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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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Sequential ensemble transform for Bayesian inverse problems
In this work, we present the Sequential Ensemble Transform (SET) method, an approach for generating approximate samples from a Bayesian posterior distribution. The method explores the posterior distribution by solving a sequence of discrete optimal transport problems to produce a series of transport plans which map prior samples to posterior samples. We prove that the sequence of Dirac mixture distributions produced by the SET method converges weakly to the true posterior as the sample size approaches infinity. Furthermore, our numerical results indicate that, when compared to standard Sequential Monte Carlo (SMC) methods, the SET approach is more robust to the choice of Markov mutation kernels and requires less computational efforts to reach a similar accuracy when used to explore complex posterior distributions. Finally, we describe adaptive schemes that allow to completely automate the use of the SET method.
Physics-informed Karhunen-Loeve and Neural Network Approximations for Solving Inverse Differential Equation Problems
Here we present the PI-CKL-NN method for parameter estimation in differential equation (DE) models given sparse measurements of the parameters and states. In the proposed approach, the space- or time-dependent parameters are approximated by Karhunen-Loeve (KL) expansions that are conditioned on the parameters’ measurements, and the states are approximated by deep neural networks (DNNs). The unknown weights in the KL expansions and DNNs are found my minimizing the cost function that enforces the measurements of the states the DE constraint. Regularization is achieved by adding the l2 norm of the conditional KL coefficients into the loss function. Our approach assumes that the parameter fields are correlated in space or time and enforces the statistical knowledge (the mean and the covariance function) in addition to the DE constraints and measurements as opposed to the physics-informed neural network (PINN) and other similar physics-informed machine learning methods where only DE constraints and data are used for parameter estimation. We use the PI-CKL-NN method for parameter estimation in an ordinary differential equation with an unknown time-dependent parameter and the one- and two-dimensional partial differential diffusion equations with unknown space-dependent diffusion coefficients. We also demonstrate that PI-CKL-NN is more accurate than the PINN method, especially when the observations of the parameters are very sparse
Gaussian process regression and conditional Karhunen-Loève models for data assimilation in inverse problems
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Randomized physics-informed machine learning for uncertainty quantification in high-dimensional inverse problems
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A directional Gaussian smoothing optimization method for computational inverse design in nanophotonics
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Large collectivity in 29Ne at the boundary of the island of inversion
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Bayesian inverse uncertainty quantification of the physical model parameters for the spallation neutron source first target station
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Exciton Chirality Inversion in Dye Dimers Templated by DNA Holliday Junction
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Gating with Charge Inversion to Control Ionic Transport in Nanopores
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Alpert multi-wavelets for functional inverse problems: direct optimization and deep learning
Computational engineering models often contain unknown entities (e.g. parameters, initial and boundary conditions) that require estimation from other measured observable data. Estimating such unknown entities is challenging when they involve spatio-temporal fields because such functional variables often require an infinite-dimensional representation. Here, we address this problem by transforming an unknown functional field using Alpert wavelet bases and truncating the resulting spectrum. Hence the problem reduces to the estimation of few coefficients that can be performed using common optimization methods. We apply this method on a one-dimensional heat transfer problem where we estimate the heat source field varying in both time and space. The observable data is comprised of temperature measured at several thermocouples in the domain. This latter is composed of either copper or stainless steel. The optimization using our method based on wavelets is able to estimate the heat source with an error between 5% and 7%. We analyze the effect of the domain material and number of thermocouples as well as the sensitivity to the initial guess of the heat source. Finally, we estimate the unknown heat source using a different approach based on deep learning techniques where we consider the input and output of a multi-layer perceptron in wavelet form. We find that this deep learning approach is more accurate than the optimization approach with errors below 4%.
On the use of adjoints in the inversion of observed quasi-static deformation
An adjoint-based conjugate gradient algorithm provides an efficient means for imaging sources of deformation within the Earth, such as volume stresses associated with fluid flow in aquifers and reservoirs. For time intervals over which the overburden deforms elastically, one can calculate the gradient elements for a single model update using just two numerical simulations. The first is a forward run that is used to compute the residuals associated with the given iteration. The second simulation is to evaluate the application of the adjoint operator to the residuals. In this adjoint calculation, the residual displacements are applied as sources at the measurement locations, driving the deformation in the simulation. The volume stress on the source grid blocks, in response to the residual displacements, provide the gradient components. We apply this technique to satellite-based interferometric synthetic aperture radar (InSAR) line-of-sight displacements that were observed over an oil reservoir in California’s Central Valley. Furthermore, we find that the adjoint-based gradient estimates, requiring 18 CPU seconds, agree with conventional numerical calculations that take over 3700 CPU seconds to compute. Conjugate gradient algorithms utilizing the conventional approach and adjoint-based gradient computations give roughly the same reductions in misfit and similar final estimates of reservoir volume change.
Physics-Based Inversion of a Millimeter-Wave Diagnostic for Liquid Metal Additive Manufacturing
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Deep Equilibrium Learning of Explicit Regularization Functionals for Imaging Inverse Problems
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Data-Driven Seismic Waveform Inversion: A Study on the Robustness and Generalization
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Structural basis for receptor selectivity and inverse agonism in S1P5 receptors
This dataset contains a subset of raw diffraction images for the serial femtosecond crystallography data of S1P5-ONO-5430608 complex. The diffraction data contain a strong systematic background as described in Lyapina et al. No additional background subtraction was applied to the data. In order to avoid its effects, the background subtraction implemented in CrystFEL, was applied (--rings-grad subtraction). The final stream, supplemented with this dataset, was obtained with: indexamajig 0.8.0 Respectively, the hkl file was obtained with: partialator 0.8.0