inverse-cnf
Inverse machine learning model using Conditional Normalizing Flow (CNF)
DeBardeleben, Nathan Andrew [Los Alamos National L↗
SEARCH · Engineering Papers
Results for “Inverse”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
A Bayesian Approach to Regional Decadal Predictability: Sparse Parameter Estimation in High-Dimensional Linear Inverse Models of High-Latitude Sea Surface Temperature Variability
Stochastic reduced models are an important tool in climate systems whose many spatial and temporal scales cannot be fully discretized or underlying physics may not be fully accounted for. One form of reduced model, the linear inverse model (LIM), has been widely used for regional climate predictability studies—typically focusing more on tropical or midlatitude studies. However, most LIM fitting techniques rely on point estimation techniques deriving from fluctuation–dissipation theory. In this methodological study we explore the use of Bayesian inference techniques for LIM parameter estimation of sea surface temperature (SST), to quantify the skillful decadal predictability of Bayesian LIM models at high latitudes. We show that Bayesian methods, when compared to traditional point estimation methods for LIM-type models, provide better calibrated probabilistic skill, while simultaneously providing better point estimates due to the regularization effect of the prior distribution in high-dimensional problems. We compare the effect of several priors, as well as maximum likelihood estimates, on 1) estimating parameter values on a perfect model experiment and 2) producing calibrated 1-yr SST anomaly forecast distributions using a preindustrial control run of the Community Earth System Model (CESM). Finally, we employ a host of probabilistic skill metrics to determine the extent to which an LIM can forecast SST anomalies at high latitudes. We find that the choice of prior distribution has an appreciable impact on estimation outcomes, and priors that emphasize physically relevant properties enhance the model’s ability to capture variability of SST anomalies.
54 ENVIRONMENTAL SCIENCES↗
Gas hydrate quantification in Walker Ridge block 313, Gulf of Mexico, from full-waveform inversion of ocean-bottom seismic data
The Gulf of Mexico (GOM) Joint Industry Project Leg 2 logging-while-drilling data in Walker Ridge lease block 313 (WR313) in the GOM detected gas hydrate in coarse- and fine-grained sediments at sites WR313-G and WR313-H. The coarse-grained units are thin ([Formula: see text]) and highly saturated, whereas the fine-grained unit is thick (approximately 200 m) with low saturation and fracture-filling gas hydrate. Unlike its coarse-grained counterpart, the seismic character of the fine-grained unit does not clearly indicate the presence of gas hydrate, which would likely have remained undiscovered in the absence of drilling. In this paper, through frequency-domain acoustic full-waveform inversion (FWI) of ocean-bottom seismometer data along a 2D multichannel seismic transect near sites WR313-G and WR313-H, we detect and quantify gas hydrate in the fine-grained unit. Key results are as follows: First, the base of the gas hydrate stability zone, which is not obvious in the reflection profile, can be discerned in the FWI results. Second, the gas hydrate in the fine-grained unit is mainly confined to the area between two sets of opposite-dipping normal faults implying that the fault architecture may be partially responsible for this gas hydrate accumulation and distribution.
Geochemistry & Geophysics↗
Neural-adjoint method for the inverse design of all-dielectric metasurfaces
All-dielectric metasurfaces exhibit exotic electromagnetic responses, similar to those obtained with metal-based metamaterials. Research in all-dielectric metasurfaces currently uses relatively simple unit-cell designs, but increased geometrical complexity may yield even greater scattering states. Although machine learning has recently been applied to the design of metasurfaces with impressive results, the much more challenging task of finding a geometry that yields a desired spectra remains largely unsolved. We propose and demonstrate a method capable of finding accurate solutions to ill-posed inverse problems, where the conditions of existence and uniqueness are violated. A specific example of finding the metasurface geometry which yields a radiant exitance matching the external quantum efficiency of gallium antimonide is demonstrated. We also show how the neural-adjoint method can intelligently grow the design search space to include designs that increasingly and accurately approximate the desired scattering response. The neural-adjoint method is not restricted to the case demonstrated and may be applied to plasmonics, photonic crystal, and other artificial electromagnetic materials.
42 ENGINEERING↗
Emulating the Deutsch-Josza algorithm with an inverse-designed terahertz gradient-index lens
An all-dielectric photonic metastructure is investigated for application as a quantum algorithm emulator (QAE) in the terahertz frequency regime; specifically, we show implementation of the Deustsh-Josza algorithm. The design for the QAE consists of a gradient-index (GRIN) lens as the Fourier transform subblock and patterned silicon as the oracle subblock. First, we detail optimization of the GRIN lens through numerical analysis. Then, we employed inverse design through a machine learning approach to further optimize the structural geometry. Through this optimization, we enhance the interaction of the incident light with the metamaterial via spectral improvements of the outgoing wave.
Blackwell, Ashley N.↗
Toward inverse backscatter absorption gas imaging: imaging a dry gas plume with ambient H 2 O absorption
This paper introduces inverse backscatter absorption gas imaging (iBAGI), a transparent gas imaging technique based on absorption spectroscopy of gases naturally present in ambient air being displaced by transparent gases. We demonstrate reconstruction of a 2-D image of a dry gas plume by measuring light of a raster-scanned diode laser tuned to an absorption line of atmospheric H 2 O. We quantify the performance depending on the distance to the backdrop and show that the key transmission metric is independent of absorption path length. We conclude with an outlook toward calibration-free, single-ended iBAGI using scattering from low-reflectivity backdrops.
Klug, Joseph C.↗
Predictable third harmonic generation in GaAs metasurfaces through group theory inverse design of meta-atoms
Here we report the group theory-based inverse design of meta-atoms for a dielectric metasurface in GaAs with predictable optical linear response and third harmonic generation (THG). Six sharp Fano resonances have been observed with a corresponding polarization dependence as predicted by group theory and the meta-atom’s symmetry in the D2h point group. THG has been observed for two modes under x-polarization excitation and one mode for y-polarization, in agreement with theoretical symmetry predictions. The polarization-dependent THG aspect ratio was observed to reach as high as 108. Through strategic structural or symmetry-preserving perturbations, it was shown that the THG can be enhanced or reduced by a factor of 7. The highest THG conversion efficiency was estimated to be 3.1×10-7 at the pump intensity of 1.51 MW/cm2. This high THG conversion efficiency indicates that our group theory approach to modal engineering opens a new path towards optical nonlinearity tuning in dielectric metasurfaces.
Aoueille, Andrew↗
Mode-multiplexed photonic integrated vector dot-product core from inverse design
Photonic computing has the potential to harness the full degrees of freedom (DOFs) of the light field, including the wavelength, spatial mode, spatial location, phase quadrature, and polarization, to achieve a higher level of computing parallelism and scalability than digital electronic processors. While multiplexing using the wavelength and other DOFs can be readily integrated on silicon photonics platforms with compact footprints, conventional mode-division multiplexed (MDM) photonic designs occupy areas exceeding tens to hundreds of microns for a few spatial modes, significantly limiting their scalability. Here, we utilize inverse design to demonstrate an ultracompact photonic computing core that calculates vector dot products based on MDM coherent mixing. Our dot-product core integrates the functionalities of two-mode multiplexers and one multimode coherent mixer within a nominal footprint of 5 μm x 3 μm . We have experimentally demonstrated computing examples on the fabricated dot-product core, including complex number multiplication and motion estimation using optical flow. The compact dot-product core design enables large-scale on-chip integration in a parallel photonic computing primitive cluster for high-throughput scientific computing and computer vision tasks.
97 MATHEMATICS AND COMPUTING↗
Hawaii Play Fairway Analysis: Lanai Resistivity and Density 3D Inversion Models
To prepare for its third phase, the Hawaii Play Fairway project conducted groundwater sampling and analyses in ten locations in the Hawaiian islands, magnetotelluric (MT) and gravity surveys, as well as calculations of 3D subsurface stress due to the weight of the rock underlying the topography of the volcano. The subsurface stresses were used to evaluate the potential for fracture-induced permeability. Inversions of the MT and gravity data produce 3D models of resistivity and density, respectively, on Lanai, across Haleakala's SW rift (Maui), and surrounding Mauna Kea (Hawaii Island). The project developed and applied a new method for incorporating depth information about resistivity, density, and potential for fracture-induced permeability into the statistical method for computing resource probability in these three focus areas. The project then incorporated the new groundwater results with the new geophysical results and the calculations of potential for fracture-induced permeability to produce updated maps of resource probability and confidence. These results were used to identify target sites for exploratory drilling. Spreadsheet information: Each sheet contains data for a particular depth in kilometers. Positive depths are above sea level, and negative below. For more information, go to the Hawaii Groundwater and Geothermal Resources Center website linked in the resources.
15 GEOTHERMAL ENERGY↗
Double exponential approximation and inverse double exponential fit for Bell Labs and International-Military Standard EMP waveforms
We summarize here the double exponential and inverse double exponential approximations for two common EMP waveforms, the Bell Laboratories (Bell Labs) and the International- military standard (IEC-MIL-STD). Both models have been used frequently due to their relatively easy analytical expressions for both the time domain waveforms and their associated frequency domain spectra.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Multichannel Deconvolution of Vibrational Shock Signals: An Inverse Filtering Approach
When transporting critical systems of national security interest, out-of-the ordinary, impulsive events that can potentially be undetected and affect overall system performance are of great concern. Impulsive events that can occur are essentially pulse-like, transient signals of short duration that evolve from various phenomena. Here the event can be created by either the dropping of a test object, the system, subjecting it to a compact high-energy blow or being struck unintentionally during transit resulting in potential damage. The intensity and location of the strike can cause an inoperability condition that is unacceptable in a national security environment. Therefore, it is essential to detect, classify and localize damage of any test object subjected to an impulsive-event. This effort was targeted to evaluate the vibrational response of test objects that are subjected to “transport” shocks and roadway vibrations during shipping and handling. Any potential damage that could be inflicted during transportation must not only be detected, but also be evaluated to determine the operational readiness of a test object before and after transport. This event is a critical task that must be addressed as part of the Lawrence Livermore National Laboratory (LLNL) national security mission. The estimation of excitation signals from noisy data is termed the deconvolution problem in the signal processing literature. The deconvolution problem is based on recovering the input excitation signal from a system characterized by its impulse response sequence. Using this model of the system, an “inverse” representation or filter is developed to remove the system from the measured data and recover the input. Deconvolution techniques have existed for a long-time; however, transient deconvolution presents a few uncommon problems, since the signal has a finite-length time duration resulting in a limited amount of data containing information about the excitation process. The transient is wideband in the frequency domain relative to any measurement sensor implying that the smaller bandwidth sensor system “filters” the excitation eliminating some of its essential information for recovery. This fact, coupled with the filtering effect of the test object itself makes this excitation recovery (deconvolution) problem a challenge for signal processing.
42 ENGINEERING↗
Extreme Scale Infrasound Inversion and Prediction for Weather Characterization and Acute Event Detection
Accurate and timely weather predictions are critical to many aspects of society with a profound impact on our economy, general well-being, and national security. In particular, our ability to forecast severe weather systems is necessary to avoid injuries and fatalities, but also important to minimize infrastructure damage and maximize mitigation strategies. The weather community has developed a range of sophisticated numerical models that are executed at various spatial and temporal scales in an attempt to issue global, regional, and local forecasts in pseudo real time. The accuracy however depends on the time period of the forecast, the nonlinearities of the dynamics, and the target spatial resolution. Significant uncertainties plague these predictions including errors in initial conditions, material properties, data, and model approximations. To address these shortcomings, a continuous data collection occurs at an effort level that is even larger than the modeling process. It has been demonstrated that the accuracy of the predictions depends on the quality of the data and is independent to a certain extent on the sophistication of the numerical models. Data assimilation has become one of the more critical steps in the overall weather prediction business and consequently substantial improvements in the quality of the data would have transformational benefits. This paper describes the use of infrasound inversion technology, enabled through exascale computing, that could potentially achieve orders of magnitude improvement in data quality and therefore transform weather predictions with significant impact on many aspects of our society.
58 GEOSCIENCES↗
Bound Constrained Partial DifferentialEquation Inverse Problem Solution by theSemi-Smooth Newton Method
We present the mathematical derivation, software implementation details, and computational results for a semi-smooth Newton method applied to two inverse problems governed by partial differential equations with bound constraints. The two problems share mathematical structural similarities to density-based topology optimization problems. The semi-smooth Newton method provides a mesh independent solution computation for the two test problems. A key step is that the complementarity part of the necessary optimality conditions are reformulated with the use of a complementarity functionφsuch that the complementarity conditions are satisfied if and only if a zero of a nonsmooth function has been obtained. The modular finite element package MFEM is utilized for the software implementation. In addition we constructed a matrix-free Operator to enable the use of efficient Krylov subspace IterativeSolver of MFEM for the solution of our two target problems.
97 MATHEMATICS AND COMPUTING↗
Report on the LLNL Global Full-waveform Inversion Workflow and Progress
LLNL has developed the SPiRaL global seismic tomography model based upon millions of body wave travel times and surface wave dispersion curves in the period range of 25-200 seconds (Simmons et al., 2021). The motivation to construct the SPiRaL model (which stands for S-, P-, Rayleigh, and Love waves) is based upon the guidance provided by a 3-D model workshop held in Berkeley in 2007 and documented in a paper by Zucca et al. (2009). It was concluded in Zucca et al. (2009) that a “practical and obtainable” model is one that predicts body wave travel times for event location while also predicting surface wave velocities for moment tensor estimation. We have largely followed that guidance which has led to the construction of SPiRaL. Looking forward, we now seek to improve the global model through full waveform inversion (FWI). This necessitates the development/customization of an efficient and flexible modeling workflow to automate the process using LLNL’s supercomputing platforms. This report briefly outlines the work that being performed at LLNL to develop the necessary tools and framework to update the SPiRaL global model with full waveform information.
58 GEOSCIENCES↗
Approximating and incorporating model uncertainty in an inversion for seismic source functions: Preliminary results
We present preliminary work on propagating model uncertainty into the estimation of the time domain source time functions of the seismic source. Our method is based on an estimated model covariance function, which we estimate from the data. The model covariance function is then used to construct a suite of surrogate Greens functions which we use in a Monte Carlo type inversion scheme. The result is a probability density function of the six independent source time functions, each of which corresponds to an individual component of the seismic moment tensor. We compare the results of our method with those obtained using a computationally expensive finite difference Monte Carlo method and find that our new method produces results that are deficient in low frequencies. The advantage of our new method, which we term the Karhunen-Loeve Monte Carlo (KLMC) method, is that is several orders of magnitude faster than our current method, which uses a finite difference scheme to produce the suite of forward models.
42 ENGINEERING↗
A laser inverse Compton scattering diagnostic to study runaway electron dynamics during tokamak disruptions
Both technical and simulation progress has been made toward implementing a laser inverse Compton scattering diagnostic on the DIII-D tokamak. This is a novel diagnostic which uses a short (sub-ns) pulse laser together with a fast (sub-ns) gated x-ray detector to measure relativistic electrons inside a tokamak plasma. The study of these relativistic electrons is important to make sure they can be removed from any tokamak-based power reactor, as they could damage vessel walls and cooling lines. This diagnostic is completely new for magnetic fusion research and has never been implemented before on a tokamak. It is found that good signals and radial profiles could be obtained with an optimized high-energy (7 Joule) laser. Using a standard off-the-shelf Nd:YAG (0.7 Joule) laser would give some signals above the noise but no ability to measure the relativistic electron radial profile.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Toward Global Regional Seismic Moment Tensor Inversion with Three-Dimensional Earth Models for Nuclear Explosion Monitoring with Sparse Networks: Demonstration of Reciprocity for Strain Greens Tensor Database Simulation with Salvus
Seismic source characterization is an essential function of global nuclear explosion monitoring (NEM). While large events (roughly with moment magnitude, M w , greater than 5.0) can often be easily detected, located and identified with high signal-to-noise ratios at teleseismic distances (> 20°), trends in NEM research require confident source characterization at much lower magnitudes (say down to 3.0) and exploitation of sparse observations (from only a few stations) at regional distance (< 20°). Regional distance waveform inversion to characterize sources is now widely used and effective (e.g. Ford et al., 2009; Alvizuri and Tape, 2018; Alvizuri et al., 2018; Chiang et al., 2018; Ford et al., 2022). These methods obtain the magnitude, depth and seismic moment tensor, which represents the forces that excited the observed seismic waves (slip on an earthquake fault, explosion, collapse or a combination of various forces). Common to many problems in seismology, the isolation of the source 2 properties requires removal of path propagation effects that waves experience while traveling through the three-dimensional (3D) Earth (the structure exists due to different rock types, material properties, temperature and tectonic processes).
58 GEOSCIENCES↗
Inverse Reinforcement Learning based Bayesian Goal Inference Method for Early Nuclear Proliferation Detection
Traditional methods for detection of nuclear proliferation indicators are usually applied after nuclear proliferation has already occurred. There is a need to advance these methods to perform early detection of nuclear proliferation indicators. In this project, we formulated an early detection problem as a sequential, decision-making, goal inference problem based on research publications of authors, to determine whether it is possible to infer whether an author will publish on a research activity before it has occurred. To develop and test our approach, we selected a civil nuclear activity for our case study. We constructed a state-action-state transition graph from publications of authors associated with the activity and the co-authors of their publications, using titles, abstracts, and author publication sequences. We then used inverse reinforcement learning to model the goal-directed behavior of authors in trajectories that terminate at selected goal states. Using a Bayesian formulation, we computed the probability that authors would reach each selected state from partially observed trajectories of their state transitions in their research topic space. The state with the highest probability was selected as the most probable goal state. Based on our results, we found that 60% of the times we can infer the correct goal state early; sometimes the inference is either delayed, or multiple states could be inferred as goal states. Overall, our results show that it is possible to perform early detection of research activities of authors in a nuclear technology area. Further research is necessary to establish a more accurate understanding of how topic modeling, topic space grid discretization, and the extent of overlap among trajectories of different goal states, affect the goal inference results. The methods developed in this work may be used to enhance data-driven methods for early detection of nuclear proliferation indicators.
97 MATHEMATICS AND COMPUTING↗