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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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At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Nuclear forensics: How science helps stop the trafficking of nuclear materials

Nuclear forensics is an essential activity in support of national counter-nuclear smuggling operations, as it can be used to provide unique and useful data to law enforcement for the prosecution of criminal nuclear smugglers and traffickers. Nuclear forensics can also be used to determine the origin of nuclear materials that have fallen out of regulatory control, allowing for the identification of weaknesses and vulnerabilities in the safeguarding of nuclear materials, while also generating a deterrent effect through attribution to the legal and responsible owners of nuclear materials out of regulatory control. This article describes the benefits of a nuclear forensics capability to any country in possession of nuclear materials, and describes how nuclear forensics can support an investigation into the origin of interdicted nuclear materials, using a case study of a nuclear materials interdiction in Australia to describe how nuclear forensics works in practice.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Elephant range and population, strontium isotopes, and genetics combine to give local-scale specificity to ivory hotspot tracking

We use Sr isotopes to increase the precision of DNA-based origin estimates of wildlife products. Population information is used to develop Sr isotope Elephant Polygons that are overlaid onto the region of origin identified by DNA assignment to determine the sources of seized ivory samples. Our approach is cognizant of isotope mixing due to isotope turnover within animals and also of the large home range of elephants or other mobile species. Genetic information from 3 different law enforcement ivory seizures suggests a region of origin confined to Kenya and Tanzania in eastern Africa. We determine characteristic 87 Sr/ 86 Sr ratios for each of 25 different Elephant Polygons within this region using analyses of more the 600 known-origin reference samples. Using both the 87 Sr/ 86 Sr ratios of the seized ivory samples and elephant population estimates from individual Elephant Polygons we find that at least 75 % of the samples likely came from a single Elephant Polygon which includes the Tsavo National Parks in Kenya and the Mkomazi National Park in Tanzania. A few samples may have come from other regions, most likely from Tanzania. This study illustrates the value of combining genetics, isotope geochemistry, and population surveys in wildlife forensics studies.

Africa↗
DOE OSTI2026

Data-based filtered dissipation rate modelling for multi-modal turbulent combustion: evaluating a priori model generalizability

Manifold-based models offer a computationally efficient alternative to directly transporting the thermochemical state in computational simulations of turbulent reacting flows, projecting the high-dimensional thermochemical state-space onto a low-dimensional manifold. Recent efforts have yielded a manifold-based model applicable to multi-modal combustion, enabling reconstruction of the thermochemical state from solutions to two-dimensional manifold equations in mixture fraction and generalized progress variable that are parameterised by three scalar dissipation rates. In coarse-grained simulations such as Large Eddy Simulation (LES), closure of the multi-modal manifold equations and subfilter variances/covariance requires closure of three filtered scalar dissipation rates. Here, the present work adopts a data-based approach, providing closure for the three filtered scalar dissipation rates via deep neural networks (DNNs). High-fidelity datasets corresponding to an autoigniting n-dodecane jet flame and a bluff body swirl-stabilized confined lifted spray flame of two aviation fuels (Jet-A and C1) with different ignition propensities are leveraged to generate training data that spans a diverse range of thermodynamic conditions and combustion modes, including low- and high-temperature ignition regimes in addition to premixed and nonpremixed behaviour. A final DNN model is trained to enforce inherent physical constraints by learning nonlinear functional transformations of the three filtered scalar dissipation rates. The generalizability of this constrained DNN model is demonstrated a priori via conditional statistics evaluated on the lifted spray flame with C1–a configuration that had not been included in the training data. Excellent DNN agreement with conditional DNS statistics is observed, and integrated gradients are computed to identify the most sensitive input variables. The similarity of the marginal PDFs of the most informative input variables and outputs across configurations are quantified via the Wasserstein metric, demonstrating that data-based models may successfully generalize to unseen parametric conditions so long as the most informative input variables share similar distributions across training and testing datasets.

Data-based modelling↗
DOE OSTI2015

A stochastic approach to quantifying the blur with uncertainty estimation for high-energy X-ray imaging systems

One of the primary causes of blur in a high-energy X-ray imaging system is the shape and extent of the radiation source, or ‘spot’. It is important to be able to quantify the size of the spot as it provides a lower bound on the recoverable resolution for a radiograph, and penumbral imaging methods – which involve the analysis of blur caused by a structured aperture – can be used to obtain the spot’s spatial profile. We present a Bayesian approach for estimating the spot shape that, unlike variational methods, is robust to the initial choice of parameters. The posterior is obtained from a normal likelihood, which was constructed from a weighted least squares approximation to a Poisson noise model, and prior assumptions that enforce both smoothness and non-negativity constraints. A Markov chain Monte Carlo algorithm is used to obtain samples from the target posterior, and the reconstruction and uncertainty estimates are the computed mean and variance of the samples, respectively. Lastly, synthetic data-sets are used to demonstrate accurate reconstruction, while real data taken with high-energy X-ray imaging systems are used to demonstrate applicability and feasibility.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Rigid monoclonal antibodies improve detection of SARS-CoV-2 nucleocapsid protein

Monoclonal antibodies (mAbs) are the basis of treatments and diagnostics for pathogens and other biological phenomena. We conducted a structural characterization of mAbs against the N-terminal domain of nucleocapsid protein (NPNTD) from SARS-CoV-2 using small-angle X-ray scattering and transmission electron microscopy. Our solution-based results distinguished the mAbs’ flexibility and how this flexibility affects the assembly of multiple mAbs on an antigen. By pairing two mAbs that bind different epitopes on the NPNTD, we show that flexible mAbs form a closed sandwich-like complex. With rigid mAbs, a juxtaposition of the antigen-binding fragments is prevented, enforcing a linear arrangement of the mAb pair, which facilitates further mAb polymerization. In a modified sandwich enzyme-linked immunosorbent assay, we show that rigid mAb-pairings with linear polymerization led to increased NPNTD detection sensitivity. These enhancements can expedite the development of more sensitive and selective antigen-detecting point-of-care lateral flow devices, which are critical for early diagnosis and epidemiological studies of SARS-CoV-2 and other pathogens.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Machine learning for energy-water nexus: challenges and opportunities

Modeling the interactions of water and energy systems is important to the enforcement of infrastructure security and system sustainability. To this end, recent technological advancement has allowed the production of large volumes of data associated with functioning of these sectors. We are beginning to see that statistical and machine learning techniques can help elucidate characteristic patterns across these systems from water availability, transport, and use to energy generation, fuel supply, and customer demand, and in the interdependencies among these systems that can leave these systems vulnerable to cascading impacts from single disruptions. In this paper, we discuss ways in which data and machine learning can be applied to the challenges facing the energy-water nexus along with the potential issues associated with the machine learning techniques themselves. We then survey machine learning techniques that have found application to date in energy-water nexus problems. We conclude by outlining future research directions and opportunities for collaboration among the energy-water nexus and machine learning communities that can lead to mutual synergistic advantage.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A shared-mobility-based framework for evacuation planning and operations under forecast uncertainty

To meet evacuation needs from carless populations who need personalized assistance to evacuate safely, in this article we propose a ridesharing-based evacuation program that recruits volunteer drivers before a disaster strikes, and then matches volunteer drivers with evacuees once demand is realized. Here we optimize resource planning and evacuation operations under uncertain spatiotemporal demand, and construct a two-stage stochastic mixed-integer program to ensure high demand fulfillment rates. We consider three formulations to improve the number of evacuees served, by minimizing an expected penalty cost, imposing a probabilistic constraint, and enforcing a constraint on the conditional value at risk of the total number of unserved evacuees, respectively. We discuss the benefits and disadvantages of the different risk measures used in the three formulations, given certain carless population sizes and the variety of evacuation modes available. We also develop a heuristic approach to provide quick, dynamic and conservative solutions. We demonstrate the performance of our approaches using five different networks of varying sizes based on regions of Charleston County, South Carolina, an area that experienced a mandatory evacuation order during Hurricane Florence, and utilize real demographic data and hourly traffic count data to estimate the demand distribution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

MAGNETIC RECONNECTION, HELICITY DYNAMICS, AND HYPER-DIFFUSION

We examine the influence of noise and Alfven wave turbulence on magnetic reconnection in a reduced magnetohydrodynamics model. We focus on the dynamics of magnetic helicity density. Helicity conservation is then used to calculate the global reconnection rate in terms of the helicity density flux. Two specific scenarios are explored-noisy reconnection and Alfven wave turbulent reconnection. For noisy reconnection, the current sheet is assumed to sit in a noisy state, marginal to plasmoid formation instability. The scaling of the reconnection rate in the presence of noise is proportional to (S {sup 2}{sub 0}/V{sub A}L {sup 2}){sup 1/11}, where S {sup 2}{sub 0}/V{sub A}L {sup 2} is the relative amplitude of the noise. We obtain this prediction using a symmetry analysis of the helicity density flux. For Alfven wave turbulent reconnection, a mean field closure scheme is applied. A reconnection rate proportional to (< B-tilde {sup 2}>/ {sup 2}){sup 1/8} is obtained, where < B-tilde {sup 2}>/ {sup 2} and (B) are the relative energy of Alfven wave turbulence and the reconnecting field. The constraint on reconnection rate enforced mean-square magnetic potential conservation is reexamined. A critical magnetic Reynolds number R{sub m,c} is identified. For R{sub m} >> R{sub m,c}, the reconnection rate becomes independent of Spitzer resistivity and thus can be higher than the Sweet-Parker model prediction. Both cases exhibit a weak dependence of the reconnection rate on the amplitude of the turbulence. Therefore, even noise or weak turbulence can trigger fast reconnection if the system is marginally stable. The important distinction between turbulent reconnection and turbulent dissipation of magnetic energy is also discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2012

GALAXY-MASS CORRELATIONS ON 10 Mpc SCALES IN THE DEEP LENS SURVEY

We examine the projected correlation of galaxies with mass from small scales (<few hundred kpc), where individual dark matter halos dominate, out to 15 Mpc, where correlated large-scale structure dominates. We investigate these profiles as a function of galaxy luminosity and redshift. Selecting 0.8 million galaxies in the Deep Lens Survey, we use photometric redshifts and stacked weak gravitational lensing shear tomography out to radial scales of 1 Degree-Sign from the centers of foreground galaxies. We detect correlated mass density from multiple halos and large-scale structure at radii larger than the virial radius and find the first observational evidence for growth in the galaxy-mass correlation on 10 Mpc scales with decreasing redshift and fixed range of luminosity. For a fixed range of redshift, we find a scaling of projected halo mass with rest-frame luminosity similar to previous studies at lower redshift. We control systematic errors in shape measurement and photometric redshift, enforce volume completeness through absolute magnitude cuts, and explore residual sample selection effects via simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

REGULATION OF THE SPECTRAL PEAK IN GAMMA-RAY BURSTS

Observations indicate that the peak of a gamma-ray burst spectrum forms in the opaque region of an ultrarelativistic jet. Recent radiative transfer calculations support this picture and show that the spectral peak is inherited from initially thermal radiation, which is changed by heating into a broad photon distribution with a high-energy tail. We discuss the processes that regulate the observed position of the spectral peak E {sub pk}. The opaque jet has three radial zones: (1) the Planck zone r < R {sub P} where a blackbody spectrum is enforced; this zone ends where the Thomson optical depth decreases to {tau} Almost-Equal-To 10{sup 5}, (2) the Wien zone R {sub P} < r < R {sub W} with a Kompaneets parameter y >> 1 where radiation has a Bose-Einstein spectrum, and (3) the Comptonization zone r > R {sub W} where the radiation spectrum develops a high-energy tail. Besides the initial jet temperature, an important factor regulating E {sub pk} is internal dissipation (of bulk motions and magnetic energy) at large distances from the central engine. Dissipation in the Planck zone reduces E {sub pk}, and dissipation in the Wien zone can increase E {sub pk}. In jets with subdominant magnetic fields, the predicted E {sub pk} varies around 1 MeV up to a maximum value of about 10 MeV. If the jet carries an energetically important magnetic field, E {sub pk} can be additionally increased by dissipation of magnetic energy. This increase is suggested by observations, which show E {sub pk} up to about 20 MeV. We also consider magnetically dominated jets; then a simple model of magnetic dissipation gives E {sub pk} Almost-Equal-To 30 {Gamma}{sub W} keV where {Gamma}{sub W} is the jet Lorentz factor at the Wien radius R {sub W}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

The neutron star mass distribution

In recent years, the number of pulsars with secure mass measurements has increased to a level that allows us to probe the underlying neutron star (NS) mass distribution in detail. We critically review the radio pulsar mass measurements. For the first time, we are able to analyze a sizable population of NSs with a flexible modeling approach that can effectively accommodate a skewed underlying distribution and asymmetric measurement errors. We find that NSs that have evolved through different evolutionary paths reflect distinctive signatures through dissimilar distribution peak and mass cutoff values. NSs in double NS and NS-white dwarf (WD) systems show consistent respective peaks at 1.33 M {sub ☉} and 1.55 M {sub ☉}, suggesting significant mass accretion (Δm ≈ 0.22 M {sub ☉}) has occurred during the spin-up phase. The width of the mass distribution implied by double NS systems is indicative of a tight initial mass function while the inferred mass range is significantly wider for NSs that have gone through recycling. We find a mass cutoff at ∼2.1 M {sub ☉} for NSs with WD companions, which establishes a firm lower bound for the maximum NS mass. This rules out the majority of strange quark and soft equation of state models as viable configurations for NS matter. The lack of truncation close to the maximum mass cutoff along with the skewed nature of the inferred mass distribution both enforce the suggestion that the 2.1 M {sub ☉} limit is set by evolutionary constraints rather than nuclear physics or general relativity, and the existence of rare supermassive NSs is possible.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Weighing 'El Gordo' with a precision scale: Hubble space telescope weak-lensing analysis of the merging galaxy cluster ACT-CL J0102–4915 at z = 0.87

We present a Hubble Space Telescope weak-lensing study of the merging galaxy cluster 'El Gordo' (ACT-CL J0102–4915) at z = 0.87 discovered by the Atacama Cosmology Telescope (ACT) collaboration as the strongest Sunyaev-Zel'dovich decrement in its ∼1000 deg{sup 2} survey. Our weak-lensing analysis confirms that ACT-CL J0102–4915 is indeed an extreme system consisting of two massive (≳ 10{sup 15} M {sub ☉} each) subclusters with a projected separation of ∼0.7 h{sub 70}{sup −1} Mpc. This binary mass structure revealed by our lensing study is consistent with the cluster galaxy distribution and the dynamical study carried out with 89 spectroscopic members. We estimate the mass of ACT-CL J0102–4915 by simultaneously fitting two axisymmetric Navarro-Frenk-White (NFW) profiles allowing their centers to vary. We use only a single parameter for the NFW mass profile by enforcing the mass-concentration relation from numerical simulations. Our Markov-Chain-Monte-Carlo analysis shows that the masses of the northwestern (NW) and the southeastern (SE) components are M{sub 200c}=(1.38±0.22)×10{sup 15} h{sub 70}{sup −1} M{sub ⊙} and (0.78±0.20)×10{sup 15} h{sub 70}{sup −1} M{sub ⊙}, respectively, where the quoted errors include only 1σ statistical uncertainties determined by the finite number of source galaxies. These mass estimates are subject to additional uncertainties (20%-30%) due to the possible presence of triaxiality, correlated/uncorrelated large scale structure, and departure of the cluster profile from the NFW model. The lensing-based velocity dispersions are 1133{sub −61}{sup +58} km s{sup −1} and 1064{sub −66}{sup +62} km s{sup −1} for the NW and SE components, respectively, which are consistent with their spectroscopic measurements (1290 ± 134 km s{sup –1} and 1089 ± 200 km s{sup –1}, respectively). The centroids of both components are tightly constrained (∼4'') and close to the optical luminosity centers. The X-ray and mass peaks are spatially offset by ∼8'' (∼62 h{sub 70}{sup −1} kpc), which is significant at the ∼2σ confidence level. The mass peak, however, does not lead the gas peak in the direction expected if we are viewing the cluster soon after first core passage during a high speed merger. Under the assumption that the merger is happening in the plane of the sky, extrapolation of the two NFW halos to a radius r{sub 200a}=2.4 h{sub 70}{sup −1} Mpc yields a combined mass of M{sub 200a}=(3.13±0.56)×10{sup 15} h{sub 70}{sup −1} M{sub ⊙}. This extrapolated total mass is consistent with our two-component-based dynamical analysis and previous X-ray measurements, projecting ACT-CL J0102–4915 to be the most massive cluster at z > 0.6 known to date.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Poynting-flux-dominated jets challenged by their photospheric emission

One of the key open question in the study of jets, in general, and jets in gamma-ray bursts (GRBs), in particular, is the magnetization of the outflow. Here we consider the photospheric emission of Poynting-flux-dominated outflows, when the dynamics is mediated by magnetic reconnection. We show that thermal three-particle processes, responsible for the thermalization of the plasma, become inefficient at a radius of r{sub sup}∼10{sup 9.5} cm, far below the photosphere, at ∼10{sup 11.5} cm. Conservation of the total photon number above r{sub sup} combined with Compton scattering below the photosphere enforces kinetic equilibrium between electrons and photons. This, in turn, leads to an increase in the observed photon temperature, which reaches ≳8 MeV (observed energy) when decoupling the plasma at the photosphere. This result is weakly dependent on the free model parameters. We show that in this case, the expected thermal luminosity is a few percent of the total luminosity, and could therefore be detected. The predicted peak energy is more than an order of magnitude higher than the observed peak energy of most GRBs, which puts strong constraints on the magnetization of these outflows.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

On obliquely magnetized and differentially rotating stars

We investigate the interaction of differential rotation and a misaligned magnetic field. The incompressible magnetohydrodynamic equations are solved numerically for a free-decay problem. In the kinematic limit, differential rotation annihilates the non-axisymmetric field on a timescale proportional to the cube root of magnetic Reynolds number (Rm), as predicted by Rädler. Nonlinearly, the outcome depends upon the initial energy in the non-axisymmetric part of the field. Sufficiently weak fields approach axisymmetry as in the kinematic limit; some differential rotation survives across magnetic surfaces, at least on intermediate timescales. Stronger fields enforce uniform rotation and remain non-axisymmetric. The initial field strength that divides these two regimes does not follow the scaling Rm{sup −1/3} predicted by quasi-kinematic arguments, perhaps because our Rm is never sufficiently large or because of reconnection. We discuss the possible relevance of these results to tidal synchronization and tidal heating of close binary stars, particularly double white dwarfs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

ICECUBE NEUTRINOS AND LORENTZ INVARIANCE VIOLATION

The IceCube neutrino telescope has found so far no evidence of gamma-ray burst (GRB) neutrinos. We here notice that these results assume the same travel times from source to telescope for neutrinos and photons, an assumption that is challenged by some much-studied pictures of spacetime quantization. We briefly review previous results suggesting that limits on quantum-spacetime effects obtained for photons might not be applicable to neutrinos, and we then observe that the outcome of GRB-neutrino searches could depend strongly on whether one allows for neutrinos to be affected by the minute effects of Lorentz invariance violation (LIV) predicted by some relevant quantum-spacetime models. We discuss some relevant issues using as an illustrative example three neutrinos that were detected by IceCube in good spatial coincidence with GRBs, but hours before the corresponding gamma rays. In general, this could happen if the earlier arrival reflects quantum-spacetime-induced LIV, but, as we stress, some consistency criteria must be enforced in order to properly test such a hypothesis. Our analysis sets the stage for future GRB-neutrino searches that could systematically test the possibility of quantum-spacetime-induced LIV.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

THE NASA AMES PAH IR SPECTROSCOPIC DATABASE VERSION 2.00: UPDATED CONTENT, WEB SITE, AND ON(OFF)LINE TOOLS

A significantly updated version of the NASA Ames PAH IR Spectroscopic Database, the first major revision since its release in 2010, is presented. The current version, version 2.00, contains 700 computational and 75 experimental spectra compared, respectively, with 583 and 60 in the initial release. The spectra span the 2.5-4000 μm (4000-2.5 cm{sup -1}) range. New tools are available on the site that allow one to analyze spectra in the database and compare them with imported astronomical spectra as well as a suite of IDL object classes (a collection of programs utilizing IDL's object-oriented programming capabilities) that permit offline analysis called the AmesPAHdbIDLSuite. Most noteworthy among the additions are the extension of the computational spectroscopic database to include a number of significantly larger polycyclic aromatic hydrocarbons (PAHs), the ability to visualize the molecular atomic motions corresponding to each vibrational mode, and a new tool that allows one to perform a non-negative least-squares fit of an imported astronomical spectrum with PAH spectra in the computational database. Finally, a methodology is described in the Appendix, and implemented using the AmesPAHdbIDLSuite, that allows the user to enforce charge balance during the fitting procedure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems

We develop a framework for goal-oriented optimal design of experiments (GOODE) for large-scale Bayesian linear inverse problems governed by PDEs. This framework differs from classical Bayesian optimal design of experiments (ODE) in the following sense: we seek experimental designs that minimize the posterior uncertainty in the experiment end-goal, e.g. a quantity of interest (QoI), rather than the estimated parameter itself. This is suitable for scenarios in which the solution of an inverse problem is an intermediate step and the estimated parameter is then used to compute a QoI. In such problems, a GOODE approach has two benefits: the designs can avoid wastage of experimental resources by a targeted collection of data, and the resulting design criteria are computationally easier to evaluate due to the often low-dimensionality of the QoIs. We present two modified design criteria, A-GOODE and D-GOODE, which are natural analogues of classical Bayesian A- and D-optimal criteria. We analyze the connections to other ODE criteria, and provide interpretations for the GOODE criteria by using tools from information theory. Then, we develop an efficient gradient-based optimization framework for solving the GOODE optimization problems. Additionally, we present comprehensive numerical experiments testing the various aspects of the presented approach. The driving application is the optimal placement of sensors to identify the source of contaminants in a diffusion and transport problem. As a result, we enforce sparsity of the sensor placements using an L1-norm penalty approach, and propose a practical strategy for specifying the associated penalty parameter.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Where did the tumor start? An inverse solver with sparse localization for tumor growth models

In this work, we present a numerical scheme for solving an inverse problem for parameter estimation in tumor growth models for glioblastomas, a form of aggressive primary brain tumor. The growth model is a reaction–diffusion partial differential equation (PDE) for the tumor concentration. We use a PDE-constrained optimization formulation for the inverse problem. The unknown parameters are the reaction coefficient (proliferation), the diffusion coefficient (infiltration), and the initial condition field for the tumor PDE. Segmentation of magnetic resonance imaging (MRI) scans drive the inverse problem where segmented tumor regions serve as partial observations of the tumor concentration. Like most cases in clinical practice, we use data from a single time snapshot. Moreover, the precise time relative to the initiation of the tumor is unknown, which poses an additional difficulty for inversion. We perform a frozen-coefficient spectral analysis and show that the inverse problem is severely ill-posed. We introduce a biophysically motivated regularization on the structure and magnitude of the tumor initial condition. In particular, we assume that the tumor starts at a few locations (enforced with a sparsity constraint on the initial condition of the tumor) and that the initial condition magnitude in the maximum norm is equal to one. We solve the resulting optimization problem using an inexact quasi-Newton method combined with a compressive sampling algorithm for the sparsity constraint. Our implementation uses PETSc and AccFFT libraries. We conduct numerical experiments on synthetic and clinical images to highlight the improved performance of our solver over a previously existing solver that uses standard two-norm regularization for the calibration parameters. The existing solver is unable to localize the initial condition. Our new solver can localize the initial condition and recover infiltration and proliferation. In clinical datasets (for which the ground truth is unknown), our solver results in qualitatively different solutions compared to the two-norm regularized solver.

97 MATHEMATICS AND COMPUTING↗