A language to simplify computation of differential mobility analyzer response functions
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Results for “Inverse Optimization”
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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Two-dimensional inverse planning and delivery with a preclinical image guided microirradiator
Purpose: Recent advances in preclinical radiotherapy systems have provided the foundation for scaling many of the elements of clinical radiation therapy practice to the dimensions and energy demanded in small animal studies. Such systems support the technical capabilities to accurately deliver highly complex dose distributions, but methods to optimize and deliver such distributions remain in their infancy. This study developed an optimization method based on empirically measured two-dimensional dose kernel measurements to deliver arbitrary planar dose distributions on a recently developed small animal radiotherapy platform.Methods: A two-dimensional dose kernel was measured with repeated radiochromic film measurements for the circular 1 mm diameter fixed collimator of the small animal radiotherapy system at 1 cm depth in a solid water phantom. This kernel was utilized in a sequential quadratic programming optimization framework to determine optimal beam positions and weights to deliver an arbitrary desired dose distribution. The positions and weights were then translated to a set of stage motions to automatically deliver the optimized dose distribution. End-to-end efficacy of the framework was quantified through five repeated deliveries of two dosimetric challenges: (1) a 5 mm radius bullseye distribution, and (2) a “sock” distribution contained within a 9 × 13 mm bounding box incorporating rectangular, semicircular, and exponentially decaying geometric constructs and a rectangular linear dose gradient region. These two challenges were designed to gauge targeting, geometric, and dosimetric fidelity.Results: Optimization of the bullseye and sock distributions required 2.1 and 5.9 min and utilized 50 and 77 individual beams for delivery, respectively. Automated delivery of the resulting optimized distributions, validated using radiochromic film measurements, revealed an average targeting accuracy of 0.32 mm, and a dosimetric delivery error along four line profiles taken through the sock distribution of 3.9%. Mean absolute delivery error across the 0–1 Gy linear dose gradient over 7.5 mm was 0.01 Gy.Conclusions: The work presented here demonstrates the potential for complex dose distributions to be planned and automatically delivered with millimeter scale heterogeneity at submillimeter accuracy. This capability establishes the technical foundation for preclinical validation of biologically guided radiotherapy investigations and development of unique radiobiological experiments.
61 RADIATION PROTECTION AND DOSIMETRY↗
Avalanching strain dynamics during the hydriding phase transformation in individual palladium nanoparticles
Phase transitions in reactive environments are crucially important in energy and information storage, catalysis and sensors. Nanostructuring active particles can yield faster charging/ discharging kinetics, increased lifespan and record catalytic activities. However, establishing the causal link between structure and function is challenging for nanoparticles, as ensemble measurements convolve intrinsic single-particle properties with sample diversity. Here we study the hydriding phase transformation in individual palladium nanocubes in situ using coherent X-ray diffractive imaging. The phase transformation dynamics, which involve the nucleation and propagation of a hydrogen-rich region, are dependent on absolute time (aging) and involve intermittent dynamics (avalanching). A hydrogen-rich surface layer dominates the crystal strain in the hydrogen-poor phase, while strain inversion occurs at the cube corners in the hydrogen-rich phase. A three-dimensional phase-field model is used to interpret the experimental results. In conclusion, our experimental and theoretical approach provides a general framework for designing and optimizing phase transformations for single nanocrystals in reactive environments.
36 MATERIALS SCIENCE↗
The constrained architecture of mammalian Hox gene clusters
In many animal species with a bilateral symmetry, Hox genes are clustered either at one or at several genomic loci. This organization has a functional relevance, as the transcriptional control applied to each gene depends upon its relative position within the gene cluster. It was previously noted that vertebrate Hox clusters display a much higher level of genomic organization than their invertebrate counterparts. The former are always more compact than the latter, they are generally devoid of repeats and of interspersed genes, and all genes are transcribed by the same DNA strand, suggesting that particular factors constrained these clusters toward a tighter structure during the evolution of the vertebrate lineage. Here, we investigate the importance of uniform transcriptional orientation by engineering several alleles within the HoxD cluster, such as to invert one or several transcription units, with or without a neighboring CTCF site. We observe that the association between the tight structure of mammalian Hox clusters and their regulation makes inversions likely detrimental to the proper implementation of this complex genetic system. We propose that the consolidation of Hox clusters in vertebrates, including transcriptional polarity, evolved in conjunction with the emergence of global gene regulation via the flanking regulatory landscapes, to optimize a coordinated response of selected subsets of target genes in cis .
59 BASIC BIOLOGICAL SCIENCES↗
Topology optimization of 3D photonic crystals with complete bandgaps
The design of photonic crystals with complete bandgaps has recently received considerable research focus for numerous reasons. This work leverages well-known nonlinear programming techniques to alleviate the non-smoothness caused by degenerate eigenvalues such that topology optimization problems can be solved with the open-source IPOPT software. A fully-vectorial plane wave expansion technique is used with an iterative eigensolver to efficiently predict dispersion properties of candidate structures. Nonlinear programming is employed to solve the inverse problem of designing three-dimensional periodic structures that exhibit complete two-dimensional (2D) and three-dimensional (3D) photonic bandgaps. Mesh refinement is performed to alleviate the large computational burden of designing and analyzing photonic crystals, and a periodic density filter is implemented to impose a minimum feature size for manufacturability considerations.
97 MATHEMATICS AND COMPUTING↗
Standard, Random, and Optimum Array conversions from Two-Pole resistance data
We present an array evaluation of standard and nonstandard arrays over a hydrogeological target. We develop the arrays by linearly combining data from the pole-pole (or 2-pole) array. The first test shows that reconstructed resistances for the standard Schlumberger and dipoledipole arrays are equivalent or superior to the measured arrays in terms of noise, especially at large geometric factors. The inverse models for the standard arrays also confirm what others have presented in terms of target resolvability, namely the dipole-dipole array has the highest resolution. In the second test, we reconstruct random electrode combinations from the 2-pole data segregated into inner, outer, and overlapping dipoles. The resistance data and inverse models from these randomized arrays show those with inner dipoles to be superior in terms of noise and resolution and that overlapping dipoles can cause model instability and low resolution. Finally, we use the 2-pole data to create an optimized array that maximizes the model resolution matrix for a given electrode geometry. The optimized array produces the highest resolution and target detail. Thus, the tests demonstrate that high quality data and high model resolution can be achieved by acquiring field data from the pole-pole array.
58 GEOSCIENCES↗
The Effects of Herbarium Specimen Characteristics on Short-Read NGS Sequencing Success in Nearly 8000 Specimens: Old, Degraded Samples Have Lower DNA Yields but Consistent Sequencing Success
Phylogenetic datasets are now commonly generated using short-read sequencing technologies unhampered by degraded DNA, such as that often extracted from herbarium specimens. The compatibility of these methods with herbarium specimens has precipitated an increase in broad sampling of herbarium specimens for inclusion in phylogenetic studies. Understanding which sample characteristics are predictive of sequencing success can guide researchers in the selection of tissues and specimens most likely to yield good results. Multiple recent studies have considered the relationship between sample characteristics and DNA yield and sequence capture success. Here we report an analysis of the relationship between sample characteristics and sequencing success for nearly 8,000 herbarium specimens. This study, the largest of its kind, is also the first to include a measure of specimen quality (“greenness”) as a predictor of DNA sequencing success. We found that taxonomic group and source herbarium are strong predictors of both DNA yield and sequencing success and that the most important specimen characteristics for predicting success differ for DNA yield and sequencing: greenness was the strongest predictor of DNA yield, and age was the strongest predictor of proportion-on-target reads recovered. Surprisingly, the relationship between age and proportion-on-target reads is the inverse of expectations; older specimens performed slightly better in our capture-based protocols. We also found that DNA yield itself is not a strong predictor of sequencing success. Most literature on DNA sequencing from herbarium specimens considers specimen selection for optimal DNA extraction success, which we find to be an inappropriate metric for predicting success using next-generation sequencing technologies.
54 ENVIRONMENTAL SCIENCES↗
Biophysical and spectral modeling for crop identification and assessment
The development of a technique for estimating all canopy parameters occurring in a canopy reflectance model from the measured canopy reflectance data is summarized. The Suits and the SAIL model for a uniform and homogeneous crop canopy were used to determine if the leaf area index and the leaf angle distribution could be estimated. Optimal solar/view angles for measuring CR were also investigated. The use of CR in many wavelengths or spectral bands and of linear and nonlinear transforms of CRs for various solar/view angles and various spectral bands is discussed as well as the inversion of rediance data inside the canopy, angle transforms for filtering out terrain slope effects, and modification of one dimensional models.
Goel, N. S.↗
Rapid 3D nanoscale coherent imaging via physics-aware deep learning
Phase retrieval, the problem of recovering lost phase information from measured intensity alone, is an inverse problem that is widely faced in various imaging modalities ranging from astronomy to nanoscale imaging. The current process of phase recovery is iterative in nature. As a result, the image formation is time consuming and computationally expensive, precluding real-time imaging. Here, we use 3D nanoscale X-ray imaging as a representative example to develop a deep learning model to address this phase retrieval problem. We introduce 3D-CDI-NN, a deep convolutional neural network and differential programing framework trained to predict 3D structure and strain, solely from input 3D X-ray coherent scattering data. Our networks are designed to be “physics-aware” in multiple aspects; in that the physics of the X-ray scattering process is explicitly enforced in the training of the network, and the training data are drawn from atomistic simulations that are representative of the physics of the material. We further refine the neural network prediction through a physics-based optimization procedure to enable maximum accuracy at lowest computational cost. 3D-CDI-NN can invert a 3D coherent diffraction pattern to real-space structure and strain hundreds of times faster than traditional iterative phase retrieval methods. Our integrated machine learning and differential programing solution to the phase retrieval problem is broadly applicable across inverse problems in other application areas.
77 NANOSCIENCE AND NANOTECHNOLOGY↗
Multimodal parameter spaces of a complex multi-channel neuron model
One of the most common types of models that helps us to understand neuron behavior is based on the Hodgkin–Huxley ion channel formulation (HH model). A major challenge with inferring parameters in HH models is non-uniqueness: many different sets of ion channel parameter values produce similar outputs for the same input stimulus. Such phenomena result in an objective function that exhibits multiple modes (i.e., multiple local minima). This non-uniqueness of local optimality poses challenges for parameter estimation with many algorithmic optimization techniques. HH models additionally have severe non-linearities resulting in further challenges for inferring parameters in an algorithmic fashion. To address these challenges with a tractable method in high-dimensional parameter spaces, we propose using a particular Markov chain Monte Carlo (MCMC) algorithm, which has the advantage of inferring parameters in a Bayesian framework. The Bayesian approach is designed to be suitable for multimodal solutions to inverse problems. We introduce and demonstrate the method using a three-channel HH model. We then focus on the inference of nine parameters in an eight-channel HH model, which we analyze in detail. We explore how the MCMC algorithm can uncover complex relationships between inferred parameters using five injected current levels. The MCMC method provides as a result a nine-dimensional posterior distribution, which we analyze visually with solution maps or landscapes of the possible parameter sets. The visualized solution maps show new complex structures of the multimodal posteriors, and they allow for selection of locally and globally optimal value sets, and they visually expose parameter sensitivities and regions of higher model robustness. We envision these solution maps as enabling experimentalists to improve the design of future experiments, increase scientific productivity and improve on model structure and ideation when the MCMC algorithm is applied to experimental data.
97 MATHEMATICS AND COMPUTING↗
Pre-Hardware Optimization and Implementation Of Fast Optics Closed Control Loop Algorithms
One of the main heritage tools used in scientific and engineering data spectrum analysis is the Fourier Integral Transform and its high performance digital equivalent - the Fast Fourier Transform (FFT). The FFT is particularly useful in two-dimensional (2-D) image processing (FFT2) within optical systems control. However, timing constraints of a fast optics closed control loop would require a supercomputer to run the software implementation of the FFT2 and its inverse, as well as other image processing representative algorithm, such as numerical image folding and fringe feature extraction. A laboratory supercomputer is not always available even for ground operations and is not feasible for a night project. However, the computationally intensive algorithms still warrant alternative implementation using reconfigurable computing technologies (RC) such as Digital Signal Processors (DSP) and Field Programmable Gate Arrays (FPGA), which provide low cost compact super-computing capabilities. We present a new RC hardware implementation and utilization architecture that significantly reduces the computational complexity of a few basic image-processing algorithm, such as FFT2, image folding and phase diversity for the NASA Solar Viewing Interferometer Prototype (SVIP) using a cluster of DSPs and FPGAs. The DSP cluster utilization architecture also assures avoidance of a single point of failure, while using commercially available hardware. This, combined with the control algorithms pre-hardware optimization, or the first time allows construction of image-based 800 Hertz (Hz) optics closed control loops on-board a spacecraft, based on the SVIP ground instrument. That spacecraft is the proposed Earth Atmosphere Solar Occultation Imager (EASI) to study greenhouse gases CO2, C2H, H2O, O3, O2, N2O from Lagrange-2 point in space. This paper provides an advanced insight into a new type of science capabilities for future space exploration missions based on on-board image processing for control and for robotics missions using vision sensors. It presents a top-level description of technologies required for the design and construction of SVIP and EASI and to advance the spatial-spectral imaging and large-scale space interferometry science and engineering.
Kizhner, Semion↗
Communication: xDH double hybrid functionals can be qualitatively incorrect for non-equilibrium geometries: Dipole moment inversion and barriers to radical-radical association using XYG3 and XYGJ-OS
Here, double hybrid (DH) density functionals are amongst the most accurate density functional approximations developed so far, largely due to the incorporation of correlation effects from unoccupied orbitals via second order perturbation theory (PT2). The xDH family of DH functionals calculate energy directly from orbitals optimized by a lower level approach like B3LYP, without self-consistent optimization. XYG3 and XYGJ-OS are two widely used xDH functionals that are known to be quite accurate at equilibrium geometries. Here, we show that the XYG3 and XYGJ-OS functionals can be ill behaved for stretched bonds well beyond the Coulson-Fischer point, predicting unphysical dipole moments and humps in potential energy curves for some simple systems like the hydrogen fluoride molecule. Numerical experiments and analysis show that these failures are not due to PT2. Instead, a large mismatch at stretched bond-lengths between the reference B3LYP orbitals and the optimized orbitals associated with the non-PT2 part of XYG3 leads to an unphysically large non-Hellman-Feynman contribution to first order properties like forces and electron densities.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
SU-E-T-185: Feasibility Study of Dose Rate Modulated Arc Therapy (DrMAT) for Lung SBRT
Purpose: To show the feasibility of clinical application of DrMAT for SBRT in lung cancer patients. DrMAT is a form of dynamic conformal arc therapy where MLC segments and dose rates are controlled through simple field weight optimization. Methods: To show feasibility a new treatment plan was created based on the CT of SBRT lung cancer patients. Static plans with 33 fields are made, which have 11deg in between each field and are acquired rotating gantry angle from 180deg to 188deg in CCW direction, total 352deg is rotated. MLC maintained static aperture for each field. To optimize 33 individual fields, field weight was adjusted accordingly using weight optimization algorithm. Keeping weights and MU of static plan, static MLC aperture was converted to multiple arc segments. Arc plan could be created with the fields in the intervals of 11deg. Static MLC should be converted to arc segment MLC. Dynamic conformal arc therapy plan consists of 33 arc fields, is converted to one dose rate modulated arc therapy (DrMAT) plan. DrMAT plan consists of 166 control points which becomes a single arc plan that changes the shape of MLC for every 2.2deg. The resulting DrMAT plan is not an inverse plan it is a simple form of dynamic conformal arc plan using field weight obtained from static plan. This is compared and evaluated with the VMAT plan. Results: DrMAT and VMAT plans have been compared based on the RTOG1021. Both DrMAT and VMAT plans satisfy 100% irradiation to 95% of PTV and critical organs did not exceed dose limit suggested in RTOG1021. DrMAT plan is almost similar with VMAT plan in Result. Conclusion: Field weight optimization method did not show better Resultcompared to VMAT optimization. However, considering simplicity, DrMAT satisfies the condition in RTOG1021. Therefore clinical application of DrMAT is feasible.
60 APPLIED LIFE SCIENCES↗
Gradient-based optimization of complex nanoparticle heterostructures enabled by deep learning on heterogeneous graphs
Applications of deep learning (DL) to design nanomaterials are hampered by a lack of suitable data representations and training data. Here, in this study, we report efforts to overcome these limitations and leverage DL to optimize the nonlinear optical properties of core–shell upconverting nanoparticles (UCNPs). UCNPs, which have applications in fields such as biosensing, super-resolution microscopy and three-dimensional printing, can emit visible and ultraviolet light from near-infrared excitations. We report a large-scale dataset of UCNP emission spectra based on accurate but expensive kinetic Monte Carlo simulations (N > 6,000) and use these data to train a heterogeneous graph neural network using a physically motivated representation of UCNP nanostructure. Applying gradient-based optimization on the trained graph neural network, we identify structures with 6.5× higher predicted emission under 800-nm illumination than any UCNP in our training set. Our work reveals design principles for UCNP heterostructures and presents a roadmap for DL-based inverse design of nanomaterials.
Sivonxay, Eric [Lawrence Berkeley National Laborat↗
A Discrete Hankel Transform Approach to Nuclear Data Processing for Fusion Applications
This study introduces advancements to the numerical solutions employed in the processing of nuclear data for fusion applications. It leverages the convolution theorem and Fourier transform techniques to enhance computational efficiency and broaden applicability. Building upon a previously reported discrete Hankel transform approach for Doppler broadening, this work refines the solution of convolution integrals central to these applications. The methodology provides a general and unified framework for evaluating any convolution operation, regardless of whether the underlying problem involves temperature effects in nuclear reactions. The applicability to the nuclear data processing for fusion is demonstrated by deriving the convolution integrals for some of the fusion-related quantities. As before, the convolution operation utilizes a Gaussian-based kernel; however, the discrete Hankel transform of order $𝛼$ = $\frac{1}{2}$ is now applied to the forward Fourier transform of the nonkernel argument, rather than the inverse Fourier transform. This modification eliminates the need for the integration of the nonkernel, cross section–based function, which is a step that posed challenges for certain pointwise cross-section representations. It also removes the requirement for cross-section linearization. Optimized for graphics processing unit architectures, the approach significantly improves computational performance. These advancements are currently under evaluation as the foundation for the next-generation thermonuclear data file processing codes being developed at Lawrence Livermore National Laboratory.
Nuclear science and engineering↗
Physics-Based Inversion of a Millimeter-Wave Diagnostic for Liquid Metal Additive Manufacturing
Not Available
36 MATERIALS SCIENCE↗
Plasma-puff initiation of high Coulomb transfer switches
The plasma-puff triggering mechanism based on a hypocycloidal pinch geometry was investigated to determine the optimal operating conditions for an azimuthally uniform surface flashover which initiates plasma-puff under wide ranges of fill gas pressures of Ar, He and N2. The optimal fill gas pressures for the azimuthally uniform plasma-puff were about 120 mTorr less than P(opt) less than 450 Torr for He and N2. For Argon 120 mTorr less than P(opt) less than 5 Torr for argon. The inverse pinch switch was triggered with the plasma-puff and the switching capability under various electrical parameters and working gas pressures of Ar, He and N2 was determined. It was also shown that the azimuthally uniform switching discharges were dependent on the type of fill gas and its fill pressure. A new concept of plasma-focus driven plasma-puff was also discussed in comparison with hypocycloidal pinch plasma-puff triggering. The main discharge of the inverse pinch switch with the plasma-focus driven plasma-puff trigger is found to be more azimuthally uniform than that with the hypocycloidal pinch plasma-puff trigger in a gas pressure region between 80 mTorr and 1 Torr. In order to assess the effects of plasma current density on material erosion of electrodes, emissions from both an inverse-pinch plasma switch (INPIStron) and from a spark gap switch under test were studied with an optical multichannel analyzer (OMA). The color temperature of the argon plasma was approximately 4,000 K which corresponded with the peak continuum emission near 750 nm. There are the strong line emissions of argon in the 650 - 800 nm range and a lack of line emissions of copper and other solid material used in the switch. This indicates that the plasma current density during closing is low and the hot spot or hot filament in the switch is negligible. This result also indicates considerable reduction of line emission with the INPIStron switch over that of a spark-gap switch. However, a strong carbon line emission exists due to vaporization of the plastic insulator used. In order to reduce the vaporization of the insulator, the plexiglass insulating material of INPIStron was replaced with Z-9 material. A comparative study of the INPIStron and a spark gap also reveals that the INPIStron, with a low impedance of Z equals 9 ohms, can transfer a high voltage pulse with a superior pulse-shape fidelity over that of a spark gap with Z equals 100 ohms.
Venable, D. D.↗
Extending the QUDA Library with the eigCG Solver
While the incremental eigCG algorithm [ 1 ] is included in many LQCD software packages, its realization on GPU micro-architectures was still missing. In this session we report our experi- ence of the eigCG implementation in the QUDA library. In particular, we will focus on how to employ the mixed precision technique to accelerate solutions of large sparse linear systems with multiple right-hand sides on GPUs. Although application of mixed precision techniques is a well-known optimization approach for linear solvers, its utilization for the eigenvector com- puting within eigCG requires special consideration. We will discuss implementation aspects of the mixed precision deflation and illustrate its numerical behavior on the example of the Wilson twisted mass fermion matrix inversions
Strelchenko, Alexei↗