Diffusion-MBIR: 3D sparse view CT reconstruction from 2D diffusion
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3D reconstruction↗
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Laser ultrasonic imaging of subsurface defects with the linear sampling method
Laser ultrasonics is a remote nondestructive evaluation technique suitable for real-time monitoring of fabrication processes in semiconductor metrology, advanced manufacturing, and other applications where non-contact, high fidelity measurements are required. Here we investigate laser ultrasonic data processing approaches to reconstruct images of subsurface side drilled holes in aluminum alloy specimens. We demonstrate through simulation that the model-based linear sampling method (LSM) can perform accurate shape reconstruction of single and multiple holes and produce images with well-defined boundaries. We experimentally confirm that LSM produces images that represent the internal geometric features of an object, some of which may be missed by conventional imaging.
42 ENGINEERING↗
An advanced model-based diagnosis engine
We have developed a new and powerful diagnosis engine that overcomes the limitations of the existing systematic methods of general diagnosis through a two-fold approach. First, we propose a novel and compact reconstruction of the General Diagnosis Engine, one of the most fundamental approaches to model-based diagnoses. We then present a novel algorithmic approach for calculation of minimal diagnosis set.
model-based diagnosis hitting set problem integer ↗
A Model-based Interpretation of Low-frequency Changes in the Carbon Cycle during the Last 120,000 years and its Implications for the Reconstruction of Atmospheric (delta) 14-C
A main caveat in the interpretation of observed changes in atmospheric (Delta)C-l4 during the last 50,000 years is the unknown variability of the carbon cycle, which together with changes in the C-14 production rates determines the C-14 dynamics. A plausible scenario explaining glacial/interglacial dynamics seen in atmospheric CO2 and (delta)C-13 was proposed recently (Kohler et al., 2005a). A similar approach that expands its interpretation to the C-14 cycle is an important step toward a deeper understanding of (Delta)C-14 variability. This approach is based on an ocean/atmosphere/biosphere box model of the global carbon cycle (BICYCLE) to reproduce low-frequency changes in atmospheric CO2 as seen in Antarctic ice cores. The model is forced forward in time by various paleoclimatic records derived from ice and sediment cores. The simulation results of our proposed scenario match a compiled CO2 record from various ice cores during the last 120,000 years with high accuracy (r(sup 2) = 0.89). We analyze scenarios with different C-14 production rates, which are either constant or based on Be-10 measured in Greenland ice cores or the recent high-resolution geomagnetic field reconstruction GLOPIS-75 and compare them with the available (Delta)C-14 data covering the last 50,000 years. Our results suggest that during the last glacial cycle in general less than 110%0o f the increased atmospheric (Delta)C-14 is based on variations in the carbon cycle, while the largest part (5/6) of the variations has to be explained by other factors. Glacial atmospheric (Delta)C-14 larger than 700% cannot not be explained within our framework, neither through carbon cycle-based changes nor through variable C-14 production. Superimposed on these general trends might lie positive anomalies in atmospheric (Delta)C-14 of approx. 50% caused by millennial-scale variability of the northern deep water production during Heinrich events and Dansgaard/Oeschger climate fluctuations. According to our model, the dominant processes that increase glacial (Delta)C-14 are a reduced glacial ocean circulation (+ approx.40%0), a restricted glacial gas exchange between the atmosphere and the surface ocean through sea ice coverage (+ approx. 20%), and the enrichment of dissolved inorganic carbon with C-14 in the surface waters through isotopic fractionation during higher glacial marine export production caused by iron fertilization (+ approx.10%).
Koehler, Peter↗
XCal: model-based approach to X-ray CT spectral calibration
Transmission X-ray computed tomography (CT) is widely used to quantitatively reconstruct 3D objects composed of multiple materials. However, accurate CT reconstruction requires the system to be calibrated to account for the effective X-ray spectrum. Unfortunately, measurement of the effective spectrum is ill-posed, and existing calibration methods require that the system be recalibrated when the system parameters are changed. In this paper, we propose XCal, a multi-energy model-based spectral calibration approach for X-ray CT. The XCal approach models the effective spectrum using a separable physics-based model of the CT system. The model parameters are then estimated by fitting calibration data with known objects at multiple energies. An important advantage of XCal is that it allows the user to change scanner settings, such as the source voltage or X-ray filters, without the need for recalibration. Evaluations on simulated and measured datasets demonstrate that XCal significantly improves the accuracy of the estimated spectrum as compared to existing calibration methods.
Li, Wenrui [Purdue Univ., West Lafayette, IN (Unit↗
ResSR: A Computationally Efficient Residual Approach to Super-Resolving Multispectral Images
Multispectral imaging (MSI) plays a critical role in material classification, environmental monitoring, and remote sensing. However, MSI sensors typically have wavelength-dependent resolution, which limits downstream analysis. MSI super-resolution (MSI-SR) methods address this limitation by reconstructing all bands at a common high spatial resolution. Existing methods can achieve high reconstruction quality but often rely on spatially-coupled optimization or large learning-based models, leading to significant computational cost and limiting their use in large-scale or time-critical settings. In this paper, we introduce ResSR, a computationally efficient, model-based MSI-SR method that achieves high-quality reconstruction without supervised training or spatially-coupled optimization. Notably, ResSR decouples spectral and spatial processing into two sequential steps. ResSR first computes a spectrally-informed high-resolution estimate of the MSI using singular value decomposition together with a spatially-decoupled approximate forward model. It then applies a residual correction step to restore low-frequency spatial consistency while preserving high-frequency detail recovered by the spectral reconstruction. ResSR achieves comparable or improved reconstruction quality relative to existing MSI-SR methods while being
Sullivan, Haley [ORNL] (ORCID:0000000274069217)↗
Aliasing as noise - A quantitative and qualitative assessment
We present a model-based argument that, for the purposes of system design and digital image processing, aliasing should be treated as signal-dependent additive noise. By using a computational simulation based on this model, we process (high resolution images of) natural scenes in a way which enables the 'aliased component' of the reconstructed image to be isolated unambiguously. We demonstrate that our model-based argument leads naturally to system design metrics which quantify the extent of aliasing. And, by illustrating several aliased component images, we provide a qualitative assessment of aliasing as noise.
Park, Stephen K.↗
Fidelity-preserving enhancement of ptychography with foundational text-to-image models
Ptychographic phase retrieval enables high-resolution imaging of complex samples but often suffers from artifacts such as grid pathology and multislice crosstalk, which degrade reconstructed images. We propose a plug-and-play (PnP) framework that integrates physics model-based phase retrieval with text-guided image editing using foundational diffusion models. By employing the alternating direction method of multipliers, our approach ensures consensus between data fidelity and artifact removal subproblems, maintaining physical consistency while enhancing image quality. Artifact removal is achieved using a text-guided diffusion image editing method (LEDITS++) with a pre-trained foundational diffusion model, allowing users to specify artifacts for removal in natural language. Demonstrations on simulated and experimental datasets show significant improvements in artifact suppression and structural fidelity, validated by metrics such as peak signal-to-noise ratio and diffraction pattern consistency. This work highlights the combination of text-guided generative models and model-based phase retrieval algorithms as a transferable and fidelity-preserving method for high-quality diffraction imaging.
image editing↗
High-dimensional maximum-entropy phase space tomography using normalizing flows
Particle accelerators generate charged-particle beams with tailored distributions in six-dimensional position-momentum space (phase space). Knowledge of the phase space distribution enables model-based beam optimization and control. In the absence of direct measurements, the distribution must be tomographically reconstructed from its projections. In this paper, we highlight that such problems can be severely underdetermined and that entropy maximization is the most conservative solution strategy. We leverage —invertible generative models—to extend maximum-entropy tomography to six-dimensional phase space and perform numerical experiments to validate the model's performance. Our numerical experiments demonstrate consistency with exact two-dimensional maximum-entropy solutions and the ability to fit complicated six-dimensional distributions to large measurement sets in reasonable time. Published by the American Physical Society 2024
43 PARTICLE ACCELERATORS↗
Efficient Model-Based Diagnosis Engine
An efficient diagnosis engine - a combination of mathematical models and algorithms - has been developed for identifying faulty components in a possibly complex engineering system. This model-based diagnosis engine embodies a twofold approach to reducing, relative to prior model-based diagnosis engines, the amount of computation needed to perform a thorough, accurate diagnosis. The first part of the approach involves a reconstruction of the general diagnostic engine to reduce the complexity of the mathematical-model calculations and of the software needed to perform them. The second part of the approach involves algorithms for computing a minimal diagnosis (the term "minimal diagnosis" is defined below). A somewhat lengthy background discussion is prerequisite to a meaningful summary of the innovative aspects of the present efficient model-based diagnosis engine. In model-based diagnosis, the function of each component and the relationships among all the components of the engineering system to be diagnosed are represented as a logical system denoted the system description (SD). Hence, the expected normal behavior of the engineering system is the set of logical consequences of the SD. Faulty components lead to inconsistencies between the observed behaviors of the system and the SD (see figure). Diagnosis - the task of finding faulty components - is reduced to finding those components, the abnormalities of which could explain all the inconsistencies. The solution of the diagnosis problem should be a minimal diagnosis, which is a minimal set of faulty components. A minimal diagnosis stands in contradistinction to the trivial solution, in which all components are deemed to be faulty, and which, therefore, always explains all inconsistencies.
Fijany, Amir↗
Model-based quantification of image quality
In 1982, Park and Schowengerdt published an end-to-end analysis of a digital imaging system quantifying three principal degradation components: (1) image blur - blurring caused by the acquisition system, (2) aliasing - caused by insufficient sampling, and (3) reconstruction blur - blurring caused by the imperfect interpolative reconstruction. This analysis, which measures degradation as the square of the radiometric error, includes the sample-scene phase as an explicit random parameter and characterizes the image degradation caused by imperfect acquisition and reconstruction together with the effects of undersampling and random sample-scene phases. In a recent paper Mitchell and Netravelli displayed the visual effects of the above mentioned degradations and presented subjective analysis about their relative importance in determining image quality. The primary aim of the research is to use the analysis of Park and Schowengerdt to correlate their mathematical criteria for measuring image degradations with subjective visual criteria. Insight gained from this research can be exploited in the end-to-end design of optical systems, so that system parameters (transfer functions of the acquisition and display systems) can be designed relative to each other, to obtain the best possible results using quantitative measurements.
Hazra, Rajeeb↗
A Hybrid Data-Driven and Model-Based Anomaly Detection Scheme for DER Operation
This paper proposes a hybrid data and model-based anomaly detection scheme to secure the operation of distributed energy resources (DERs) in distribution grids. Data-driven autoencoders are set up at the edge device level and they use local DER operational data as inputs. The abnormal statuses are detected by analyzing reconstruction errors. In parallel, modelbased state estimation (SE) is set up at the central level and it uses system-wide models and measurements as data inputs. The anomalies are identified by analyzing measurement residuals. The hybrid scheme preserves the benefits of both data-driven and model-based analyses and thus improves the robustness and the accuracy of anomaly detection. Numerical tests based on the model of a real distribution feeder in Southern California highlight the proposed scheme's effectiveness and benefits.
anomaly detection↗
Intensification of North American Megadroughts through Surface and Dust Aerosol Forcing
Tree-ring-based reconstructions of the Palmer drought severity index (PDSI) indicate that, during the Medieval Climate Anomaly (MCA), the central plains of North America experienced recurrent periods of drought spanning decades or longer. These megadroughts had exceptional persistence compared to more recent events, but the causes remain uncertain. The authors conducted a suite of general circulation model experiments to test the impact of sea surface temperature (SST) and land surface forcing on the MCA megadroughts over the central plains. The land surface forcing is represented as a set of dune mobilization boundary conditions, derived from available geomorphological evidence and modeled as increased bare soil area and a dust aerosol source (32deg-44degN, 105deg-95degW). In the experiments, cold tropical Pacific SST forcing suppresses precipitation over the central plains but cannot reproduce the overall drying or persistence seen in the PDSI reconstruction. Droughts in the scenario with dust aerosols, however, are amplified and have significantly longer persistence than in other model experiments, more closely matching the reconstructed PDSI. This additional drying occurs because the dust increases the shortwave planetary albedo, reducing energy inputs to the surface and boundary layer. The energy deficit increases atmospheric stability, inhibiting convection and reducing cloud cover and precipitation over the central plains. Results from this study provide the first model-based evidence that dust aerosol forcing and land surface changes could have contributed to the intensity and persistence of the central plains megadroughts, although uncertainties remain in the formulation of the boundary conditions and the future importance of these feedbacks.
Earth surface↗
Artificial Intelligence-Driven Management of Sustainable Energy Resources: Visibility, Operation, and Control
The rapid global transition toward sustainable energy resources (SERs) is reshaping how modern power systems are observed, optimized, and controlled. While SERs have significantly advanced decarbonization, their weather dependence, variability, and inverter-dominated characteristics challenge traditional, centralized, and deterministic grid operation. At the same time, the proliferation of high-resolution data from inverters, smart meters, and sensors offers unprecedented visibility into system dynamics. Yet, it also exceeds the analytical capability of conventional model-based approaches. Artificial intelligence (AI) provides a new foundation for addressing these challenges by bridging physical laws with data-driven learning, enabling accurate state awareness, adaptive operation, and coordinated control across distributed assets. This article examines how AI transforms the management of SER-rich power systems along three critical dimensions: 1) enhancing visibility by inferring behind-the-meter (BTM) activities, assessing SER flexibility, and reconstructing system states from sparse or noisy measurements; 2) improving operation through AI-enhanced SER service provision, volt/var control (VVC), and dynamic operating envelopes (DOE) for efficiency and security; and 3) advancing control by embedding learning-based intelligence into inverter coordination, voltage and frequency regulation, and long-term dispatch. Together, these developments reveal how AI can convert the variability of SERs from an operational challenge into a source of flexibility, resilience, and intelligence, paving the way toward sustainable, adaptive, and self-optimizing power systems.
24 POWER TRANSMISSION AND DISTRIBUTION↗
A multi-resolution approach to hydraulic fracture simulation
Abstract We present a multi-resolution approach for constructing model-based simulations of hydraulic fracturing, wherein flow through porous media is coupled with fluid-driven fracture. The approach consists of a hybrid scheme that couples a discrete crack representation in a global domain to a phase-field representation in a local subdomain near the crack tip. The multi-resolution approach addresses issues such as the computational expense of accurate hydraulic fracture simulations and the difficulties associated with reconstructing crack apertures from diffuse fracture representations. In the global domain, a coupled system of equations for displacements and pressures is considered. The crack geometry is assumed to be fixed and the displacement field is enriched with discontinuous functions. Around the crack tips in the local subdomains, phase-field sub-problems are instantiated on the fly to propagate fractures in arbitrary, mesh independent directions. The governing equations and fields in the global and local domains are approximated using a combination of finite-volume and finite element discretizations. The efficacy of the method is illustrated through various benchmark problems in hydraulic fracturing, as well as a new study of fluid-driven crack growth around a stiff inclusion.
58 GEOSCIENCES↗
Fault Detection via Occupation Kernel Principal Component Analysis
Reliable operation of automatic systems is heavily dependent on the ability to detect faults in the underlying dynamics. While traditional model-based methods have been widely used for fault detection, data-driven approaches have garnered increasing attention due to their ease of deployment and minimal need for expert knowledge. In this letter, we present a novel principal component analysis (PCA) method that uses occupation kernels. Occupation kernels result in feature maps that are tailored to the measured data, have inherent noise-robustness due to the use of integration, and can utilize irregularly sampled system trajectories of variable lengths for PCA. The occupation kernel PCA method is used to develop a reconstruction error approach to fault detection and its efficacy is validated using numerical simulations.
42 ENGINEERING↗
Sub-10 second fly-scan nano-tomography using machine learning
Abstract X-ray computed tomography is a versatile technique for 3D structure characterization. However, conventional reconstruction algorithms require that the sample not change throughout the scan, and the timescale of sample dynamics must be longer than the data acquisition time to fulfill the stable sample requirement. Meanwhile, concerns about X-ray-induced parasite reaction and sample damage have driven research efforts to reduce beam dosage. Here, we report a machine-learning-based image processing method that can significantly reduce data acquisition time and X-ray dose, outperforming conventional approaches like Filtered-Back Projection, maximum-likelihood, and model-based maximum-a-posteriori probability. Applying machine learning, we achieve ultrafast nano-tomography with sub-10 second data acquisition time and sub-50 nm pixel resolution in a transmission X-ray microscope. We apply our algorithm to study dynamic morphology changes in a lithium-ion battery cathode under a heating rate of 50 o C min −1 , revealing crack self-healing during thermal annealing. The proposed method can be applied to various tomography modalities.
36 MATERIALS SCIENCE↗
Qualitative and quantitative enhancement of parameter estimation for model-based diagnostics using automatic differentiation with an application to inertial fusion
Parameter estimation using observables is a fundamental concept in the experimental sciences. Mathematical models that represent the physical processes can enable reconstructions of the experimental observables and greatly assist in parameter estimation by turning it into an optimization problem which can be solved by gradient-free or gradient-based methods. In this work, the recent rise in flexible frameworks for developing differentiable scientific computing programs is leveraged in order to dramatically accelerate data analysis of a common experimental diagnostic relevant to laser–plasma and inertial fusion experiments, Thomson scattering. A differentiable Thomson-scattering data analysis tool is developed that uses reverse-mode automatic differentiation (AD) to calculate gradients. By switching from finite differencing to reverse-mode AD, three distinct outcomes are achieved. First, gradient descent is accelerated dramatically to the extent that it enables near real-time usage in laser–plasma experiments. Second, qualitatively novel quantities which require $\mathcal{O}(10^3)$ parameters can now be included in the analysis of data which enables unprecedented measurements of small-scale laser–plasma phenomena. Third, uncertainty estimation approaches that leverage the value of the Hessian become accurate and efficient because reverse-mode AD can be used for calculating the Hessian.
97 MATHEMATICS AND COMPUTING↗