in quantum Hall point contact geometries that parallels those found in black holes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2011

Physical properties of mesenchymal stem cells are coordinated by the perinuclear actin cap

Highlights: {yields} Cell thickness and stiffness of rat MSC are inversely correlated. {yields} Perinuclear actin cap coordinates the cell thickness and stiffness of rat MSC. {yields} Physical properties of rat MSCs regulate their proliferation activity. {yields} Physical properties of MSCs are potent indicators for their physiological functions. -- Abstract: Mesenchymal stem cells (MSCs) have been extensively investigated for their applications in regenerative medicine. Successful use of MSCs in cell-based therapies will rely on the ability to effectively identify their properties and functions with a relatively non-destructive methodology. In this study, we measured the surface stiffness and thickness of rat MSCs with atomic force microscopy and clarified their relation at a single-cell level. The role of the perinuclear actin cap in regulating the thickness, stiffness, and proliferative activity of these cells was also determined by using several actin cytoskeleton-modifying reagents. This study has helped elucidate a possible link between the physical properties and the physiological function of the MSCs, and the corresponding regulatory role of the actin cytoskeleton.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Envisioned role of slit bypass pores in physical aging of carbon molecular sieve membranes

Carbon molecular sieve (CMS) hollow fibers remain a special class of rigid, molecular sieving membranes with unique scalability and excellent separation properties for challenging gas-pairs. These high-performing membranes are subject to moderate productivity losses and selectivity gains over time via a phenomenon typically referred to as physical aging. This article considers the role of slit bypass pores in physical aging as sensed by membrane property changes of CMS derived from aromatic polyimide precursors. Such pores are thought to be edge imperfections between adjacent ultramicroporous plates that may tighten over time, causing a reduction in penetrant diffusion coefficients. The envisioned mechanism leading to such slit bypass pores and their effects on penetrant transport are considered here from a fundamental perspective. Significantly, these slit bypass pores can be manipulated to suppress physical aging in CMS hollow fiber membranes, therefore enabling attractive long-term separation properties for multiple challenging applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Respecting causality for training physics-informed neural networks

While the popularity of physics-informed neural networks (PINNs) is steadily rising, to this date PINNs have not been successful in simulating dynamical systems whose solution exhibits multi-scale, chaotic or turbulent behavior. In this work we attribute this shortcoming to the inability of existing PINNs formulations to respect the spatio-temporal causal structure that is inherent to the evolution of physical systems. We argue that this is a fundamental limitation and a key source of error that can ultimately steer PINN models to converge towards erroneous solutions. We address this pathology by proposing a simple re-formulation of PINNs loss functions that can explicitly account for physical causality during model training. We demonstrate that this simple modification alone is enough to introduce significant accuracy improvements, as well as a practical quantitative mechanism for assessing the convergence of a PINNs model. Here we provide state-of-the-art numerical results across a series of benchmarks for which existing PINNs formulations fail, including the chaotic Lorenz system, the Kuramoto–Sivashinsky equation in the chaotic regime, and the Navier–Stokes equations. To the best of our knowledge, this is the first time that PINNs have been successful in simulating such systems, introducing new opportunities for their applicability to problems of industrial complexity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Physics-guided neural networks with engineering domain knowledge for hybrid process modeling

As neural networks are more frequently used to solve problems in science and engineering, the methods used to incorporate scientific knowledge into these networks are becoming increasingly complex. Here, this work breaks down these complicated techniques into a set of basic strategies which can easily be applied to diverse situations. Several novel neural networks are built using the categories laid out in this work. These networks are tested on simulated data from a continuous stirred tank reactor (CSTR) model to evaluate the advantages provided by each network. The three points demonstrated in this work are: (1) architectural hybrid models can speed up convergence and reduce the amount of data necessary to train a model; (2) adding a physics-guided loss function can improve model generalization and make models more physically consistent; (3) using physics-guided initialization and transfer learning improves accuracy and speeds up convergence, but can harm generalizability if used incorrectly.

42 ENGINEERING↗
DOE OSTI2022

Woven ceramic matrix composite surrogate model based on physics-informed recurrent neural network

A recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear constitutive behavior of woven ceramic matrix composites (CMCs) driven by matrix damage at multiple length scales. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the multiscale generalized method of cells (MSGMC) approach coupled with a matrix damage model. This coupling permits simulating the nonlinear behavior of woven CMCs based on constituent response at the micro-, meso-, and macroscales. The multiscale repeating unit cell is loaded under non-monotonic conditions including multiple load / unload cycles and tension / compression. The fiber volume fraction as well as the intra- and intertow void volume fractions are also varied in the generation of training data. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input strain sequence and fiber and void volume fractions, the resulting stress versus strain response while satisfying physical constraints such as positive semi-definiteness of the tangent stiffness matrix and linear elastic unloading. Further, the trained surrogate model effectively matches the stress versus strain response and successfully predicts the tangent modulus throughout the loading regime. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex architectures, nonlinear multiaxial material response, and under non-monotonic loading conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Physics-informed heterogeneous graph neural networks for DC blocker placement

The threat of geomagnetic disturbances (GMDs) to the reliable operation of the bulk energy system has spurred the development of effective strategies for mitigating their impacts. One such approach involves placing transformer neutral blocking devices, which interrupt the path of geomagnetically induced currents (GICs) to limit their impact. The high cost of these devices and the sparsity of transformers that experience high GICs during GMD events, however, calls for a sparse placement strategy that involves high computational cost. To address this challenge, we developed a physics-informed heterogeneous graph neural network (PIHGNN) for solving the graph-based dc-blocker placement problem. Our approach combines a heterogeneous graph neural network (HGNN) with a physics-informed neural network (PINN) to capture the diverse types of nodes and edges in ac/dc networks and incorporates the physical laws of the power grid. We train the PIHGNN model using a surrogate power flow model and validate it using case studies. Results demonstrate that PIHGNN can effectively and efficiently support the deployment of GIC dc-current blockers, ensuring the continued supply of electricity to meet societal demands. Furthermore, our approach has the potential to contribute to the development of more reliable and resilient power grids capable of withstanding the growing threat that GMDs pose.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

A rock physics and seismic reservoir characterization study of the Rock Springs Uplift, a carbon dioxide sequestration site in Southwestern Wyoming

We present a reservoir geophysics study, including rock physics modeling and seismic inversion, of a carbon dioxide sequestration site in Southwestern Wyoming, namely the Rock Springs Uplift, and build a petrophysical model for the potential injection reservoirs for carbon dioxide sequestration. Our objectives include the facies classification and the estimation of the spatial model of porosity and permeability for two sequestration targets of interest, the Madison Limestone and the Weber Sandstone. The available dataset includes a complete set of well logs at the location of the borehole available in the area, a set of 110 core samples, and a seismic survey acquired in the area around the well. The proposed study includes a formation evaluation analysis and facies classification at the well location, the calibration of a rock physics model to link petrophysical properties and elastic attributes using well log data and core samples, the elastic inversion of the pre-stack seismic data, and the estimation of the reservoir model of facies, porosity and permeability conditioned by seismic inverted elastic attributes and well log data. In particular, the rock physics relations are facies-dependent and include granular media equations for clean and shaley sandstone, and inclusion models for the dolomitized limestone. The permeability model has been computed by applying a facies-dependent porosity-permeability relation calibrated using core sample measurements. Finally, the study shows that both formations show good storage capabilities. The Madison Limestone includes a homogeneous layer of high-porosity high-permeability dolomite; the Weber Sandstone is characterized by a lower average porosity but the layer is thicker than the Madison Limestone.

58 GEOSCIENCES↗
DOE OSTI2018

Physical fractionation of sweet sorghum and forage/energy sorghum for optimal processing in a biorefinery

Sorghum offers great potential as a feedstock for the production of fuels and chemicals from both water-extractable sugars and the cell wall biopolymers, while its within-plant structural and compositional heterogeneity may allow for physical fractionations to tailor feedstock properties to a biorefining process. In this study, the stem internodes of two sorghum (Sorghum bicolor L. Moench) genotypes, a sweet sorghum (‘Della’) and a forage/energy sorghum (‘TX08001’), were first subjected to fractionation by manual classification by stem anatomy and internode proximity to the ground to yield 18 fractions. These fractions exhibited substantial differences in cell wall morphology, composition, and recalcitrance to mild alkaline pretreatment and enzymatic hydrolysis. While the sweet sorghum cultivar held nearly 70% more water-extractable sugar (sucrose, glucose, fructose, starch) in the stems than the forage/energy sorghum hybrid, both cultivars exhibited comparable diversity of composition and these compositions were remarkably similar in similar tissues and stem regions between the two cultivars. The fractions isolated from the pith parenchyma were the least recalcitrant to mild alkaline pretreatment and enzymatic hydrolysis and contained less lignin than fractions isolated from the epidermis, outer and inner rind, and internal vascular bundles. The pith samples isolated from the lowest region of the stem from both cultivars exhibited near-theoretical sugar hydrolysis yields when no pretreatment was employed and exhibited the lowest lignin contents of any of the fractions. Next, a physical fractionation approach approximating a commercial “de-pithing” process utilizing wet disintegration and sieving was applied to the forage/energy sorghum. A pith-rich fraction representing approximately 20% of the extractives-free mass of the stem could be isolated with this approach and, relative to the other fractions, was low in lignin, high in ash, highly hygroscopic, and showed an improved response to mild alkaline pretreatment and enzymatic hydrolysis at low enzyme loadings. Overall, these results demonstrate how heterogeneity within sorghum stems can be exploited using physical fractionation approaches to yield fractions enriched in desired properties that may allow for more streamlined processing.

09 BIOMASS FUELS↗
DOE OSTI2017

Two-scale model for the effect of physical aging in elastomers filled with hard nanoparticles

A two-scale model is developed, and solved numerically, to describe the mechanical behavior of elastomers filled with hard nanoparticles. Of particular interest is the slow recovery of the elastic modulus after large-amplitude oscillatory deformation. To account for this effect, the physical aging of the glassy bridges between the filler particles is captured with two thermal degrees of freedom for the matrix material, namely a kinetic and a configurational one. Formulating the two-scale model enriched with aging in a nonequilibrium thermodynamics context, first results in a constitutive relation for the Cauchy stress tensor. Second, the dynamics of physical aging is described, which eventually results in the slow recovery of the elastic modulus with waiting time. The proposed model is investigated numerically under large amplitude oscillatory shear deformation. Of particular interest in this respect is the coupling of the micro-scale dynamics with the physical aging on the macroscopic scale. This coupling is examined in detail, both in an approximate way using a Gaussian approximation, as well as numerically, under specific conditions. It turns out that the CONNFFESSIT approach (Laso and Öttinger 1993 ) can not be employed for the numerical solution of the model under arbitrary loading conditions because of the novel structure of the two-level coupling term. While a procedure for solving the model numerically for the case of strong applied deformation is presented in this paper, other solution methodologies need to be sought for the cases of weak and no applied deformation.

36 MATERIALS SCIENCE↗