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Results for “trajectory inference”

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NASA NTRS1982

Plasma observations near Saturn - Initial results from Voyager 2

Results of plasma measurements made by Voyager 2 in the vicinity of Saturn are discussed and compared with those made by Pioneer 11 and Voyager 1 in a more limited range of latitudes. The initial bow shock crossing on the inbound trajectory closely agreed with the shock position inferred from the external ram pressure in the solar wind, although boundaries on the outbound pass were much further out than expected. Magnetospheric plasma observations reveal the presence of (1) shocked solar wind plasma in the magnetosheath between 30 and 22 Saturn radii; (2) a variable density region between 17 Saturn radii and the magnetopause; (3) an extended thick plasma sheet between 17 and 7 Saturn radii; and (4) an inner plasma torus probably originating from local sources. The ratio of heavy to light ions was observed to vary with distance to the equatorial plane in the dayside magnetosphere, with the heavy ions, probably O(+), more closely confined to the equatorial plane. The plasma data also account for the observed inner boundary of the neutral hydrogen torus discovered by Voyager 1.

Bridge, H. S.↗

NASA NTRS2017

Fast Optimization for Aircraft Descent and Approach Trajectory

We address problem of on-line scheduling of the aircraft descent and approach trajectory. We formulate a general multiphase optimal control problem for optimization of the descent trajectory and review available methods of its solution. We develop a fast algorithm for solution of this problem using two key components: (i) fast inference of the dynamical and control variables of the descending trajectory from the low dimensional flight profile data and (ii) efficient local search for the resulting reduced dimensionality non-linear optimization problem. We compare the performance of the proposed algorithm with numerical solution obtained using optimal control toolbox General Pseudospectral Optimal Control Software. We present results of the solution of the scheduling problem for aircraft descent using novel fast algorithm and discuss its future applications.

Luchinsky, Dmitry G.↗

DOE OSTI2023

Ultrabroadband flying-focus using an axiparabola-echelon pair

Flying-focus pulses promise to revolutionize laser-driven secondary sources by decoupling the trajectory of the peak intensity from the native group velocity of the medium over distances much longer than a Rayleigh range. Previous demonstrations of the flying focus have either produced an uncontrolled trajectory or a trajectory that is engineered using chromatic methods that limit the duration of the peak intensity to picosecond scales. Here we demonstrate a controllable ultrabroadband flying focus using a nearly achromatic axiparabola-echelon pair. Spectral interferometry using an ultrabroadband superluminescent diode was used to measure designed super- and subluminal flying-focus trajectories and the effective temporal pulse duration as inferred from the measured spectral phase. The measurements demonstrate that a nearly transform- and diffraction-limited moving focus can be created over a centimeter-scale—an extended focal region more than 50 Rayleigh ranges in length. This ultrabroadband flying-focus and the novel axiparabola-echelon configuration used to produce it are ideally suited for applications and scalable to >100 TW peak powers.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Street context of various demographic groups in their daily mobility

Abstract We present an urban science framework to characterize phone users’ exposure to different street context types based on network science, geographical information systems (GIS), daily individual trajectories, and street imagery. We consider street context as the inferred usage of the street, based on its buildings and construction, categorized in nine possible labels. The labels define whether the street is residential, commercial or downtown, throughway or not, and other special categories. We apply the analysis to the City of Boston, considering daily trajectories synthetically generated with a model based on call detail records (CDR) and images from Google Street View. Images are categorized both manually and using artificial intelligence (AI). We focus on the city’s four main racial/ethnic demographic groups (White, Black, Hispanic and Asian), aiming to characterize the differences in what these groups of people see during their daily activities. Based on daily trajectories, we reconstruct most common paths over the street network. We use street demand (number of times a street is included in a trajectory) to detect each group’s most relevant streets and regions. Based on their street demand, we measure the street context distribution for each group. The inclusion of images allows us to quantitatively measure the prevalence of each context and points to qualitative differences on where that context takes place. Other AI methodologies can further exploit these differences. This approach presents the building blocks to further studies that relate mobile devices’ dynamic records with the differences in urban exposure by demographic groups. The addition of AI-based image analysis to street demand can power up the capabilities of urban planning methodologies, compare multiple cities under a unified framework, and reduce the crudeness of GIS-only mobility analysis. Shortening the gap between big data-driven analysis and traditional human classification analysis can help build smarter and more equal cities while reducing the efforts necessary to study a city’s characteristics.

Salgado, Ariel (ORCID:0000000177015372)↗

NASA NTRS2004

Combining Satellite Data, Trajectory Modeling and Surface Insolation Measurements to Deduce the Direct Radiative Effect of Smoke Aerosol

In this paper, we have introduced a method of inferring the radiative effect of smoke aerosols using a technique that combines satellite remote sensing with trajectory modeling. The results shown here clearly show large flux biases between theoretical and measured radiative fluxes correlate with the arrival of smoke aerosol to the area. Further analysis is required to convincingly demonstrate that the reason for these differences is the radiative effect of the smoke aerosols. To do this, the estimated fluxes taken from the ERA-15 will be recomputed every 3 hours using International Satellite Cloud Climatology Project (ISCCP) data set entitled DX gridded to a 1o equal angle resolution (see paper 7B.2 for details). Surface radiometric and ancillary data for several more Canadian surface sites are being obtained at minute temporal resolution. The ultimate purpose of this research is to derive aerosol smoke maps for fire events such as this to be included in an aerosol climatology and be incorporated in the computation of the earth's surface radiation budget to better understand the radiative effect of aerosols.

Stackhouse, Paul W., Jr.↗

DOE OSTI2022

Ion energy distribution in an electron beam ion trap inferred from simulations of the trapped ion cloud

For this work, we have inferred the energy distribution of trapped ions in an electron beam ion trap (EBIT) from simulations of the spatial distribution of Fe 13+ ions and a comparison with measured visible light images of the ion cloud. We simulated the cloud of Fe 13+ ions by computing ion trajectories in the EBIT for different ion energy distributions used to initialize the trajectories. We then performed a least-squares fit to infer the ion energy distribution that best reproduced the measured ion cloud. These best-fit distributions were typically non-Maxwellian. For electron beam energies of 395–475 eV and electron beam currents of 1–9 mA, we find that the average ion energy is in the range of 10–300 eV. We also find that the average ion energy increases with increasing beam current approximately as $\langle$E$\rangle$ ≈ 25 I e eV, where I e is the electron beam current in mA. We have also compared our results to Maxwell-Boltzmann-distribution ion clouds. We find that our best-fit non-thermal distributions have an $\langle$E$\rangle$ that is less than half that of the T from the best-fit Maxwell-Boltzmann distributions ($\langle$E$\rangle$/q)/T=0.41 ± 0.05.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2025

Physics-tailored machine learning reveals unexpected physics in dusty plasmas

Dusty plasma is a mixture of ions, electrons, and macroscopic charged particles that is commonly found in space and planetary environments. The particles interact through Coulomb forces mediated by the surrounding plasma, and as a result, the effective forces between particles can be nonconservative and nonreciprocal. Machine learning (ML) models are a promising route to learn these complex forces, yet their structure should match the underlying physical constraints to provide useful insight. Here, we demonstrate and experimentally validate an ML approach that incorporates physical intuition to infer force laws in a laboratory dusty plasma. Trained on 3D particle trajectories, the model accounts for inherent symmetries, nonidentical particles, and learns the effective nonreciprocal forces between particles with exquisite accuracy (R 2 > 0.99). We validate the model by inferring particle masses in two independent yet consistent ways. The model’s accuracy enables precise measurements of particle charge and screening length, identifying large deviations from common theoretical assumptions. Our ability to identify unknown physics from experimental data demonstrates how ML-powered approaches can guide new routes of scientific discovery in many-body systems. Furthermore, we anticipate our ML approach to be a starting point for inferring laws from dynamics in a wide range of many-body systems, from colloids to living organisms.

Science & Technology - Other Topics↗

NASA NTRS1990

Real-time fuzzy inference based robot path planning

This project addresses the problem of adaptive trajectory generation for a robot arm. Conventional trajectory generation involves computing a path in real time to minimize a performance measure such as expended energy. This method can be computationally intensive, and it may yield poor results if the trajectory is weakly constrained. Typically some implicit constraints are known, but cannot be encoded analytically. The alternative approach used here is to formulate domain-specific knowledge, including implicit and ill-defined constraints, in terms of fuzzy rules. These rules utilize linguistic terms to relate input variables to output variables. Since the fuzzy rulebase is determined off-line, only high-level, computationally light processing is required in real time. Potential applications for adaptive trajectory generation include missile guidance and various sophisticated robot control tasks, such as automotive assembly, high speed electrical parts insertion, stepper alignment, and motion control for high speed parcel transfer systems.

Pacini, Peter J.↗

DOE OSTI2020

Annihilation-to-nothing: a quantum gravitational boundary condition for the Schwarzschild black hole

The interior of a static Schwarzschild metric can be written in terms of two functions, similar to some models of anisotropic cosmology. With a suitable choice of canonical variables, we solve the Wheeler-DeWitt equation (WDW) inside the horizon of a Schwarzschild black hole. By imposing classicality near the horizon, and requiring boundedness of the wave function, we get a rather generic solution of the WDW equation, whose steepest-descent solution, i.e., the ridge of the wave function, coincides nicely with the classical trajectory. However, there is an ambiguity in defining the arrow of time which leads to two possible interpretations—(i) if there is only one arrow of time, one can infer that the steepest-descent of the wave function follows the classical trajectory throughout: coming from the event horizon and going all the way down to the singularity, while (ii) if there are two different arrows of time in two separate regimes, it can be inferred that the steepest-descent of the wave function comes inwards from the event horizon in one region while it moves outwards from the singularity in the other region, and there exists an annihilation process of these two parts of the wave function inside the horizon. Additionally, adopting the second interpretation could shed light on the information loss paradox: as time goes on, probabilities for histories that include black holes and singularities decay to zero and eventually only trivial geometries dominate.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Operator inference with roll outs for learning reduced models from scarce and low-quality data

Data-driven modeling has become a key building block in computational science and engineering. However, data that are available in science and engineering are typically scarce, often polluted with noise and affected by measurement errors and other perturbations, which makes learning the dynamics of systems challenging. Here, in this work, we propose to combine data-driven modeling via operator inference with the dynamic training via roll outs of neural ordinary differential equations. Operator inference with roll outs inherits interpretability, scalability, and structure preservation of traditional operator inference while leveraging the dynamic training via roll outs over multiple time steps to increase stability and robustness for learning from low-quality and noisy data. Numerical experiments with data describing shallow water waves and surface quasi-geostrophic dynamics demonstrate that operator inference with roll outs provides predictive models from training trajectories even if data are sampled sparsely in time and polluted with noise of up to 10%.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Analysis of streaked images of x-ray self-emission in laser-driven spherical implosions

Imaging of x-ray self-emission provides a powerful in situ measurement of the spatial and temporal evolution of high-energy-density plasmas. However, interpretation of these measurements requires detailed understanding of the data-generating process. This work presents a case study in the interpretation of x-ray self-emission data for the specific application of streaked one-dimensional slit imaging of spherical laser-driven implosions. A comprehensive generative model of the streaked slit-imaging diagnostic is developed including detailed treatments of the radiation transfer, photometrics, and photostatistics associated with the measurement. The model is used to generate realistic synthetic streaked images and to analyze experimental streaked images to extract important physical quantities of interest. An example analysis of streaked images from implosion experiments on the OMEGA laser is presented, where the model developed in this work is used to constrain the trajectory and peak velocity of the implosion using Bayesian inference.

Bayesian inference↗

NASA NTRS2008

Boundary Layer Transition on X-43A

The successful Mach 7 and 10 flights of the first fully integrated scramjet propulsion systems by the Hyper-X (X-43A) program have provided the means with which to verify the original design methodologies and assumptions. As part of Hyper-X s propulsion-airframe integration, the forebody was designed to include a spanwise array of vortex generators to promote boundary layer transition ahead of the engine. Turbulence at the inlet is thought to provide the most reliable engine design and allows direct scaling of flight results to groundbased data. Pre-flight estimations of boundary layer transition, for both Mach 7 and 10 flight conditions, suggested that forebody boundary layer trips were required to ensure fully turbulent conditions upstream of the inlet. This paper presents the results of an analysis of the thermocouple measurements used to infer the dynamics of the transition process during the trajectories for both flights, on both the lower surface (to assess trip performance) and the upper surface (to assess natural transition). The approach used in the analysis of the thermocouple data is outlined, along with a discussion of the calculated local flow properties that correspond to the transition events as identified in the flight data. The present analysis has confirmed that the boundary layer trips performed as expected for both flights, providing turbulent flow ahead of the inlet during critical portions of the trajectory, while the upper surface was laminar as predicted by the pre-flight analysis.

Berry, Scott↗

NASA NTRS2010

Physics-Based Methods of Failure Analysis and Diagnostics in Human Space Flight

The Integrated Health Management (IHM) for the future aerospace systems requires to interface models of multiple subsystems in an efficient and accurate information environment at the earlier stages of system design. The complexity of modern aeronautic and aircraft systems (including e.g. the power distribution, flight control, solid and liquid motors) dictates employment of hybrid models and high-level reasoners for analysing mixed continuous and discrete information flow involving multiple modes of operation in uncertain environments, unknown state variables, heterogeneous software and hardware components. To provide the information link between key design/performance parameters and high-level reasoners we rely on development of multi-physics performance models, distributed sensors networks, and fault diagnostic and prognostic (FD&P) technologies in close collaboration with system designers. The main challenges of our research are related to the in-flight assessment of the structural stability, engine performance, and trajectory control. The main goal is to develop an intelligent IHM that not only enhances components and system reliability, but also provides a post-flight feedback helping to optimize design of the next generation of aerospace systems. Our efforts are concentrated on several directions of the research. One of the key components of our strategy is an innovative approach to the diagnostics/prognostics based on the real time dynamical inference (DI) technologies extended to encompass hybrid systems with hidden state trajectories. The major investments are into the multiphysics performance modelling that provides an access of the FD&P technologies to the main performance parameters of e.g. solid and liquid rocket motors and composite materials of the nozzle and case. Some of the recent results of our research are discussed in this chapter. We begin by introducing the problem of dynamical inference of stochastic nonlinear models and reviewing earlier results. Next, we present our analytical approach to the solution of this problem based on the path integral formulation. The resulting algorithm does not require an extensive global search for the model parameters, provides optimal compensation for the effects of dynamical noise, and is robust for a broad range of dynamical models. In the following Section the strengths of the algorithm are illustrated illustrated by inferring the parameters of the stochastic Lorenz system and comparing the results with those of earlier research. Next, we discuss a number of recent results in application to the development of the IHM for aerospace system. Firstly, we apply dynamical inference approach to a solution of classical three tank problems with mixed unknown continuous and binary parameters. The problem is considered in the context of ground support system for filling fuel tanks of liquid rocket motors. It is shown that the DI algorithm is well suited for successful solution of a hybrid version of this benchmark problem even in the presence of additional periodic and stochastic perturbation of unknown strength. Secondly, we illustrate our approach by its application to an analysis of the nozzle fault in a solid rocket motor (SRM). The internal ballistics of the SRM is modelled as a set of one-dimensional partial differential equations coupled to the dynamics of the propellant regression. In this example we are specifically focussed on the inference of discrete and continuous parameters of the nozzle blocking fault and on the possibility of an application of the DI algorithm to reducing the probability of "misses" of an on-board FD&P for SRM. In the next section re-contact problem caused by first stage/upper stage separation failure is discussed. The reaction forces imposed on the nozzle of the upper stage during the re-contact and their connection to the nozzle damage and to the thrust vector control (TVC) signal are obtained. It is shown that transient impact induced torquean be modelled as a response of an effective damped oscillator. A possible application of the DI algorithm to the inference of damage parameters and predicting fault dynamics ahead of time using the actuator signal is discussed. Finally, we formulate Bayesian inferential framework for development of the IHM system for in-flight structural health monitoring (SHM) of composite materials. We consider the signal generated by piezoelectric actuator mounted on composite structure generating elastic waves in it. The signal received by the sensor is than compared with the baseline signal. The possibility of damage inference is discussed in the context of development of the SHM.

Smelyanskiy, Vadim N.↗

DOE OSTI2026

Learning turbulent flows with generative models for super resolution and sparse flow reconstruction

Neural operators are promising surrogates for dynamical systems but when trained with standard L 2 losses they tend to oversmooth fine-scale turbulent structures. Here, we show that combining operator learning with generative modeling overcomes this limitation. We consider three practical turbulent-flow challenges where conventional neural operators fail: spatio-temporal super-resolution, forecasting, and sparse flow reconstruction. For Schlieren jet super-resolution, an adversarially trained neural operator (adv-NO) reduces the energy-spectrum error by 15 × while preserving sharp gradients at neural operator-like inference cost. For 3D homogeneous isotropic turbulence, adv-NO trained on only 160 timesteps from a single trajectory forecasts accurately for five eddy-turnover times and offers 114 × wall-clock speed-up at inference than the baseline diffusion-based forecasters, enabling near-real-time rollouts. For reconstructing cylinder wake flows from highly sparse Particle Tracking Velocimetry-like inputs, a conditional generative model infers full 3D velocity and pressure fields with correct phase alignment and statistics. These advances enable accurate reconstruction and forecasting at low compute cost, bringing near-real-time analysis and control within reach in experimental and computational fluid mechanics.

Fluid dynamics↗

DOE OSTI2021

(U) A Theoretical Study of Asay Foil Trajectories (V.02)

We consider the trajectory of a generic Asay foil ejecta momentum diagnostic sensor, for scenarios where ejecta are produced at a planar surface and fly ballistically through a perfect vacuum to the sensor. To do so, we build upon a previously established mathematical formalism derived for the analytic study of stationary sensors (i.e., piezopins). First, we derive the momentum conservation equation for the problem, in a form amenable to accelerating sensors, in terms of a generic ejecta source areal mass function (“source model”). This defines an integro-differential equation (IDE) for the foil trajectory. When ejecta production is instantaneous - as is generally assumed in momentum diagnostic data analyses - the IDE leads to an implicit and easily calculable closed-form solution for the foil trajectory in a perfect system, as long as the ejecta particle velocity distribution is twice-integrable. General properties of the instant-production trajectory solution indicate the existence of a boundary condition the particle velocity distribution must satisfy in order for the analytically predicted foil trajectories to be compatible with certain features commonly observed in foil data. This boundary condition is identical to one derived previously from a consideration of piezopin data. Armed with the analytic solution for instant production, we also consider various techniques used to extract time-dependent cumulative ejecta masses from foil trajectories, and derive an expression for the error imposed by using an approximated equation of motion. This analytic trajectory solution furthermore makes it possible to examine the common practice of presenting inferred cumulative ejecta masses as a function of a normalized implied velocity; we derive conditions under which this methodology is and is not meaningful. We also propose a strategy for extending the instant-production trajectory solution to time-dependent source functions.

42 ENGINEERING↗

NASA NTRS2011

Thoughts Without Content are Empty, Intuitions Without Concepts are Blind - Determinism and Contingency Revisited

Was the emergence of life a predictable outcome of chemical evolution on earth? Could evolution produce life very different from ours? These are one of the oldest questions in the field of the origin of life that not only have broad philosophical implications but also impact how we approach the problem from the methodological standpoint. Framing the issue in terms of the dichotomy between contingency and determinism is not a fortunate because these two terms in their conventional meaning are neither mutually exclusive nor jointly exhaustive. Determinism, represented in natural sciences by Newtonian physics, relies on the assumption that every event is causally determined by a chain of previous events. In the context of the origin of life it means that once the initial conditions on the early earth have been specified further evolution follows inevitably. Considering uncertainties about conditions on the prebiotic earth, many plausible sets of initial conditions can be defined, each followed by a separate deterministic trajectory. This conventional understanding of determinism does not admit contingency. Further, it has no implications for evaluating how many sets of initial conditions lead to the emergence of life. It appears that a better framing of the problem is as follows: given plausible sets of initial conditions on the early earth how probable and broadly spread are evolutionary trajectories that lead to life? Instead of undertaking an impossible task of specifying microscopic initial conditions for all components of the system one uses a reduced representation of this system and specify only a small set of essential macroscopic parameters, values (or ranges of values) of which can be identified, inferred or estimated from experiment, theory or historical record. The following evolutionary trajectories are still governed by laws of physics and chemistry but become probabilistic and "contingency" is admitted as variations in other variables in the system. A similar reasoning is common in other fields of science, for example in statistical mechanics. Some trajectories lead to life, perhaps in different forms, whereas others do not. Of our true interest is the ratio of these two outcomes. The issue of determinism does not directly enter the picture. The debate about the likelihood of the emergence of life is quite old. One view holds that the origin of life is an event governed by chance, and the result of so many random events (contingencies) is unpredictable. This view was eloquently expressed by Monod. In his book "Chance or Necessity" he argued that life was a product of "nature's roulette." In an alternative view, expressed in particular by deDuve and Morowitz, the origin of life is considered a highly probable or even inevitable event (although its details need not be determined in every respect). Only in this sense the origin of life can be considered a "deterministic event".

Pohorille, Andrew↗

DOE OSTI2024

Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model

The brain produces diverse functions, from perceiving sounds to producing arm reaches, through the collective activity of populations of many neurons. Determining if and how the features of these exogenous variables (e.g., sound frequency, reach angle) are reflected in population neural activity is important for understanding how the brain operates. Often, high-dimensional neural population activity is confined to low-dimensional latent spaces. However, many current methods fail to extract latent spaces that are clearly structured by exogenous variables. This has contributed to a debate about whether or not brains should be thought of as dynamical systems or representational systems. Here, we developed a new latent process Bayesian regression framework, the orthogonal stochastic linear mixing model (OSLMM) which introduces an orthogonality constraint amongst time-varying mixture coefficients, and provide Markov chain Monte Carlo inference procedures. We demonstrate superior performance of OSLMM on latent trajectory recovery in synthetic experiments and show superior computational efficiency and prediction performance on several real-world benchmark data sets. We primarily focus on demonstrating the utility of OSLMM in two neural data sets: μ ECoG recordings from rat auditory cortex during presentation of pure tones and multi-single unit recordings form monkey motor cortex during complex arm reaching. We show that OSLMM achieves superior or comparable predictive accuracy of neural data and decoding of external variables (e.g., reach velocity). Most importantly, in both experimental contexts, we demonstrate that OSLMM latent trajectories directly reflect features of the sounds and reaches, demonstrating that neural dynamics are structured by neural representations. Together, these results demonstrate that OSLMM will be useful for the analysis of diverse, large-scale biological time-series datasets.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

I Know I'm Right, But Does My Phone?

Transportation is the largest source of green-house gas emissions in the United States. Reducing transportation emissions depends on human travel behavior, which relies on local land use and planning. Travel diaries, consisting of sequences of trips between places for a particular individual, are typically used to instrument human travel behavior. However, these diaries are only as accurate as the underlying methods used to construct them. Travel diary algorithms have been a popular research topic since the advent of GPS tracking surveys. Mode inference algorithms in particular have been well represented in literature. However, these algorithms have typically been validated using prompted recall of pre-segmented trips, which doesn't account for segmentation error, thus disregarding the continuity of mode inference. Furthermore, phone operating systems and applications have adopted battery-conserving techniques, but we are not aware of prior work that has characterized the resulting data collection errors or evaluated procedures to mitigate them. We introduce a framework to evaluate accuracy of trip length computations and mode inference. We develop a temporal alignment procedure in analyzing continuous mode-segmented trajectories for groups of trips. We then apply our framework to evaluate an example set of travel diary algorithms from the open-source OpenPATH travel diary platform against MobilityNet, a public dataset containing information from three artificial timelines that cover 15 different travel modes. Our results show that inference based on an integration with map features results in weighted F_1 scores of 0.60 (iOS) and 0.74 (android). We also show that OpenPATH tends to under count trip length, with mean of signed relative error of -0.0438 on android and -0.0704 on iOS. We hope that other travel diary algorithms will be evaluated using this standardized process, and that the results used to understand and improve the state-of-the-art in this field.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗