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DOE OSTI2021

Epistatic Net allows the sparse spectral regularization of deep neural networks for inferring fitness functions

Abstract Despite recent advances in high-throughput combinatorial mutagenesis assays, the number of labeled sequences available to predict molecular functions has remained small for the vastness of the sequence space combined with the ruggedness of many fitness functions. While deep neural networks (DNNs) can capture high-order epistatic interactions among the mutational sites, they tend to overfit to the small number of labeled sequences available for training. Here, we developed Epistatic Net (EN), a method for spectral regularization of DNNs that exploits evidence that epistatic interactions in many fitness functions are sparse. We built a scalable extension of EN, usable for larger sequences, which enables spectral regularization using fast sparse recovery algorithms informed by coding theory. Results on several biological landscapes show that EN consistently improves the prediction accuracy of DNNs and enables them to outperform competing models which assume other priors. EN estimates the higher-order epistatic interactions of DNNs trained on massive sequence spaces-a computational problem that otherwise takes years to solve.

Aghazadeh, Amirali (ORCID:0000000302230873)↗

DOE OSTI2021

Computing the shape gradient of stellarator coil complexity with respect to the plasma boundary

Coil complexity is a critical consideration in stellarator design. The traditional two-step optimization approach, in which the plasma boundary is optimized for physics properties and the coils are subsequently optimized to be consistent with this boundary, can result in plasma shapes which cannot be produced with sufficiently simple coils. To address this challenge, we propose a method to incorporate considerations of coil complexity in the optimization of the plasma boundary. Coil complexity metrics are computed from the current potential solution obtained with the REGCOIL code (Landreman, Nucl. Fusion , vol. 57, 2017, 046003). While such metrics have previously been included in derivative-free fixed-boundary optimization (Drevlak et al. , Nucl. Fusion , vol. 59, 2018, 016010), we compute the local sensitivity of these metrics with respect to perturbations of the plasma boundary using the shape gradient (Landreman & Paul, Nucl. Fusion , vol. 58, 2018, 076023). We extend REGCOIL to compute derivatives of these metrics with respect to parameters describing the plasma boundary. In keeping with previous research on winding surface optimization (Paul et al. , Nucl. Fusion , vol. 58, 2018, 076015), the shape derivatives are computed with a discrete adjoint method. In contrast with the previous work, derivatives are computed with respect to the plasma surface parameters rather than the winding surface parameters. To further reduce the resolution required to compute the shape gradient, we present a more efficient representation of the plasma surface which uses a single Fourier series to describe the radial distance from a coordinate axis and a spectrally condensed poloidal angle. This representation is advantageous over the standard cylindrical representation used in the VMEC code (Hirshman & Whitson, Phys. Fluids , vol. 26, 1983, pp. 3553–3568), as it provides a uniquely defined poloidal angle, eliminating a null space in the optimization of the plasma surface. In comparison with previous spectral condensation methods (Hirshman & Breslau, Phys. Plasmas , vol. 5, 1998, p. 2664), the modified poloidal angle is obtained algebraically rather than through the solution of a nonlinear optimization problem. The resulting shape gradient highlights features of the plasma boundary that are consistent with simple coils and can be used to couple coil and fixed-boundary optimization.

Physics↗

NASA NTRSDate not supplied

A New, Efficient, and Consistent Method for Generating Climate Data Record from Operational Hyperspectral Sounder Instruments on AQUA, S-NPP and NOAA 20

Operational IR sounders such AIRS on NASA Aqua, CrIS on S-NPP and on NOAA 20 satellites provide high quality hyperspectral measurements for weather and climate applications. Climate products are typically derived by performing spatial and temporal averaging of level-2 products. It is a time-consuming process to generate level-2 data products since modern hyperspectral satellite sensors have millions of observations each day with thousands of spectral channels for each observation. Additionally, differences in level-2 retrieval algorithms for different satellite sensors can lead to errors in the climate products. We have developed a Climate Fingerprinting Sounder Product (ClimFiSP), which is derived from spatiotemporally averaged level-1 hyperspectral radiances directly. The ClimFiSP algorithm uses consistent radiative kernels and a robust spectral fingerprinting method. It can provide fast and accurate data fusion products from multiple satellite sensors. We have applied this method to both AIRS and CrIS (on SNPP and on NOAA 20) data and generated two decades climate data records for atmospheric temperature, water vapor, cloud, trace gases, and surface skin temperature. The ClimFiSP are being transitioned to NASA data centers for routine generations level-3 products.

Xu Liu↗

NASA NTRS1989

Nonprincipal plane scattering of flat plates and pattern control of horn antennas

Using the geometrical theory of diffraction, the traditional method of high frequency scattering analysis, the prediction of the radar cross section of a perfectly conducting, flat, rectangular plate is limited to principal planes. Part A of this report predicts the radar cross section in nonprincipal planes using the method of equivalent currents. This technique is based on an asymptotic end-point reduction of the surface radiation integrals for an infinite wedge and enables nonprincipal plane prediction. The predicted radar cross sections for both horizontal and vertical polarizations are compared to moment method results and experimental data from Arizona State University's anechoic chamber. In part B, a variational calculus approach to the pattern control of the horn antenna is outlined. The approach starts with the optimization of the aperture field distribution so that the control of the radiation pattern in a range of directions can be realized. A control functional is thus formulated. Next, a spectral analysis method is introduced to solve for the eigenfunctions from the extremal condition of the formulated functional. Solutions to the optimized aperture field distribution are then obtained.

Balanis, Constantine A.↗

DOE OSTI2022

Spectral quadrature for the first principles study of crystal defects: Application to magnesium

In this work, we present an accurate and efficient finite-difference formulation and parallel implementation of Kohn-Sham Density (Operator) Functional Theory (DFT) for non periodic systems embedded in a bulk environment. Specifically, employing non-local pseudopotentials, local reformulation of electrostatics, and truncation of the spatial Kohn-Sham Hamiltonian, and the Linear Scaling Spectral Quadrature method to solve for the pointwise electronic fields in real-space and the non-local component of the atomic force, we develop a parallel finite difference framework suitable for distributed memory computing architectures to simulate non-periodic systems embedded in a bulk environment. Choosing examples from magnesium-aluminum alloys, we first demonstrate the convergence of energies and forces with respect to spectral quadrature polynomial order, and the width of the spatially truncated Hamiltonian. Next, we demonstrate the parallel scaling of our framework, and show that the computation time and memory scale linearly with respect to the number of atoms. Next, we use the developed framework to simulate isolated point defects and their interactions in magnesium-aluminum alloys. Our findings conclude that the binding energies of divacancies, Al solute-vacancy and two Al solute atoms are anisotropic and are dependent on cell size. Furthermore, the binding is favorable in all three cases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1995

Automated spectral classification and the GAIA project

Two dimensional spectral types for each of the stars observed in the global astrometric interferometer for astrophysics (GAIA) mission would provide additional information for the galactic structure and stellar evolution studies, as well as helping in the identification of unusual objects and populations. The classification of the large quantity generated spectra requires that automated techniques are implemented. Approaches for the automatic classification are reviewed, and a metric-distance method is discussed. In tests, the metric-distance method produced spectral types with mean errors comparable to those of human classifiers working at similar resolution. Data and equipment requirements for an automated classification survey, are discussed. A program of auxiliary observations is proposed to yield spectral types and radial velocities for the GAIA-observed stars.

Lasala, Jerry↗

DOE OSTI2022

Maximum Likelihood Spectrum Decomposition for Isotope Identification and Quantification

A spectral decomposition method has been implemented to identify and quantify isotopic source terms in high-resolution gamma-ray spectroscopy in static geometry and shielding scenarios. Monte Carlo simulations were used to build the response matrix of a shielded high-purity germanium detector monitoring an effluent stream with a Marinelli configuration. The decomposition technique was applied to a series of calibration spectra taken with the detector using a multi-nuclide standard. These results are compared with decay-corrected values from the calibration certificate. For most nuclei in the standard ( 241 Am, 109 Cd, 137 Cs, and 60 Co), the deviations from the certificate values were generally no more than 6% with a few outliers as high as 10%. Furthermore, for 57 Co, the radionuclide with the lowest activity, the deviations from the standard reached as high as 25%, driven by the meager statistics in the calibration spectra. In addition, a complete treatment of error propagation for the technique is presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS2003

Gust Acoustic Response of a Single Airfoil Using the Space-Time CE/SE Method

A 2D parallel Euler code based on the space-time conservation element and solution element (CE/SE) method is validated by solving the benchmark problem I in Category 3 of the Third CAA Workshop. This problem concerns the acoustic field generated by the interaction of a convected harmonic vortical gust with a single airfoil. Three gust frequencies, two gust configurations, and three airfoil geometries are considered. Numerical results at both near and far fields are presented and compared with the analytical solutions, a frequency-domain solver GUST3D solutions, and a time-domain high-order Discontinuous Spectral Element Method (DSEM) solutions. It is shown that the CE/SE solutions agree well with the GUST3D solution for the lowest frequency, while there are discrepancies between CE/SE and GUST3D solutions for higher frequencies. However, the CE/SE solution is in good agreement with the DSEM solution for these higher frequencies. It demonstrates that the CE/SE method can produce accurate results of CAA problems involving complex geometries by using unstructured meshes.

Scott, James↗

NASA NTRS2002

Science Objectives of EOS-Aura's Ozone Monitoring Instrument (OMI)

OMI is a UV/VIS nadir solar backscatter spectrograph, which provides near global coverage in one day with a spatial resolution of 13 x 24 sq km. OMI is a new instrument, with a heritage from the European satellite instruments GOME, GOMOS and SCIAMACHY. OMI's unique capabilities for measuring important trace gases with a small footprint and daily global coverage, in conjunction with the other Aura instruments, will make a major contribution to our understanding of stratospheric and tropospheric chemistry and climate change. OMI will measure solar irradiance and Earth radiances in the wavelength range of 270 to 500 nm with spectral resolution of about 0.5 nm and a spectral sampling of about 2-3 per FWHM. From these observations, total columns of O3, NO2, BrO and SO2 will be derived from the back-scattered solar radiance using differential absorption spectroscopy (DOAS). The TOMS total ozone record will also be continued by employing the well established TOMS algorithm. Because of the high accuracy and spatial resolution of the measurements, a good estimate of tropospheric amounts of ozone and NO2 are expected. Ozone profiles will be derived using the optimal estimation method. The spectral aerosol optical depth will be determined from measurements between 340 and 500 nm. This will provide information on aerosol concentration, aerosol size distribution and aerosol type. This wavelength range makes it possible to retrieve aerosol information over both land and sea. OMI observations will also allow retrievals of cloud coverage and cloud heights. From these products, the UV-B flux at the surface can then be derived with high spatial resolution.

Levelt, P. F.↗

NASA NTRS1972

Effect of radiometric errors on accuracy of temperature-profile measurement by spectral scanning using absorption-emission pyrometry

The spectral-scanning method may be used to determine the temperature profile of a jet- or rocket-engine exhaust stream by measurements of gas radiation and transmittance, at two or more wavelengths. A single, fixed line of sight is used, using immobile radiators outside of the gas stream, and there is no interference with the flow. At least two sets of measurements are made, each set consisting of the conventional three radiometric measurements of absorption-emission pyrometry, but each set is taken over a different spectral interval that gives different weight to the radiation from a different portion of the optical path. Thereby, discrimination is obtained with respect to location along the path. A given radiometric error causes an error in computed temperatures. The ratio between temperature error and radiometric error depends on profile shape, path length, temperature level, and strength of line absorption, and the absorption coefficient and its temperature dependency. These influence the choice of wavelengths, for any given gas. Conditions for minimum temperature error are derived. Numerical results are presented for a two-wavelength measurement on a family of profiles that may be expected in a practical case of hydrogen-oxygen combustion. Under favorable conditions, the fractional error in temperature approximates the fractional error in radiant-flux measurement.

Buchele, D. R.↗

NASA NTRS1988

Inversion of the Solar Rotation Rate Versus Depth and Latitude

Three differential inversion techniques were used to compute the internal solar rotation rate from several sets of n-averaged frequency splittings. An iterative variation of the spectral expansion method; the optimal averaging kernel method; and a piecewise constant constrained least square method were used to invert the data. Each computation was carried out independently. While they present similar trends, each of the solutions differs in detail. A consistent feature in all the inversions is the disappearance of differential rotation below the base of the convection zone. Also, a strong differential signature in the deeper part of the convection zone is present in most of the solutions. A slow decrease of the rotation rate with depth for the equatorial and midlatitude curves is significant in the spectral expansion and the least square results but only marginally apparent in the averaging kernel results.

Korzennik, Sylvain G.↗

NASA NTRS1980

Determination of collisional linewidths and shifts by a convolution method

A technique is described for fitting collisional linewidths and shifts from experimental spectral data. The method involves convoluting a low-pressure reference spectrum with a Lorentz shape function and comparing the convoluted spectrum with higher pressure spectra. Several experimental examples are given. One advantage of the method is that no extra information is needed about the instrument response function or spectral modulation. In addition, the method is shown to be relatively insensitive to the presence of reflections in the sample cell.

Pickett, H. M.↗

DOE OSTI2024

2-D seismic wave propagation using the distributional finite-difference method: further developments and potential for global seismology

SUMMARY We present a time-domain distributional finite-difference scheme based on the Lebedev staggered grid for the numerical simulation of wave propagation in acoustic and elastic media. The central aspect of the proposed method is the representation of the stresses and displacements with different sets of B-splines functions organized according to the staggered grid. The distributional finite-difference approach allows domain-decomposition, heterogeneity of the medium, curvilinear mesh, anisotropy, non-conformal interfaces, discontinuous grid and fluid–solid interfaces. Numerical examples show that the proposed scheme is suitable to model wave propagation through the Earth, where sharp interfaces separate large, relatively homogeneous layers. A few domains or elements are sufficient to represent the Earth’s internal structure without relying on advanced meshing techniques. We compare seismograms obtained with the proposed scheme and the spectral element method, and we show that our approach offers superior accuracy, reduced memory usage, and comparable efficiency.

Geochemistry & Geophysics↗

DOE OSTI2021

Uncertainty of SW Cloud Radiative Effect in Atmospheric Models Due to the Parameterization of Liquid Cloud Optical Properties

Clouds are largely responsible for the spread of climate models predictions. Here we focus on the uncertainties in cloud shortwave radiative effect due to the parameterization of liquid cloud single scattering properties (SSPs) from liquid water content (LWC) and droplet number concentration (N), named parameterization of cloud optical properties. Uncertainties arise from not accounting for the droplet size distribution (DSD)—which affects the estimation of the effective radius (r eff ) and modulates the r eff -dependency of the SSPs—and from averaging SSPs over wide spectral bands. To assess these uncertainties a series of r eff -dependent SSPs parameterizations corresponding to various DSDs and spectral averaging methods are derived and implemented in a radiative code. Combined with the DSD-dependent estimation of r eff they are used to compute the bulk radiative properties (reflectance, transmittance, absorptance) of various clouds (defined in terms of LWC and N), including a homogeneous cloud, more realistic case studies, and outputs of a climate model. The results show that the cloud radiative forcing can vary up to 20% depending on the assumed DSD. Likewise, differences up to 20% are obtained for heating rates. The estimation of r eff is the main source of uncertainty, while the SSPs parameterization contributes to around 20% of the total uncertainty. Spectral averaging is less an issue, except for atmospheric absorption. Overall, global shortwave cloud radiative effect can vary by 6 W m –2 depending on the assumed DSD shape, which is about 13% of the best observational estimate.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1989

Spectral calibration analysis of the airborne oceanographic lidar

Efforts were concentrated on the spectral resolution of the Airborne Oceanographic Lidar (AOL). This year's work was targeted towards the analysis of calibration techniques to enable the AOL to measure absolute radiances of both passive and active modes of operation. Absolute spectral calibration of the AOL is necessary in order to fully understand and monitor the sensitivity and stability of the total system. Calibration is also needed to obtain valid surface truth data, with which to improve the accuracy of satellite-borne oceanic color scanners. In particular, accurate measurements of oceanic chlorophyll concentrations rests upon reliable irradiance calibrations of both laser induced and solar induced chlorophyll fluoresence. An analysis was performed on the spectral calibration methods used by the AOL. The optical path of the instrumentation was examined to study how the radiance from a calibration sphere was influenced. Ray tracing analysis was performed, including the Cassegrain-telescope optics. It was determined that the calibration radiance was significantly effected by optical-defocusing, due to close positioning of the calibration sphere with respect to the telescope. Since the multi-mode usages of the AOL require varying altitudes and trajectories, a computational algorithm was developed to compensate for image distortions of the telescope optics. Secondary mirror blockage, secondary vignetting, and beam divergence was determined, in order to account for the actual amount of calibrated flux received at the spectral sensors.

Rousey, Carlton E.↗

DOE OSTI2025

Technical Report on Subsurface Monitoring of the Brady Hot Spring Geothermal Site, Nevada, based upon Full Waveform Inversion

Abilities to accurately characterize the subsurface in a geothermal setting is key to assess and support production. An important element of geothermal reservoir monitoring is also the ability to investigate fluid transport within fracture network. This report focuses on improving subsurface imaging and monitoring in geothermal settings using full waveform inversion based on the adjoint method and time-lapse imaging. To assess our method, we rely on a dense seismic dataset collected in 2016 at the Brady Hot Springs geothermal site in Nevada for the DOE-funded project Poroelastic Tomography by Adjoint Inverse Modeling of Data from Seismology, Geodesy, and Hydrology. This dataset captures subsurface changes across four stages of geothermal power plant operations, which involve varying rates of fluid injection and extraction. Two velocity models were previously derived from this dataset using different methods: one based on travel times and another on sweep interferometry. Our first step is to refine these models using adjoint tomography, which has been applied successfully at global and regional-scales but is less common at the reservoir-scale. Two approaches are then explored for time-lapse analysis: directly comparing refined tomographic models from different stages or backpropagating waveform differences relative to a baseline tomographic model. The main take away is that both approaches highlight similar reservoir behaviors, but the latter approach is more computationally effective in capturing small-scale changes in subsurface properties. For this work, we leverage the use of Salvus (www.mondaic.com), an end-to-end seismic imaging solution, relying on the spectral element method to compute forward and adjoint simulations, and developed by Mondaic Ltd. It includes integrated workflow management that handles waveform and metadata, launches simulations, computes waveform misfits and adjoint sources, and iterates for model updates by nonlinear optimization.

15 GEOTHERMAL ENERGY↗

DOE OSTI2020

Active- and transfer-learning applied to microscale-macroscale coupling to simulate viscoelastic flows

Active- and transfer-learning are applied to microscale dynamics of polymer flows for the multiscale discovery of effective constitutive approximations required in viscoelastic flow simulation. The result is macroscopic rheology directly connected to a microstructural model. Micro and macroscale simulations are adaptively coupled by means of Gaussian process regression (GPR) to run the expensive microscale computations only as necessary. This multiscale method is demonstrated with flows of a polymer solution as a model system. At the microscale level dissipative particle dynamics (DPD) is employed to model the fluid as a suspension of bead-spring micro-structures subjected to steady shear flow. The results yield the non-Newtonian viscosity and the first normal stress difference at strain rates as training data used in a GPR model. DPD parameters are calibrated with respect to experimental data for a real polymer solution. Compliance with these data requires adjustment of the DPD model's cutoff radius, which then becomes a function of the second invariant of the strain rate tensor. The FENE-P model is chosen for the macroscale description using the spectral element method (SEM) to simulate channel flow and flow past a circular cylinder. The DPD results at the lowest possible shear strain rate yield an estimate of the zero-shear rate viscosity, which allows the initiation of the macroscale flow by SEM as a Newtonian fluid. The resulting strain-rate field is surveyed to determine additional shear strain rate sampling points for the DPD system. This new information allows an initial fitting of parameters of the constitutive equation followed by new SEM simulations at the macroscale. Additionally, guided by active-learning GPR to select new sampling points, this process continues until convergence is achieved. The effectiveness of this new simulation paradigm for viscoelastic flows is tested with different macroscale operating conditions. The effective closure learned in the channel simulation is then transferred directly to the flow past a circular cylinder at low Reynolds number, where the results show that only two additional DPD simulations are required to achieve a satisfactory constitutive model. With an increase of the Reynolds number, the active-learning scheme automatically detects the inaccuracy of the learned constitutive model, and initiates additional DPD simulations for the extra data needed to once again close the microscale-macroscale coupled system. This new paradigm of active- and transfer-learning for multiscale modeling is readily applicable to other microscale-macroscale coupled simulations of complex fluids and other materials. Furthermore, the coupling between microscale and macroscale solvers can be seamlessly implemented with our open source multiscale universal interface (MUI) library.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Efficient sampling of free energy landscapes with functions in Sobolev spaces

Molecular simulations of biological and physical phenomena generally involve sampling complicated, rough energy landscapes characterized by multiple local minima. In this work, we introduce a new family of methods for advanced sampling that draw inspiration from functional representations used in machine learning and approximation theory. As shown here, such representations are particularly well suited for learning free energies using artificial neural networks. As a system evolves through phase space, the proposed methods gradually build a model for the free energy as a function of one or more collective variables, from both the frequency of visits to distinct states and generalized force estimates corresponding to such states. Implementation of the methods is relatively simple and, more importantly, for the representative examples considered in this work, they provide computational efficiency gains of up to several orders of magnitude over other widely used simulation techniques.

Approximation theory↗