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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Multiple Solutions of Boundary-Value Problems for Hamiltonian Systems

We consider two-point boundary-value problems for a Hamiltonian system of the form x′ = f(k, y), y′ = g(x, 𝜆), where k and 𝜆 are parameters. The numbers of solutions, both positive and oscillatory, for the boundary-value problems are estimated. Our main tool is the analysis of the phase plane combined with the evaluation of time-map functions. The bifurcation diagram and solution curves are constructed for the Hamiltonian system. We also present examples illustrating the bifurcations with respect to the parameters k and 𝜆:.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Geologic CO2 Input Into Groundwater and the Atmosphere, Soda Springs, ID, USA

A set of CO2 flux, geochemical, and hydrologic measurement techniques was used to characterize the source of and quantify gaseous and dissolved CO2 discharges from the area of Soda Springs, southeastern Idaho. An eddy covariance system was deployed for approximately one month near a bubbling spring and measured net CO2 fluxes from - 74 to 1147 g m- 2 d- 1. An inversion of measured eddy covariance CO2 fluxes and corresponding modeled source weight functions mapped the surface CO2 flux distribution within and quantified CO2 emission rate (24.9 t d- 1) from a 0.05 km2 area surrounding the spring. Soil CO2 fluxes (< 1 to 52,178 g m- 2 d- 1) were measured within a 0.05 km2 area of diffuse degassing using the accumulation chamber method. The estimated CO2 emission rate from this area was 49 t d- 1. A carbon mass balance approach was used to estimate dissolved CO2 discharges from contributing sources at nine springs and the Soda Springs geyser. Total dissolved inorganic carbon (as CO2) discharge for all sampled groundwater features was 57.1 t d- 1. Of this quantity, approximately 3% was derived from biogenic carbon dissolved in infiltrating groundwater, 35% was derived from carbonate mineral dissolution within the aquifer(s), and 62% was derived from deep source(s). Isotopic compositions of helium (1.74–2.37 Ra) and deeply derived carbon (d13C approximately 3‰) suggested contribution of volatiles from mantle and carbonate sources. Assuming that the deeply derived CO2 discharge estimated for sampled groundwater features (approximately 35 t d- 1) is representative of springs throughout the study area, the total rate of deeply derived CO2 input into the groundwater system within this area could be ~ 350 t d- 1, similar to CO2 emission rates from a number of quiescent volcanoes.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Prediction and uncertainty quantification of shale well performance using multifidelity Monte Carlo

Uncertainty quantification is an integral component of reservoir management, especially considering the inherent uncertainty in subsurface systems. While a standard practice to estimate the uncertainty, Monte Carlo (MC) simulation is computationally intense when the sampling population comprises high-fidelity simulations. Alternatively, the Multi-fidelity Monte Carlo (MFMC) simulation overcomes this computational intensity by integrating low- and high-fidelity simulations. Our goal is to minimize the number of expensive high-fidelity simulations while maintaining accuracy and using numerous fast and cheap low-fidelity simulations to efficiently sample to input parameter space of interest. We selected gas production from unconventional wells to demonstrate the potential speedups and accuracy of the MFMC approach. The model fidelity usually determines the trade-off between accuracy and efficiency. While the high-fidelity model is more accurate, the low-fidelity model is more efficient. Our high-fidelity simulation includes reservoir simulations of a hydraulically fractured well. On the other hand, our low-fidelity model comprises the parallel-plate flow model. We used differential programming to efficiently solve the 1D flow model, where automatic differentiation is used to efficiently compute the gradients. We matched the production profile of high-fidelity simulations with our low-fidelity simulations. Then, we used a support vector regression to map the high- and low-fidelity input parameters. The mapping function is essential to tune the low-dimensional parameter space of the low-fidelity model to the high-dimensional parameter space of the high-fidelity model. We found that we can use a combination of 9 high fidelity and 10,000 low fidelity simulations to efficiently and accurately simulate pressure management. This method is at least two orders of magnitude faster than only using high-fidelity simulations. Finally, from a broader perspective, MFMC could efficiently estimate the uncertainty of various systems and models, integrating low- and high-fidelity models.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2024

A deep learning-based workflow for fast prediction of 3D state variables in geological carbon storage: A dimension reduction approach

Deep learning (DL) models are extensively used as surrogate models for high-fidelity simulations of multiphase fluid flow in porous media at large scales, enabling fast forecasts of the spatial–temporal evolution of three-dimensional (3D) state variables in geological carbon storage (GCS). However, training these models in high-dimensional space remains computationally demanding and prone to overfitting because of limited training data. This paper presents a novel workflow to address these challenges by integrating dimension reduction (DR) methods. Here, the proposed workflow employed pre-trained DR models to extract the latent variables of geological models and state variables and utilized the multi-layer perceptron (MLP) for constructing mapping functions between the input and output variables in latent spaces. Subsequently, the pre-trained reconstruction models converted the MLP-predicted latent state variables to their original high-dimensional form. Furthermore, we proposed a novel strategy for the DR and reconstruction of 3D saturation fields to account for the unique data characteristics of sparsity, nonuniformity, and discontinuity. The proposed strategy applied PCA and inverse PCA for 2D average saturation fields and developed a DL-based 3D reconstruction model, leveraging three 2D average saturation fields as input to produce a 3D saturation field as output. The pre-training of DR and reconstruction models and training of MLP models were conducted on 84 Gulf of Mexico (GoM) simulations and evaluated on 12 testing simulations. Each simulation contained 720 monthly time steps, with the first 360 months as the injection period and the rest as the post-injection period. The proposed workflow, incorporating DR and DL models, accurately predicts the normalized 3D pressure fields, achieving mean square error (MSE) of 2.92 × 10 -7 compared to the ground truth obtained from a full-physics simulator. Furthermore, the proposed strategy outperformed PCA and convolutional autoencoder (CAE) models on 3D saturation fields, resulting in minor workflow prediction errors with an MSE of 2.93 × 10 -5 . The results suggest the proposed workflow provides sufficient predictive fidelity across temporal and spatial scales, and enables a speedup of 160 times compared to the full-physics simulator, facilitating improved decision-making and risk assessment for large-scale GCS management in real-time scenarios.

3D reconstruction model↗
DOE OSTI2013

The Mouse House: A brief history of the ORNL mouse-genetics program, 1947–2009

The large mouse genetics program at the Oak Ridge National Lab is often re-membered chiefly for the germ-cell mutation-rate data it generated and their uses in estimating the risk of heritable radiation damage. In fact, it soon became a multi-faceted research effort that, over a period of almost 60 years, generated a wealth of information in the areas of mammalian mutagenesis, basic genetics (later enriched by molecular techniques), cytogenetics, reproductive biology, biochemistry of germ cells, and teratology. Research in the area of germ-cell mutagenesis explored the important physical and biological factors that affect the frequency and nature of induced mutations and made several unexpected discoveries, such as the major importance of the perigametic interval (the zygote stage) for the origin of spontaneous mutations and for the sensitivity to induced genetic change. Of practical value was the discovery that ethylnitrosourea was a supermutagen for point mutations, making high-efficiency mutagenesis in the mouse feasible worldwide. Teratogenesis findings resulted in recommendations still generally accepted in radiological practice. Studies supporting the mutagenesis research added whole bodies of information about mammalian germ-cell development and about molecular targets in germ cells. The early decision to not merely count but propagate genetic variants of all sorts made possible further discoveries, such as the Y-Chromosome s importance in mammalian sex determination and the identification of rare X-autosome translocations, which, in turn, led to the formulation of the single-active-X hypothesis and provided tools for studies of functional mosaicism for autosomal genes, male sterility, and chromosome-pairing mechanism. Extensive genetic and then molecular analyses of large numbers of induced specific-locus mutants resulted in fine-structure physical and correlated functional mapping of significant portions of the mouse genome and constituted a valuable source of mouse models for human genetic disorders.

germ-cell mutagenesis↗
DOE OSTI2015

Conserved amino acids within the N-terminus of the West Nile virus NS4A protein contribute to virus replication, protein stability and membrane proliferation

The West Nile virus strain Kunjin virus (WNV{sub KUN}) NS4A protein is a multifunctional protein involved in many aspects of the virus life-cycle and is a major component of the WNV{sub KUN} replication complex (RC). Previously we identified a conserved region in the C-terminus of NS4A regulating proteolytic processing and RC assembly, and now investigate key conserved residues in the N-terminus of NS4A and their contribution to WNV{sub KUN} replication. Mutation of P13 completely ablated replication, whereas, mutation of P48 and D49, near the first transmembrane helix, and G66 within the helix, showed variable defects in replication, virion secretion and membrane proliferation. Intriguingly, the P48 and G66 NS4A mutants resulted in specific proteasome depletion of NS4A that could in part be rescued with a proteasome inhibitor. Our results suggest that the N-terminus of NS4A contributes to correct folding and stability, essential for facilitating the essential roles of NS4A during replication. - Highlights: • Mutation of Proline13 of the WNV NS4A protein is lethal to replication. • 1st TMB helix of NS4A contributes to protein stability and membrane remodelling. • Unstable mutants of NS4A can be rescued with a proteasome inhibitor. • This study (and of others) contributes to a functional mapping of the NS4A protein.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Dissolution of a cylindrical disk in Hele-Shaw flow: a conformal-mapping approach

Here we apply conformal mapping to find the evolving shapes of a dissolving cylinder in a potential flow. Similar equations can be used to describe melting in a flowing liquid phase. Results are compared with microfluidic experiments and numerical simulations. Shapes predicted by conformal mapping agree almost perfectly with experimental observations, after a modest (20 %) rescaling of the time. Finite-volume simulations show that the differences with experiment are connected to the underlying assumptions of the analytical model: potential flow and diffusion-limited dissolution. Approximate solutions of the equations describing the evolution of the shape of the undissolved solid can be derived from a Laurent expansion of the mapping function from the unit circle. Asymptotic expressions for the evolution of the area of the disk and the shift in its centre of mass have been derived at low and high Péclet number. Analytic approximations to the leading-order Laurent coefficients provide additional insight into the mechanisms underlying pore-scale dissolution.

58 GEOSCIENCES↗
DOE OSTI2021

Trap-Assisted Dopant Compensation Prevents Shunting in Poly-Si Passivating Interdigitated Back Contact Silicon Solar Cells

Using a trap-assisted compensation model, we explain why polycrystalline Si (poly-Si) passivating contacts are able to achieve low leakage current between the doped fingers of interdigitated back contact (IBC) monocrystalline Si solar cells despite mixing of boron and phosphorus dopants in the isolation region. The fill factor of IBC solar cells is strongly affected by the electrical isolation region between n- and p-type fingers, as this region is critical in minimizing shunting losses. During fabrication of monocrystalline Si solar cells with poly-Si passivating contacts, the intrinsic poly-Si isolation region inevitably gets contaminated with both p- and n-type dopants. Using dopant profiles measured with time-of-flight secondary ion mass spectrometry and scanning probe measurements of the isolation region, we demonstrate that despite the dopant spreading during cell processing, a well-compensated region between the doped fingers exists that prevents shunting. The trap-assisted dopant compensation mechanism significantly widens the compensated region to tens of microns, where the residual dopant densities are below the trap density. This enables a high-resistivity region, resulting in low shunt current. Using one-dimensional (1-D) finite element diode simulations, we identify the design parameters and experimental conditions under which a sufficiently resistive region can form. Furthermore, our measurements of 2-D local resistivity and work function maps across the isolation region using scanning spreading resistance microscopy and Kelvin probe force microscopy demonstrate the existence of a highly resistive, wide compensated region and confirm our proposed compensation mechanism. For our structures, this region is ~25 µm in width within a ~150 µm wide finger isolation region with nearly 3 orders of magnitude higher resistivity than the regions dominated by a single type of dopant.

14 SOLAR ENERGY↗
DOE OSTI2017

Computationally-Guided Synthetic Control over Pore Size in Isostructural Porous Organic Cages

The physical properties of 3-D porous solids are defined by their molecular geometry. Hence, precise control of pore size, pore shape, and pore connectivity are needed to tailor them for specific applications. However, for porous molecular crystals, the modification of pore size by adding pore-blocking groups can also affect crystal packing in an unpredictable way. This precludes strategies adopted for isoreticular metal-organic frameworks, where addition of a small group, such as a methyl group, does not affect the basic framework topology. Here, we narrow the pore size of a cage molecule, CC3, in a systematic way by introducing methyl groups into the cage windows. Computational crystal structure prediction was used to anticipate the packing preferences of two homochiral methylated cages, CC14-R and CC15-R, and to assess the structure-energy landscape of a CC15-R/CC3-S cocrystal, designed such that both component cages could be directed to pack with a 3-D, interconnected pore structure. The experimental gas sorption properties of these three cage systems agree well with physical properties predicted by computational energy-structure-function maps.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Predicting CO 2 Plume Migration in Heterogeneous Formations Using Conditional Deep Convolutional Generative Adversarial Network

Numerical simulation of flow and transport in heterogeneous formations has long been studied, especially for uncertainty quantification and risk assessment. The high computational cost associated with running large-scale numerical simulations in a Monte Carlo sense has motivated the development of surrogate models, which aim to capture the important input-output relations of physics-based models but require only a fraction of the cost of full model runs. In this work, we formulate a conditional deep convolutional generative adversarial network (cDC-GAN) surrogate model to learn the dynamic functional mappings in multiphase models. The cDC-GAN belongs to a class of semisupervised learning methods that can be used to learn the data generation processes. Like the original GAN, a main strength of the cDC-GAN is that it includes a self-training scheme for improving the quality of generative modeling in a game theoretic framework, without requiring extensive statistical knowledge and assumptions on input data distributions. In particular, our cDC-GAN model is designed to learn cross-domain mappings between high-dimensional input (e.g., permeability) and output (e.g., phase saturations) pairs, with the ability to incorporate conditioning information (e.g., prediction time). As a use case, we demonstrate the performance of cDC-GAN for predicting the migration of carbon dioxide (CO 2 ) plume in heterogeneous carbon storage reservoirs, which has both numerical and practical significance because of the safe storage requirements now mandated in many countries. Results show that cDC-GAN achieves high accuracy in predicting the spatial and temporal evolution patterns of the injected CO 2 plume, as compared to the original results obtained using a compositional reservoir simulator. The performance of cDC-GAN models, trained using the same number of training samples, stays relatively robust when the level of spatial heterogeneity is increased. Our cDC-GAN is pattern based and is not limited by the underlying physics. Thus, it provides a general framework for developing surrogate models, and for conducting uncertainty analyses for a wide range of physics-based models used in both groundwater and subsurface energy exploration applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Bridging Hydrological Ensemble Simulation and Learning Using Deep Neural Operators

Ensemble-based simulation and learning (ESnL) has long been used in hydrology for parameter inference, but computational demands of process-based ESnL can be quite high. To address this issue, we propose a deep neural operator learning approach. Neural operators are generic machine learning algorithms that can learn functional mappings between infinite-dimensional spaces, providing a highly flexible tool for scientific machine learning. Our approach is built upon DeepONet, a specific deep neural operator, and is designed to address several common problems in hydrology, namely, model parameter estimation, prediction at ungaged locations, and uncertainty quantification. Here we demonstrate the effectiveness of our DeepONet-based workflow using an existing large model ensemble created for an eastern U.S. watershed that is instrumented with 10 streamflow gages. Results suggest DeepONet achieves high efficiency in learning an ML surrogate model from the model ensemble, with the modified Kling-Gupta Efficiency exceeding 0.9 on holdout test sets. Parameter inference, carried out using the trained DeepONet surrogate model and genetic algorithm, also yields robust results. Additionally, we formulate and train a separate DeepONet model for physics-informed, seq-to-seq streamflow forecasting, which further reduces biases in the pre-trained DeepONet surrogate model. While this study focuses primarily on a single watershed, our approach is general and may be extended to enable learning from model ensembles across multiple basins or models. Thus, this research represents a significant contribution to the application of hybrid machine learning in hydrology.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing

We show that comprehensive sequence-function maps obtained by deep sequencing can be used to reprogram interaction specificity and to leapfrog over bottlenecks in affinity maturation by combining many individually small contributions not detectable in conventional approaches. We use this approach to optimize two computationally designed inhibitors against H1N1 influenza hemagglutinin and, in both cases, obtain variants with subnanomolar binding affinity. The most potent of these, a 51-residue protein, is broadly cross-reactive against all influenza group 1 hemagglutinins, including human H2, and neutralizes H1N1 viruses with a potency that rivals that of several human monoclonal antibodies, demonstrating that computational design followed by comprehensive energy landscape mapping can generate proteins with potential therapeutic utility.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Novel genetic risk factors influence progression of islet autoimmunity to type 1 diabetes

Type 1 diabetes arises from the autoimmune destruction of insulin-producing beta-cells of the pancreas, resulting in dependence on exogenously administered insulin to maintain glucose homeostasis. In this study, our aim was to identify genetic risk factors that contribute to progression from islet autoimmunity to clinical type 1 diabetes. We analyzed 6.8 million variants derived from whole genome sequencing of 160 islet autoantibody positive subjects, including 87 who had progressed to type 1 diabetes. The Cox proportional-hazard model for survival analysis was used to identify genetic variants associated with progression. We identified one novel region, 20p12.1 (TASP1; genome-wide P < 5 × 10 –8 ) and three regions, 1q21.3 (MRPS21–PRPF3), 2p25.2 (NRIR), 3q22.1 (COL6A6), with suggestive evidence of association (P < 8.5 × 10 –8 ) with progression from islet autoimmunity to type 1 diabetes. Once islet autoimmunity is initiated, functional mapping identified two critical pathways, response to viral infections and interferon signaling, as contributing to disease progression. These results provide evidence that genetic pathways involved in progression from islet autoimmunity differ from those pathways identified once disease has been established. These results support the need for further investigation of genetic risk factors that modulate initiation and progression of subclinical disease to inform efforts in development of novel strategies for prediction and intervention of type 1 diabetes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Permutation invariant potential energy surfaces for polyatomic reactions using atomistic neural networks

The applicability and accuracy of the Behler-Parrinello atomistic neural network method for fitting reactive potential energy surfaces is critically examined in three systems, H + H{sub 2} → H{sub 2} + H, H + H{sub 2}O → H{sub 2} + OH, and H + CH{sub 4} → H{sub 2} + CH{sub 3}. A pragmatic Monte Carlo method is proposed to make efficient choice of the atom-centered mapping functions. The accuracy of the potential energy surfaces is not only tested by fitting errors but also validated by direct comparison in dynamically important regions and by quantum scattering calculations. Our results suggest this method is both accurate and efficient in representing multidimensional potential energy surfaces even when dissociation continua are involved.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Discovering causal structure with reproducing-kernel Hilbert space ε -machines

We merge computational mechanics’ definition of causal states (predictively equivalent histories) with reproducing-kernel Hilbert space (RKHS) representation inference. The result is a widely applicable method that infers causal structure directly from observations of a system’s behaviors whether they are over discrete or continuous events or time. A structural representation—a finite- or infinite-state kernel ϵ-machine—is extracted by a reduced-dimension transform that gives an efficient representation of causal states and their topology. In this way, the system dynamics are represented by a stochastic (ordinary or partial) differential equation that acts on causal states. We introduce an algorithm to estimate the associated evolution operator. Paralleling the Fokker–Planck equation, it efficiently evolves causal-state distributions and makes predictions in the original data space via an RKHS functional mapping. We demonstrate these techniques, together with their predictive abilities, on discrete-time, discrete-value infinite Markov-order processes generated by finite-state hidden Markov models with (i) finite or (ii) uncountably infinite causal states and (iii) continuous-time, continuous-value processes generated by thermally driven chaotic flows. The method robustly estimates causal structure in the presence of varying external and measurement noise levels and for very high-dimensional data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Stereo-particle image velocimetry uncertainty quantification

Particle image velocimetry (PIV) measurements are subject to multiple elemental error sources and thus estimating overall measurement uncertainty is challenging. Recent advances have led to a posteriori uncertainty estimation methods for planar two-component PIV. However, no complete methodology exists for uncertainty quantification in stereo PIV. In the current paper, a comprehensive framework is presented to quantify the uncertainty stemming from stereo registration error and combine it with the underlying planar velocity uncertainties. The disparity in particle locations of the dewarped images is used to estimate the positional uncertainty of the world coordinate system, which is then propagated to the uncertainty in the calibration mapping function coefficients. Next, the calibration uncertainty is combined with the planar uncertainty fields of the individual cameras through an uncertainty propagation equation and uncertainty estimates are obtained for all three velocity components. The methodology was tested with synthetic stereo PIV data for different light sheet thicknesses, with and without registration error, and also validated with an experimental vortex ring case from 2014 PIV challenge. Thorough sensitivity analysis was performed to assess the relative impact of the various parameters to the overall uncertainty. The results suggest that in absence of any disparity, the stereo PIV uncertainty prediction method is more sensitive to the planar uncertainty estimates than to the angle uncertainty, although the latter is not negligible for non-zero disparity. Overall the presented uncertainty quantification framework showed excellent agreement between the error and uncertainty RMS values for both the synthetic and the experimental data and demonstrated reliable uncertainty prediction coverage. Finally, this stereo PIV uncertainty quantification framework provides the first comprehensive treatment on the subject and potentially lays foundations applicable to volumetric PIV measurements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

The positive orthogonal Grassmannian and loop amplitudes of ABJM

Herein we study the combinatorics associated with the positive orthogonal Grassmannian OG k and its connection to ABJM scattering amplitudes. We present a canonical embedding of OG k into the Grassmannian Gr( k , 2 k ), from which we deduce the canonical volume form that is invariant under equivalence moves. Remarkably the canonical forms of all reducible graphs can be converted into reduced ones with products of d log forms. Unlike super Yang–Mills, here the Jacobian plays a crucial role to ensure the d log form of the reduced representation. Furthermore in the positive region, we identify the functional map that arises from the triangle equivalence move as a 3-string scattering S -matrix which satisfies the tetrahedron equations by Zamolodchikov, implying (2 + 1)-dimensional integrability. We discuss the solution to the BCFW recursion relation for loop amplitudes, and demonstrate the presence of all physical singularities as well as the absence of all spurious ones. The on-shell diagram solution to the loop recursion relation exhibits manifest two-site cyclic symmetry and reveals that, to all loop, four and six-point amplitudes only have logarithmic singularities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

DiffLense: a conditional diffusion model for super-resolution of gravitational lensing data

Abstract Gravitational lensing data is frequently collected at low resolution due to instrumental limitations and observing conditions. Machine learning-based super-resolution techniques offer a method to enhance the resolution of these images, enabling more precise measurements of lensing effects and a better understanding of the matter distribution in the lensing system. This enhancement can significantly improve our knowledge of the distribution of mass within the lensing galaxy and its environment, as well as the properties of the background source being lensed. Traditional super-resolution techniques typically learn a mapping function from lower-resolution to higher-resolution samples. However, these methods are often constrained by their dependence on optimizing a fixed distance function, which can result in the loss of intricate details crucial for astrophysical analysis. In this work, we introduce DiffLense , a novel super-resolution pipeline based on a conditional diffusion model specifically designed to enhance the resolution of gravitational lensing images obtained from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). Our approach adopts a generative model, leveraging the detailed structural information present in Hubble space telescope (HST) counterparts. The diffusion model, trained to generate HST data, is conditioned on HSC data pre-processed with denoising techniques and thresholding to significantly reduce noise and background interference. This process leads to a more distinct and less overlapping conditional distribution during the model’s training phase. We demonstrate that DiffLense outperforms existing state-of-the-art single-image super-resolution techniques, particularly in retaining the fine details necessary for astrophysical analyses.

Computer Science↗