Differential algebraic fast multipole-accelerated boundary element method for nonlinear beam dynamics in arbitrary enclosures
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43 PARTICLE ACCELERATORS↗
SEARCH · Engineering Papers
Results for “Differentiable physics”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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vdisquants: Video Distrometer derived radar equivalent quantities. Retrievals from the VDIS instrument providing radar equivalent quantities, including dual polarization radar quantities (e.g., Z, Differential Reflectivity ZDR).
1. Provide cleaned and filtered data stream from multiple ARM disdrometers; 2. Estimate commonly-used physical parameters of the Drop Size Distribution (DSD). 3. Calculate radar equivalent quantities such as the Radar Reflectivity Factor Z.
54 ENVIRONMENTAL SCIENCES↗
Evolution of a terrestrial magma ocean: Thermodynamics, kinetics, rheology, convection, differentiation
The evolution of an initially totally molten magma ocean is constrained on the basis of analysis of various physical problems in the magma ocean. First of all an equilibrium thermodynamics of the magma ocean is developed in the melting temperature range. The equilibrium thermodynamical parameters are found as functions only of temperature and pressure and are used in the subsequent models of kinetics and convection. Kinematic processes determine the crystal size and also determine a non-equilibrium thermodynamics of the system. Rheology controls all dynamical regimes of the magma ocean. The thermal convection models for different rheological laws are developed for both the laminar convection and for turbulent convection in the case of equilibrium thermodynamics of the multiphase system. The evolution is estimated on the basis of all the above analysis.
Solomatov, V. S.↗
Oxide Acidity Modulates Structural Transformations in Hydrogen Titanates during Electrochemical Li-Ion Insertion
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Saeed, Saeed↗
Test of the Equivalence Principle in an Einstein Elevator
The scientific goal of the experiment is to test the equality of gravitational and inertial mass (i.e., to test the Principle of Equivalence) by measuring the independence of the rate of fall of bodies from their compositions. The measurement is accomplished by measuring the relative displacement (or equivalently acceleration) of two falling bodies of different materials which are the proof masses of a differential accelerometer spinning about an horizontal axis to modulate a possible violation signal. A non-zero differential acceleration appearing at the signal frequency will indicate a violation of the Equivalence Principle. The goal of the experiment is to measure the Eotvos ratio og/g (differential acceleration/common acceleration) with a targeted accuracy that is about two orders of magnitude better than the state of the art (presently at several parts in 10(exp 13). The analyses carried out during this first grant year have focused on: (1) evaluation of possible shapes for the proof masses to meet the requirements on the higher-order mass moment disturbances generated by the falling capsule; (2) dynamics of the instrument package and differential acceleration measurement in the presence of errors and imperfections; (3) computation of the inertia characteristic of the instrument package that enable a separation of the signal from the dynamics-related noise; (4) a revised thermal analysis of the instrument package in light of the new conceptual design of the cryostat; (5) the development of a dynamic and control model of the capsule attached to the gondola and balloon to define the requirements for the leveling mechanism (6) a conceptual design of the leveling mechanism that keeps the capsule aligned before release from the balloon; and (7) a new conceptual design of the customized cryostat and a preliminary valuation of its cost. The project also involves an international cooperation with the Institute of Space Physics (IFSI) in Rome, Italy. The group at IFSI is in charge of prototyping the differential accelerometer and carrying out precursor laboratory measurements. During this grant year, our partners analyzed and then designed a new prototype of differential accelerometer that has several characteristics in common with the flight accelerometer at this point of the instrument development. The highlights of these activities are documented in a section of this report.
Shapiro, Irwin I.↗
Exact enforcement of temporal continuity in sequential physics-informed neural networks
The use of deep learning methods in scientific computing represents a potential paradigm shift in engineering problem solving. One of the most prominent developments is Physics-Informed Neural Networks (PINNs), in which neural networks are trained to satisfy partial differential equations (PDEs). While this method shows promise, the standard version has been shown to struggle in accurately predicting the dynamic behavior of time-dependent problems. To address this challenge, methods have been proposed that decompose the time domain into multiple segments, employing a distinct neural network in each segment and directly incorporating continuity between them in the loss function of the minimization problem. In this work we introduce a method to exactly enforce continuity between successive time segments via a solution ansatz. This hard constrained sequential PINN (HCS-PINN) method is simple to implement and eliminates the need for any loss terms associated with temporal continuity. The method is tested for a number of benchmark problems involving both linear and non-linear PDEs. Examples include various first order time dependent problems in which traditional PINNs struggle, namely advection, Allen–Cahn, and Korteweg–de Vries equations. Furthermore, second and third order time-dependent problems are demonstrated via wave and Jerky dynamics examples, respectively. Notably, the Jerky dynamics problem is chaotic, making the problem especially sensitive to temporal accuracy. Finally, the numerical experiments conducted with the proposed method demonstrated superior convergence and accuracy over both traditional PINNs and the soft-constrained counterparts.
42 ENGINEERING↗
Latent Stochastic Differential Equations for Modeling Quasar Variability and Inferring Black Hole Properties
Quasars are bright and unobscured active galactic nuclei (AGN) thought to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The temporal variability of a quasar’s brightness contains valuable information about its physical properties. The UV/optical variability is thought to be a stochastic process, often represented as a damped random walk described by a stochastic differential equation (SDE). Upcoming wide-field telescopes such as the Rubin Observatory Legacy Survey of Space and Time (LSST) are expected to observe tens of millions of AGN in multiple filters over a ten year period, so there is a need for efficient and automated modeling techniques that can handle the large volume of data. Latent SDEs are machine learning models well suited for modeling quasar variability, as they can explicitly capture the underlying stochastic dynamics. In this work, we adapt latent SDEs to jointly reconstruct multivariate quasar light curves and infer their physical properties such as the black hole mass, inclination angle, and temperature slope. Our model is trained on realistic simulations of LSST ten year quasar light curves, and we demonstrate its ability to reconstruct quasar light curves even in the presence of long seasonal gaps and irregular sampling across different bands, outperforming a multioutput Gaussian process regression baseline. Our method has the potential to provide a deeper understanding of the physical properties of quasars and is applicable to a wide range of other multivariate time series with missing data and irregular sampling.
79 ASTRONOMY AND ASTROPHYSICS↗
First Differential Measurement of the Single 𝜋 + Production Cross Section in Neutrino Neutral-Current Scattering
Since its first observation in the 1970s, neutrino-induced neutral-current single positive pion production (NC1𝜋 + ) has remained an elusive and poorly understood interaction channel. This process is a significant background in neutrino oscillation experiments and studying it further is critical for the physics program of next-generation accelerator-based neutrino oscillation experiments. In this Letter, we present the first double-differential cross-section measurement of NC1𝜋 + interactions using data from the ND280 detector of the T2K experiment collected in 𝜈-beam mode. The measured flux-averaged integrated cross section is 𝜎 = (6.07 ± 1.22) × 10 −41 cm 2 /nucleon. We compare the results on a hydrocarbon target to the predictions of several neutrino interaction generators and final-state-interaction models. While model predictions agree with the differential results, the data show a weak preference for a cross-section normalization approximately 30% higher than predicted by most models studied in this Letter.
Neutrino detection↗
Data-driven, multi-moment fluid modeling of Landau damping
Deriving governing equations of complex physical systems based on first principles can be quite challenging when there are certain unknown terms and hidden physical mechanisms in the systems. In this work, we apply a deep learning architecture to learn fluid partial differential equations (PDEs) of a plasma system based on the data acquired from a fully kinetic model. Here, the learned multi-moment fluid PDEs are demonstrated to incorporate kinetic effect such as Landau damping. Based on the learned fluid closure, the data-driven, multi-moment fluid modeling can well reproduce all the physical quantities derived from the fully kinetic model. The calculated damping rate of Landau damping is consistent with both the fully kinetic simulation and the linear theory. The data-driven fluid modeling of PDEs for complex physical systems may be applied to improve the fluid closure and reduce the computational cost of multi-scale modeling of global systems.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
First Measurement of $𝜈_𝑒$ and $\bar{𝜈}_𝑒$ Charged-Current Single Charged-Pion Production Differential Cross Sections on Argon Using the MicroBooNE Detector
Understanding electron neutrino interactions is crucial for measurements of neutrino oscillations and searches for new physics in neutrino experiments. We present the first measurement of the flux-averaged $𝜈_𝑒$ + $\bar{𝜈}_𝑒$ charged-current single charged-pion production cross section on argon using the MicroBooNE detector and data from the NuMI neutrino beam. The total cross section is measured to be [0.93 ± 0.13(stat) ± 0.27(syst)] × 10 −39 cm 2 /nucleon at a mean $𝜈_𝑒$ + $\bar{𝜈}_𝑒$ energy of 730 MeV. Differential cross sections are also reported in electron energy, electron and pion angles, and electron-pion opening angle.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Control of micromachined deformable mirrors
A micromachined deformable mirror with pixelated electrostatic actuators is proposed. The paper begins with a physical description of the proposed mirror. Then a mathematical model in the form of a nonlinear partial differential equation describing the mirror surface deformations is derived. This model is used to derive the required voltages for the actuators to achieve a specified static deformation of the mirror surface. This is followed by the derivation of a static nonlinear feedback controller for achieving noninteracting actuation. Then the structure for a complete control system for wavefront correction is proposed. The paper concludes with a discussion of the physical implementation of the proposed control system.
Agronin, M. L.↗
Control of Micromachined Deformable Mirrors
A micromachined deformable mirror with pixelated electrostatic actuators is proposed. The paper begins with a physical description of the proposed mirror. Then, a mathematical model in the form of a nonlinear partial differential equation describing the mirror surface deformations is derived. This model is used to derive the required voltages for the actuators to achieve a specified static deformation of the mirror surface. This is followed by the derivation of a static nonlinear feedback controller for achieving noninteracting actuation. Then, the structure for a complete control system for wavefront correction is proposed. The paper concludes with a discussion of the physical implementation of the proposed control system.
mirrors↗
Field Work Proposal ERKJ358: Black-box training for scientific machine learning models (Final Report)
The overarching goal of this project is to develop a scalable black-box training capability for scientific machine learning (SciML) problems that are non-trainable with existing automatic differentiation (AD)-based algorithms. AD assumes that a loss function can be decomposed into a sequence of elementary operations whose derivatives are known. This assumption is violated when the loss function includes a black-box physical model (e.g., a legacy simulator). The current strategy, converting a black-box simulator to an AD-enabled code via differential programming, is inflexible and time-, labor-consuming. Thus, black-box optimization is a main workhorse for training SciML models, e.g., in scientific reinforcement learning, hyper-parameter fine tuning, designing SciML models with adversarial robustness, etc.
97 MATHEMATICS AND COMPUTING↗
Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning
Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces. In this work, we develop a differentiable geometric deep learning (GDL) model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation and optimization towards fast charging battery electrolytes. In particular, we extend mixture thermodynamic and transport laws by creating GDL-learnable physical coefficients. We evaluate our model with mixture thermodynamics and ion transport properties, where we show improved prediction accuracy and model robustness of Diff-Mix than its purely data-driven variants. Furthermore, with a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps, via differentiable optimization built on DiffMix gradients. By combining GDL, mixture physics laws, and robotic experimentation, DiffMix expands the predictive modeling methods for chemical mixtures and enables efficient optimization in large chemical spaces.
25 - ENERGY STORAGE↗
Massive right-handed neutrinos in $\bar{B} \to D^* τ\bar X$ decay
We explore signatures of a massive right-handed neutrino (RHN) in angular distributions of $\bar{B} \to D^* (\to D π) τ(\to πν_τ) \bar X$ decays, where $X$ is an invisible state. We assume the new physics is described by the standard model effective field theory extended with an RHN in the MeV-GeV mass range. We calculate for the first time the full differential distributions in terms of the visible final states, including the decay of the $τ$ lepton. We evaluate the sensitivity of various distributions to the new physics operators.
FOS: Physical sciences↗
Differential Deposition to Correct Surface Figure Deviations in Astronomical Grazing-Incidence X-Ray Optics
A coating technique is being developed to correct the surface figure deviations in reflective-grazing-incidence X-ray optics. These optics are typically designed to have precise conic profiles, and any deviation in this profile, as a result of fabrication, results in a degradation of the imaging performance. To correct the mirror profiles, physical vapor deposition has been utilized to selectively deposit a filler material inside the mirror shell. The technique, termed differential deposition, has been implemented as a proof of concept on miniature X-ray optics developed at MSFC for medical-imaging applications. The technique is now being transferred to larger grazing-incidence optics suitable for astronomy and progress to date is reported.
Kilaru, Kiranmayee↗
Magnetic Helicity Condensation and the Solar Cycle
Solar filaments exhibit a global chirality pattern where dextral/sinistral filaments, corresponding to negative/ positive magnetic helicity, are dominant in the northern/southern hemisphere. This pattern is opposite to the sign of magnetic helicity injected by differential rotation along east–west oriented polarity inversion lines, posing a major conundrum for solar physics. A resolution of this problem is offered by the magnetic helicity-condensation model of Antiochos. To investigate the global consequences of helicity condensation for the hemispheric chirality pattern, we apply a temporally and spatially averaged statistical approximation of helicity condensation. Realistic magnetic field configurations in both the rising and declining phases of the solar cycle are simulated. For the helicity-condensation process, we assume convective cells consisting of positive/negative vorticities in the northern/southern hemisphere that inject negative/positive helicity. The magnitude of the vorticity is varied as a free parameter, corresponding to different rates of helicity injection. To reproduce the observed percentages of dominant and minority filament chiralities, we find that a vorticity of magnitude 2.5 x 10(exp −6)/s is required. This rate, however, is insufficient to produce the observed unimodal profile of chirality with latitude. To achieve this, a vorticity of at least 5 x 10(exp −6)/s is needed. Our results place a lower limit on the small-scale helicity injection required to dominate differential rotation and reproduce the observed hemispheric pattern. Future studies should aim to establish whether the helicity injection rate due to convective flows and/or flux emergence across all latitudes of the Sun is consistent with our results.
Mackay, Duncan H.↗
Differential analysis of incompressibility in neutron-rich nuclei
Both the incompressibility K A of a finite nucleus of mass A and that (K ∞ ) of infinite nuclear matter are fundamentally important for many critical issues in nuclear physics and astrophysics. While some consensus has been reached about K ∞ , accurate theoretical predictions and experimental extractions of K τ characterizing the isospin dependence of K A have been very difficult. We propose a differential approach to extract K τ and K ∞ independently from the K A data of any two nuclei in a given isotope chain. Applying this method to the K A data from isoscalar giant monopole resonances (ISGMR) in even-even Pb, Sn, Cd, and Ca isotopes taken by Garg et al. at the Research Center for Nuclear Physics (RCNP), Osaka University, Japan, we find that the 106 Cd– 116 Cd and 112 Sn– 124 Sn pairs having the largest differences in isospin asymmetries in their respective isotope chains measured so far provide consistently the most accurate up-to-date K τ value of K τ = –616 ± 59 MeV and K τ =–623 ± 86 MeV, respectively, largely independent of the remaining uncertainties of the surface and Coulomb terms in expanding K A , while the K ∞ values extracted from different isotopes chains are all well within the current uncertainty range of the community consensus for K ∞ . Moreover, the size and origin of the “soft Sn puzzle” is studied with respect to the “stiff Pb phenomenon.” Furthermore, it is found that the latter is favored due to a much larger (by ≈ 380 MeV) K τ for Pb isotopes than for Sn isotopes, while K ∞ from analyzing the K A data of Sn isotopes is only about 5 MeV less than that from analyzing the Pb data.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗