Search NASA⌕ Search

SEARCH · Search NASA

Results for “mathematical regularization”

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.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Ultrasound waveform tomography with spatial and edge regularization

Synthetic-aperture ultrasound tomography systems and methods using scanning arrays and algorithms configured to simultaneously acquire ultrasound transmission and reflection data, and process the data for improved ultrasound tomography imaging, wherein the tomography imaging comprises total-variation regularization, or a modified total variation regularization, particularly with edge-guided or spatially variant regularization.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Enhancing Interpretability in Generative Modeling: Statistically Disentangled Latent Spaces Guided by Generative Factors in Scientific Datasets

This study addresses the challenge of statistically extracting generative factors from complex, high-dimensional datasets in unsupervised or semi-supervised settings. We investigate encoder-decoder-based generative models for nonlinear dimensionality reduction, focusing on disentangling low-dimensional latent variables corresponding to independent physical factors. Introducing Aux-VAE, a novel architecture within the classical Variational Autoencoder framework, we achieve disentanglement with minimal modifications to the standard VAE loss function by leveraging prior statistical knowledge through auxiliary variables. These variables guide the shaping of the latent space by aligning latent factors with learned auxiliary variables. We validate the efficacy of Aux-VAE through comparative assessments on multiple datasets, including astronomical simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Estimation and Fusion for Tracking Over Long-Haul Links Using Artificial Neural Networks

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as tracking and/or monitoring of one or more dynamic targets. A remote fusion center fuses the information provided by these sensors so that a final estimate of certain target characteristics – such as the position – is expected to possess much improved quality. In this paper, we pursue learning-based approaches for estimation and fusion of target states in longhaul sensor networks. In particular, we consider learning based on various implementations of artificial neural networks (ANNs). Finally, the joint effect of (i) imperfect communication condition, namely, link-level loss and delay, and (ii) computation constraints, in the form of low-quality sensor estimates, on ANN-based estimation and fusion, is investigated by means of analytical and simulation studies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Bilevel optimization, deep learning and fractional Laplacian regularization with applications in tomography

Here we consider a generalized bilevel optimization framework for solving inverse problems. We introduce fractional Laplacian as a regularizer to improve the reconstruction quality, and compare it with the total variation regularization. We emphasize that the key advantage of using fractional Laplacian as a regularizer is that it leads to a linear operator, as opposed to the total variation regularization which results in a nonlinear degenerate operator. Inspired by residual neural networks, to learn the optimal strength of regularization and the exponent of fractional Laplacian, we develop a dedicated bilevel optimization neural network with a variable depth for a general regularized inverse problem. We illustrate how to incorporate various regularizer choices into our proposed network. As an example, we consider tomographic reconstruction as a model problem and show an improvement in reconstruction quality, especially for limited data, via fractional Laplacian regularization. We successfully learn the regularization strength and the fractional exponent via our proposed bilevel optimization neural network. We observe that the fractional Laplacian regularization outperforms total variation regularization. This is specially encouraging, and important, in the case of limited and noisy data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Customizable adaptive regularization techniques for B-spline modeling

B-spline models are a powerful way to represent scientific data sets with a functional approximation. However, these models can suffer from spurious oscillations when the data to be approximated are not uniformly distributed. Model regularization (i.e., smoothing) has traditionally been used to minimize these oscillations; unfortunately, it is sometimes impossible to sufficiently remove unwanted artifacts without smoothing away key features of the data set. In this article, we present a method of model regularization that preserves significant features of a data set while minimizing artificial oscillations. Our method varies the strength of a smoothing parameter throughout the domain automatically, removing artifacts in poorly-constrained regions while leaving other regions unchanged. Further, the proposed method selectively incorporates regularization terms based on first and second derivatives to maintain model accuracy while minimizing numerical artifacts. The behavior of our method is validated on a collection of two- and three-dimensional data sets produced by scientific simulations. In addition, a key tuning parameter is highlighted and the effects of this parameter are presented in detail. This paper is an extension of our previous conference paper at the 2022 International Conference on Computational Science (ICCS) (Lenz et al., 2022).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

The Auxiliary Space Preconditioner for the de Rham Complex

Here we generalize the construction and analysis of auxiliary space preconditioners to the $n$-dimensional finite element subcomplex of the de Rham complex. These preconditioners are based on a generalization of a decomposition of Sobolev space functions into a regular part and a potential. A discrete version is easily established using the tools of finite element exterior calculus. We then discuss the four-dimensional de Rham complex in detail. By identifying forms in four dimensions with simple proxies, form operations are written out in terms of familiar algebraic operations on matrices, vectors, and scalars. This provides the basis for our implementation of the preconditioners in four dimensions. Extensive numerical experiments illustrate their performance, practical scalability, and parameter robustness, all in accordance with the theory.

4D↗
DOE OSTI2023

Not so HOT Triangulations

Here, we propose primal–dual mesh optimization algorithms that overcome shortcomings of the standard algorithm while retaining some of its desirable features. “Hodge-Optimized Triangulations” defines the “HOT energy” as a bound on the discretization error of the diagonalized Delaunay Hodge star operator. HOT energy is a natural choice for an objective function, but unstable for both mathematical and algorithmic reasons: it has minima for collapsed edges, and its extrapolation to non-regular triangulations is inaccurate and has unbounded minima. We propose a different extrapolation with a stronger theoretical foundation, and avoid extrapolation by recalculating the objective just beyond the flip threshold. We propose new objectives, based on normalizations of the HOT energy, with barriers to edge collapses and other undesirable configurations. We propose mesh improvement algorithms coupling these. When HOT optimization nearly collapses an edge, we actually collapse the edge. Otherwise, we use the barrier objective to update positions and weights and remove vertices. By combining discrete connectivity changes with continuous optimization, we more fully explore the space of possible meshes and obtain higher quality solutions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Elasticity of a Filament with Kinks

Using the wormlike chain model, we analytically study the elasticity of a filament with kinks. We calculate the position probability density function and the force constant of a kinked filament with a general kink angle. Then, using the mathematical induction, we obtain the positional-orientational probability density function of a filament with regular kinks. For this filament, we compute the force constant in two different directions. In longitudinal direction of the filament, the force constant is proportional to the inverse of the number of the segments, i.e., 1 / m, while in transverse direction, it is proportional to 1/m{sup 3}.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Open Call LDRD: Physically Informed Autoencoders for Galactic Redshift Regression

Physical constraints have been suggested to make neural network models more generalizable, act scientifically plausible, and be more data-efficient over unconstrained baselines. In this report, we present preliminary work on evaluating the effects of adding soft physical constraints to computer vision neural networks trained to estimate the conditional density of redshift on input galaxy images for the Sloan Digital Sky Survey. We introduce physically motivated soft constraint terms that are not implemented with differential or integral operators. We frame this work as a simple ablation study where the effect of including soft physical constraints is compared to an unconstrained baseline. We compare networks using standard point estimate metrics for photometric redshift estimation, as well as metrics to evaluate how faithful our conditional density estimate represents the probability over the ensemble of our test dataset. We find no evidence that the implemented soft physical constraints are more effective regularizers than augmentation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

A Bound- and Equality-Constrained Quadratic Programming Method for Image Reconstruction

Image deblurring is modeled as an ill-posed, linear inverse problem and can be written in terms of an optimization problem. By adding bound and equality constraints and/or penalty terms to the objective function, the problem is regularized and a viable solution exists; however, there are few methods available for solving such constrained problems. Here we formulate two regularized solutions into constrained optimization problems and adapt an iterative augmented Lagrangian solver. Results are shown on synthetic data in one- and two-dimensions, as well as on a radiograph used to calibrate the transmission curve of a pulsed power X-ray source at a U.S. Department of Energy radiography facility.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

A variational treatment of material configurations with application to interface motion and microstructural evolution

We present a unified variational treatment of evolving configurations in crystalline solids with microstructure. The crux of our treatment lies in the introduction of a vector configurational field. This field lies in the material, or configurational, manifold, in contrast with the traditional displacement field, which we regard as lying in the spatial manifold. We identify two distinct cases which describe (a) problems in which the configurational field's evolution is localized to a mathematically sharp interface, and (b) those in which the configurational field's evolution can extend throughout the volume. The first case is suitable for describing incoherent phase interfaces in polycrystalline solids, and the latter is useful for describing smooth changes in crystal structure and naturally incorporates coherent (diffuse) phase interfaces. These descriptions also lead to parameterizations of the free energies for the two cases, from which variational treatments can be developed and equilibrium conditions obtained. For sharp interfaces that are out-of-equilibrium, the second law of thermodynamics furnishes restrictions on the kinetic law for the interface velocity. The class of problems in which the material undergoes configurational changes between distinct, stable crystal structures are characterized by free energy density functions that are non-convex with respect to configurational strain. For physically meaningful solutions and mathematical well-posedness, it becomes necessary to incorporate interfacial energy. This we have done by introducing a configurational strain gradient dependence in the free energy density function following ideas laid out by Toupin (Arch. Rat. Mech. Anal., 11, 1962, 385-414). The variational treatment leads to a system of partial differential equations governing the configuration that is coupled with the traditional equations of nonlinear elasticity. The coupled system of equations governs the configurational change in crystal structure, and elastic deformation driven by elastic, Eshelbian, and configurational stresses. As a result, numerical examples are presented to demonstrate interface motion as well as evolving microstructures of crystal structures.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Analysis of self-similar solutions of multidimensional conservation laws

This project focused on analysis of multidimensional conservation laws, specifically on extensions to the study of self-siminar solutions, a project initiated by the PI. In addition, progress was made on an approach to studying conservation laws of very low regularity; in this research, the context was a novel problem in chromatography. Two graduate students in mathematics were supported during the grant period, and have almost completed their thesis research.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Ir-Rational Number Institute Report 2017-2018

The 2017 – 2018 Los Alamos National Laboratory Math and Science Academy Ir-Rational Number Institute (IrRNI) six-all-day sessions were held on Northern New Mexico College campus (NNMC) between September 2017 and March 2018. The IrRNI included 7 Northern New Mexico school districts (Bloomfield Public Schools, Cuba Independent Schools, Española Public Schools, Los Alamos Public Schools, Pojoaque Valley School District, Pecos Independent School District and Santa Fe Public Schools) and one Bureau of Indian Education school district, Jemez Day School, located in Jemez Pueblo. Twenty-nine teachers of mathematics, kindergarten through eighth grade, 2 principals, 1 STEM Coordinator, and 1 New Mexico Public Education Math and Science Bureau staffer* attended regularly and were eligible for a Certificate of Completion or Certificate of Engagement on the last day of instruction ( 20 participants were new to the IrRNI and 13 were returnees from the previous year or the MSA Summer Institute).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Numerical Solution of the Inverse Scattering Problem for the Acoustic Equation in an Absorptive Layered Medium

We consider a nonlinear ordinary differential equation associated with a number of inverse scattering problems in acoustic and seismic sounding in which acoustic impedance and an impedance-dependent unknown damping coefficient are the unknown function. We prove that the Cauchy problem is uniquely solvable when the derivative is treated as a generalized function. It is established that the inverse scattering problem in a layered dissipative medium simultaneously determines the acoustic impedance and the damping coefficient. A regularized numerical algorithm is proposed and numerical results are reported.

97 MATHEMATICS AND COMPUTING↗