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At least 235 records · Page 13

Optimization of key energy and performance metrics for drug product manufacturing

During the development of pharmaceutical manufacturing processes, detailed systems-based analysis and optimization are required to control and regulate critical quality attributes within specific ranges, to maintain product performance. As discussions on carbon footprint, sustainability, and energy efficiency are gaining prominence, the development and utilization of these concepts in pharmaceutical manufacturing are seldom reported, which limits the potential of pharmaceutical industry in maximizing key energy and performance metrics. Based on an integrated modeling and techno-economic analysis framework previously developed by the authors, this study presents the development of a combined sensitivity analysis and optimization approach to minimize energy consumption while maintaining product quality and meeting operational constraints in a pharmaceutical process. The optimal input process conditions identified were validated against experiments and good agreement resulted between simulated and experimental data. Here, the results also allowed for a comparison of the capital and operational costs for batch and continuous manufacturing schemes under nominal and optimized conditions. Using the nominal batch operations as a basis, the optimized batch operation results in a 71.7% reduction of energy consumption, whereas the optimized continuous case results in an energy saving of 83.3%.

59 BASIC BIOLOGICAL SCIENCES↗

Computing Composition/Depth Profiles From X-Ray Diffraction

Diffraction-intensity bands deconvolved relatively quickly. TIBAC constructs composition/depth profiles from X-ray diffraction-intensity bands. Intensity band extremely sensitive to shape of composition/depth profile. TIBAC incorporates straightforward transformation of intensity band that retains accuracy of earlier simulation models, but is several orders of magnitude faster in total computational time. TIBAC written in FORTRAN 77 for batch execution.

Wiedemann, K. E.↗

Simultaneous quaternion estimation (QUEST) and bias determination

Tests of a new method for the simultaneous estimation of spacecraft attitude and sensor biases, based on a quaternion estimation algorithm minimizing Wahba's loss function are presented. The new method is compared with a conventional batch least-squares differential correction algorithm. The estimates are based on data from strapdown gyros and star trackers, simulated with varying levels of Gaussian noise for both inertially-fixed and Earth-pointing reference attitudes. Both algorithms solve for the spacecraft attitude and the gyro drift rate biases. They converge to the same estimates at the same rate for inertially-fixed attitude, but the new algorithm converges more slowly than the differential correction for Earth-pointing attitude. The slower convergence of the new method for non-zero attitude rates is believed to be due to the use of an inadequate approximation for a partial derivative matrix. The new method requires about twice the computational effort of the differential correction. Improving the approximation for the partial derivative matrix in the new method is expected to improve its convergence at the cost of increased computational effort.

Markley, F. Landis↗

Attitude determination and parameter estimation using vector observations - Application

This paper presents tests of a new method for the simultaneous estimation of spacecraft attitude and sensor biases, based on a quaternion estimation algorithm minimizing Wahba's loss function. The new method is compared with a conventional batch least-squares differential correction algorithm. The estimates are based on data from strapdown gyros and star trackers, simulated with varying levels of Gaussian noise for both inertially-fixed and earth-pointing attitudes. Both algorithms solve for the spacecraft attitude and the gyro drift rate biases. In the majority of tests performed, the two methods converge to the same estimates in the same number of iterations, but the new algorithm requires about 60 percent more computational effort. Some cases were found in which the new method converges in fewer iterations than the differential correction, and some for which the differential correction requires fewer iterations.

Markley, F. Landis↗

NASA Tech Briefs, January 2005

Topics covered include: Fiber-Optic Sensor Would Monitor Growth of Polymer Film; Sensors for Pointing Moving Instruments Toward Each Other; Pd/CeO2/SiC Chemical Sensors; Microparticle Flow Sensor; Scattering-Type Surface-Plasmon-Resonance Biosensors; Diode-Laser-Based Spectrometer for Sensing Gases; Improved Cathode Structure for a Direct Methanol Fuel Cell; X-Band, 17-Watt Solid-State Power Amplifier; Improved Anode for a Direct Methanol Fuel Cell; Tools for Designing and Analyzing Structures; Interactive Display of Scenes with Annotations; Solving Common Mathematical Problems; Tools for Basic Statistical Analysis; Program Calculates Forces in Bolted Structural Joints; Integrated Structural Analysis and Test Program; Molybdate Coatings for Protecting Aluminum Against Corrosion; Synthesizing Diamond from Liquid Feedstock; Modifying Silicates for Better Dispersion in Nanocomposites; Powder-Collection System for Ultrasonic/Sonic Drill/Corer; Semiautomated, Reproducible Batch Processing of Soy; Hydrogen Peroxide Enhances Removal of NOx from Flue Gases; Subsurface Ice Probe; Real-Time Simulation of Aeroheating of the Hyper-X Airplane; Using Laser-Induced Incandescence To Measure Soot in Exhaust; Method of Real-Time Principal-Component Analysis; Insect-Inspired Flight Control for Unmanned Aerial Vehicles; Domain Compilation for Embedded Real-Time Planning; Semantic Metrics for Analysis of Software; Simulation of Laser Cooling and Trapping in Engineering Applications; Large Fluvial Fans and Exploration for Hydrocarbons; Doping-Induced Interband Gain in InAs/AlSb Quantum Wells; Development of Software for a Lidar-Altimeter Processor; Upgrading the Space Shuttle Caution and Warning System; and Fractal Reference Signals in Pulse-Width Modulation.

Source record↗

Universal method for the optimization of HDC coating uniformity on non-planar, non-stationary substrates for inertial confinement fusion targets

The thickness uniformity of chemical vapor deposited (CVD) diamond coatings on non-planar, non-stationary substrates depends on both the intrinsic instantaneous coating thickness distribution (ICTD) of the coating conditions used and, if applicable, on the frequency of substrate reorientation. While important for many CVD diamond applications, the relative impact of the ICTD and substrate reorientation on the coating thickness uniformity has not been studied. In this work, we systematically investigate the effect of these factors for microwave-plasma chemical vapor deposition (MPCVD) of diamond (referred to as high density carbon (HDC) in the inertial confinement fusion (ICF) community) coatings on spherical, rolling substrates. This coating technique is used to fabricate capsules for ICF experiments, which require extreme coating uniformity with <0.3 % thickness variation (so-called Mode 1 or M1) to ensure symmetric compression of imploding targets. To extract the otherwise unobservable reorientation timescale (Δt), Monte Carlo simulations were performed using experimental ICTD data as input. This combined approach confirms scaling relationships between the substrate reorientation timescale as well as coating thickness and coating uniformity, as expected from a 3D random walk. Simulations confirm that M1 is Rayleigh-distributed and scales as (Δt) 1/2 , consistent with the randomization of two angles that determine orientation of a sphere. We also demonstrate that, under the conditions studied, Δt is the dominant factor in determining thickness uniformity while the intrinsic ICTD has minimal impact. Finally, experiments show that Δt can be affected by total batch size under constant agitation conditions due to space constraints that limit the capsule reorientation kinetics. In conclusion, this study highlights the utility of a combined experiment-simulation approach as a general methodology for understanding and improving coating uniformity on non-planar, non-stationary substrates.

Capsule↗

Development of New Lunar Highland Regolith Simulant, NUW-LHT-5M

Introduction: NASA has a need for large quantities of lunar simulants that closely match future manned lunar missions at landing sites near the Lunar South Pole. The current simulant was designed to approximate NU-LHT-2M, and -4M, except using natural minerals and a fully synthetic, non-basaltic, high calcium glass. Washington Mills in Niagara Falls, NY was contracted to produce this new simulant due to their electric arc furnace (EAF) fusion technology and capabilities for crushing and sizing ceramics. The primary objectives of this work were: (1) to create a commercially produced simulant that contains a high fidelity, high calcium (An90+), low Mg/Fe glass without basaltic constituents; (2) to create a simulant that uses existing, terrestrially mined, and readily available minerals blended with a synthetic glass, and (3) accomplish objectives (1) and (2) using conventional processing methods capable of production levels that can meet substantial current and future demands of NASA projects. Another consideration was that highland simulants closest to meeting these requirements, NU-LHT-2M and -4M types, are out of production and largely out of stock. Design: The design chosen for development and production for this work is NU-LHT-2M, de-signed by Doug Stoeser and Douglas Rickman, which uses Stillwater anorthosite and norite rocks and synthetic glass. The composition targeted the average composition, glass content and particle size distribution of Apollo 16 samples [1]. The design utilizes two Stillwater minerals, anorthosite (37.7 wt.%), norite (17.6 wt.%), a commercially sourced olivine (4.7 wt.%), and a synthetic glass (40 wt.%). Processing: Stillwater rocks were hand collected and initially crushed by the USGS. Further crushing and milling was performed at Washing-ton Mills. For the glass, oxides were blended in the ap-propriate ratios, blended in a v-blender, and placed in a graphite lined, water cooled pot. The mixture was fused in Washington Mills’ pilot scale, 500 kW EAF. The molten glass was then poured into water and quenched to inhibit crystallization. Several pour/quench cycles were required to produce the total quantity of glass needed. Anorthosite, norite and olivine were milled together to the target particle size in a ball mill. The glass was ball milled separately. The complete simulant was produced by blending the milled materials. Particle size distribution, phase and chemical analyses of the materials was were per-formed at Washington Mills. Results: The glass composition was found to match the Apollo 16 oxide content average [1] quite well, with the exception of SiO2 and Fe2O3. The deviation of SiO2 and Fe2O3 from batched content was believed to be due to limitations inherent in the use of the arc furnace technique for these types of materials. A particle size distribution of the full simulant formulation closely matching the average for the Apollo 16 samples was also achieved. Fig. 1 shows the morphology of the final simulant particles. The size distribution is compared to the target in Fig. 2. Conclusions: A new lunar highlands type simulant containing a high-quality synthetic glass, closely matching an average composition of the Apollo 16 regolith was produced. Processing utilized easily scalable mineral and glass making methods.

simulant↗

Determining the Solubility Behavior of Kogarkoite in Simulated Nuclear Waste

Kogarkoite (Na 3 FSO 4 ) is a sparingly soluble fluoride–sulfate double salt that has been identified in high level nuclear waste sludge at the Hanford Site and, more recently, in sludge batch compilation samples at the Savannah River Site (SRS). Due to its complex dissolution behavior, which exhibits an inverse dependence on sodium ion activity, the presence of this mineral poses significant challenges to waste retrieval and processing. Incomplete dissolution during sludge washing can lead to the retention of fluoride and sulfate in the high-level waste feed, potentially causing the formation of corrosive, immiscible molten salt layers, known as "glass gall,” in vitrification melters. Current efforts to optimize flowsheet parameters and wash-water volumes are hindered by the absence of a commercially available, certified reference material, which prevents the accurate calibration of analytical methods and the verification of dissolution kinetics. To address this critical gap, this research focuses on the laboratory synthesis of pure Kogarkoite to serve as a standard for comprehensive solubility and washing performance testing. A coupled synthesis and simulant campaign was executed using an evaporative crystallization protocol designed to replicate the dynamic concentration effects observed in tank farm operations. Thirteen simulant matrices were prepared by dissolving systematically varied ratios of sodium fluoride (NaF) and sodium sulfate (Na 2 SO 4 ) in deionized water under three distinct caustic regimes: 0.0 g (control), 4.0 g (~1 M), and 12.0 g (~3 M) sodium hydroxide (NaOH). While thermodynamic equilibrium models suggest that high-caustic environments should favor the stability of the double salt7, results from this evaporative study at 25 0 C revealed a distinct kinetic divergence. Simulants with high hydroxide loading predominantly yielded large, blocky crystals of sodium sulfate decahydrate (Na 2 SO 4 .10H 2 O). Successful synthesis of pure Kogarkoite was achieved exclusively in specific NaOH-free compositional windows, where the precipitate manifested as fine, opaque granular aggregates. Ion chromatography (IC) analysis confirmed phase purity through the simultaneous stoichiometric depletion of both fluoride and sulfate from the supernatant. This successful synthesis establishes a reproducible route to generate bulk Kogarkoite, enabling the subsequent phase of quantitative dissolution testing using inhibited water to optimize sludge-batch assembly.

Sarker, Md Sharif [Florida International Univ. (FI↗

(U) A General-Purpose Code for Correlated Sampling Using Batch Statistics with MCNP6 for Fixed-Source Problems

Correlated sampling can be used to reduce the uncertainty of a difference of tallies by taking advantage of the negative covariance term in the sandwich formula. Booth first showed how correlated sampling can be applied with batch statistics using MCNP’s tally fluctuation chart (TFC) to reduce the uncertainty of a difference of tallies in fixed-source problems. Booth presented a problem in which a 1273% uncertainty in a difference was reduced to 8% by accounting for correlations. Researchers He and Su recently studied correlated sampling using the TFC in MCNP version 5. They determined that the code did not print enough digits in the TFC tally means for accurate batch statistics in some cases. After modifying the source code, they concluded that “correlated sampling can yield a standard deviation of about one magnitude smaller than that predicted by the direct, un-correlated simulation when the changes in system response are small (say about 1%), which is equivalent to saving in CPU time by a factor of 100. Such saving [sic] becomes less significant as the change in system response becomes larger.” He and Su provided the formulas needed to apply batch statistics to compute the correlated uncertainty of a difference of tallies. In this report, we follow up on their work by providing the formulas needed to apply batch statistics to compute the correlated uncertainty of a ratio of tallies and of a difference of two tallies divided by a third tally. We extend these formulas to differences and ratios of ratios. These formulas are applied to reduce the uncertainty associated with calculating a relative sensitivity. He and Su did not investigate the accuracy of their correlated sampling uncertainty estimates. We use their test problems and evaluate the accuracy of the uncertainty estimates by comparing with results obtained from random sampling, and, in simple cases, with theoretical values of the “exact” uncertainties. We find that the uncertainties obtained from batch statistics are accurate as long as at least 100 batches are used. We present a new computer code, COSUBS (COrrelated Sampling Using Batch Statistics), that reads MCNP6 TFCs and applies correlated sampling using batch statistics for the tally combinations that the user specifies. COSUBS is a very general tool that compares all TFCs for a base case and one or two perturbed cases. It computes uncertainties for ratios if given only a base case. This report is organized as follows. The equations to apply batch statistics to the difference of random tallies are reviewed in Sec. II. Section III presents the equations for applying batch statistics to a ratio of random tallies; this is useful for computing relative sensitivities using a one-sided finite difference and the relative sensitivity using the differential operator method. Section IV presents the equations for applying batch statistics to a difference of two random tallies divided by a third; this is useful for computing a relative sensitivities using a central difference. Section V presents the equations for applying batch statistics to a difference of two ratios with four random tallies. Section VI presents the equations for applying batch statistics to a one-sided finite difference estimate of the relative sensitivity of a ratio (this uses four random tallies). Section VII presents the equations for applying batch statistics to a central difference estimate of the relative sensitivity of a ratio (this uses six random tallies). Section VIII presents the equations for applying batch statistics to a sum of random tallies. Section IX discusses how to apply batch statistics using MCNP6. Section X presents COSUBS, describing its command-line options and logic. Sections XI through XVI present numerical results for various test problems. Section XVII is a summary and conclusions. Appendix A derives the theoretical Monte Carlo tally variance given certain assumptions; these variances are used to verify the batch statistics for some of the problems. Appendix B lists the MCNP6 input for the unperturbed example problem. Appendix C presents modifications made to MCNP6.3 to support this work.

97 MATHEMATICS AND COMPUTING↗

Metal leaching from Lithium-ion and Nickel-metal hydride batteries and photovoltaic modules in simulated landfill leachates and municipal solid waste materials

Photovoltaic (PV) modules and batteries can either be recycled or disposed of in landfills at end-of-life (EoL). This work focuses on disposal since the benefit of recycling PV modules and batteries is well established. This study characterizes the potential toxicity due to metals leaching from selected PV modules and batteries through both the Toxicity Characteristic Leaching Procedure (TCLP) and in-house batch leaching protocols to probe the impact on metallic ion mobility as a function of the different types of e-waste entering the waste stream, the magnitude of device damage when placed into the waste stream, and the simulated municipal solid waste (SMSW) composition. Our results showed that one PV module and three battery types in this study should be classified as hazardous waste within the U.S. However, for some of the other e-wastes, metals of concern including Cr, Cu, Hg, Ni, Pb, and Zn leached during the batch tests but not in the TCLP regulatory method. For most of the waste types, the amounts of metals that leached in the TCLP test and batch tests were much lower than the total extractable amounts, demonstrating the potential for additional unaccounted for amounts of metals to leach. Our results demonstrate that the TCLP regulatory method might fail at predicting potential leaching and at capturing the complexity of e-waste leaching in landfill conditions. It confirms that additional work is needed urgently to develop appropriate EoL procedures for MSW with PV and battery e-waste.

42 ENGINEERING↗

Convergence of Hyperbolic Neural Networks Under Riemannian Stochastic Gradient Descent

Abstract We prove, under mild conditions, the convergence of a Riemannian gradient descent method for a hyperbolic neural network regression model, both in batch gradient descent and stochastic gradient descent. We also discuss a Riemannian version of the Adam algorithm. We show numerical simulations of these algorithms on various benchmarks.

Whiting, Wes (ORCID:0000000247505060)↗

Structural origin of disorder-induced ion conduction in NaFePO 4 cathode materials

Diffusion in NaFePO 4 can be enhanced through amorphization. Based on computations using DFT and machine learning potentials, we ascribe this phenomenon to the formation of less constrained Na-ion environments upon disordering. Most modern battery technologies depend on solid-state crystalline cathode materials. However, some of these materials are constrained by the low ionic conductivity of their most stable phases. An example of this is maricite (NaFePO 4 ). Interestingly, experiments have shown that maricite can improve its rate capability through disordering (amorphization). However, experimental characterization of amorphous cathode materials remains a major challenge, hindering a clear understanding of the structural origin of the disorder-induced improvement in sodium-ion mobility. To address this, we here employ molecular dynamics simulations by first training a machine learning potential for NaFePO 4 based on the atomic cluster expansion approach and a batch active learning potential parameterization scheme. This potential is then applied to explore the structural and dynamical properties of NaFePO 4 glasses as cathode materials. Specifically, we investigate the effect of glass structure on sodium-ion diffusion, revealing the relative influences of short-range and medium-range order features. We find significant heterogeneity in sodium-ion diffusivity in the glass, with fast-conducting ions residing in less constrained atomic environments with fewer P and Fe neighbors. These more mobile ions are also surrounded by larger ring-type structures. Overall, the results and developed approach present promising avenues for developing high-performance glassy cathodes for next-generation batteries.

Christensen, Rasmus↗

Modeling Molten Salt Reactor Fission Product Removal with SCALE

Liquid-fuel Molten Salt Reactors (MSRs) allow for continuous online fuel treatment and processing using a variety of subsystems. Among these subsystems, the off-gas system (OGS) continuously removes fission products from the primary fuel salt through a helium sparging process. Accurately modeling fission product behavior is paramount for the development of MSRs; while there are a variety of depletion modeling and simulation tools that allow for removal of material from a defined system, most of them work in a semicontinuous, batch-wise manner. The new tools implemented into TRITON, in the SCALE suite for reactor analysis and design, aim to address this issue by implementing truly continuous material feeds and removals. In addition, material accountancy is enhanced in these tools, which enable tracking of removed material in user-specified mixtures. These continuous depletion tools provide a way to model material transport throughout systems more efficiently and accurately. This report validates the new SCALE MSR modeling capabilities using data from the Molten Salt Reactor Experiment (MSRE). After a framework was established for deriving realistic removal rates based on design specifications, these rates were used by TRITON to continuously remove specific elements while performing burnup calculations. Results were compared to those obtained in MSRE, with a focus on the 135 Xe poison fraction. After these capabilities were validated, the learned concepts and best practices were applied to model the Molten Salt Breeder Reactor (MSBR) and the Molten Salt Demonstration Reactor (MSDR) to study the effects of continuous fission product removal in fuel salt reactivity, as well as the lifetime effects.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Sorbent Based Post-Combustion CO 2 Slipstream Testing

TDA Research, Inc. has developed a low-cost sorbent (alkalized alumina) based technology for post-combustion CO 2 capture. The sorbent runs in an isothermal process for adsorption and regeneration. Neither temperature swing nor pressure swing is needed. TDA designed a system which operated 10 fixed beds to simulate a moving bed process. The flow pattern was optimized to minimize the steam usage for regeneration. An excellent sorbent (Batch 1) was also developed with collaboration with our partner. The sorbent degraded after long term operation. We developed a reprocessing procedure, which can be conducted in situ, to restore the sorbent capture capacity as the fresh sorbent after running for about 1250 hours. The reprocessing extended the sorbent longevity significantly. TDA designed a pilot unit (40’ x 32’ x 11.5’), processing flue gas equivalent to 0.5 MW of power generation. It had 10 beds and each bed could hold 1.75 m 3 of sorbent. We worked with Springs Fabrication, Inc. to complete the construction of the pilot unit. It was then shipped and installed at NCCC. The pilot unit was kept running in 24-hour mode when the flue gas and steam were available. It could run automatically without operator on site. Parametric and long-term (2 months) tests were carried out successfully. The results showed that TDA’s process can achieve 90% capture and 95% CO 2 purity for both coal and NG flue gases. For coal flue gas, the system reached performance target when processing up to 0.62 MW flue gas, 24% higher than the design capacity. Thus, capital cost could be saved on the reactors and sorbent. The strip air flow was designed to be 0.25 of that of the flue gas. The test data showed the strip/flue ratio can be reduced to as little as 0.18, which saves the power consumption for the strip air blower. The pressure drop was found to be lower than what the empirical equation calculated. The sorbent still had 91.7% of original capacity after 3-month test. With the data from the pilot test, we updated the TEA. For a 550-MW e supercritical coal fired power plant with CO 2 capture, the capture cost for TDA’s process is $34.9/tonne CO 2 captured, which meets DOE’s goal of $40/tonne and is 17.1% less than the DOE baseline Case 12. Therefore, TDA’s process has a good potential for commercialization.

01 COAL, LIGNITE, AND PEAT↗

Real-time attitude determination and gyro calibration

We present results for two real-time filters prototyped for the Compton Gamma Ray Observatory (GRO), the Extreme Ultraviolet Explorer (EUVE), the Cosmic Background Explorer (COBE), and the next generation of Geostationary Operational Environmental Satellites (GOES). Both real and simulated data were used to solve for attitude and gyro biases. These filters promise advantages over single-frame and batch methods for missions like GOES, where startup and transfer-orbit operations require quick knowledge of attitude and gyro biases.

Challa, M.↗

Visualization of Computational Fluid Dynamics

Scientific visualization serves the dual purpose of exploration and exposition of the results of numerical simulations of fluid flow. Along with the basic visualization process which transforms source data into images, there are four additional components to a complete visualization system: Source Data Processing, User Interface and Control, Presentation, and Information Management. The requirements imposed by the desired mode of operation (i.e. real-time, interactive, or batch) and the source data have their effect on each of these visualization system components. The special requirements imposed by the wide variety and size of the source data provided by the numerical simulation of fluid flow presents an enormous challenge to the visualization system designer. We describe the visualization system components including specific visualization techniques and how the mode of operation and source data requirements effect the construction of computational fluid dynamics visualization systems.

Gerald-Yamasaki, Michael↗

Data for An End-to-End Pipeline for Succinic Acid Production at an Industrially Relevant Scale Using Issatchenkia orientalis

Microbial production of succinic acid (SA) at an industrially relevant scale has been hindered by high downstream processing costs arising from neutral pH fermentation for over three decades. Here, we metabolically engineer the acid-tolerant yeast Issatchenkia orientalis for SA production, attaining the highest titers in sugar-based media at low pH (pH 3) in fed-batch fermentations, i.e. 109.5 g/L in minimal medium and 104.6 g/L in sugarcane juice medium. We further perform batch fermentation using sugarcane juice medium in a pilot-scale fermenter (300×) and achieve 63.1 g/L of SA, which can be directly crystallized with a yield of 64.0%. Finally, we simulate an end-to-end low-pH SA production pipeline, and techno-economic analysis and life cycle assessment indicate our process is financially viable and can reduce greenhouse gas emissions by 34–90% relative to fossil-based production processes. We expect I. orientalis can serve as a general industrial platform for production of organic acids.

Metabolomics↗

Component-Level Inverse Design of Transmon Qubits Using Neural Networks

Designing a superconducting qubit to realize specific Hamiltonian parameters typically requires iterating through a time and compute-intensive forward loop in which the designer chooses a layout geometry, simulates it, extracts circuit parameters such as capacitances, and refines the geometry. We study the inverse version of this task using a neural-network workflow that maps target Hamiltonian parameters directly to component-level layout parameters, which we subsequently demonstrate on a planar transmon layout. During training, we pair the inverse model with a frozen forward surrogate model and evaluate the loss in Hamiltonian space rather than in layout-parameter space. In validation against a conventional EM solver, 97% of generated designs produce usable geometries, and the inverse-plus-surrogate pipeline reaches mean percent errors of 0.73% for qubit frequency and 1.58% for anharmonicity, comparable to or below the fabrication and simulation-to-measurement uncertainty expected for academic-process transmon devices of this type. A single pipeline query takes ~60 ms on CPU, versus ~2 min for a conventional EM capacitance extraction on the same hardware, a speedup of approximately 2,000x. Batching minimizes the AI model inference overhead, reducing the runtime to 3.1 microseconds per sample on CPU and 2.6 microseconds per sample on GPU at a batch size of 2048, resulting in speedups of 3.9 x 10^7 and 4.6 x 10^7, respectively, relative to a single conventional CPU EM extraction. Our results indicate that component-level inverse design usefully extends and complements conventional EM simulation, including for small datasets on the order of 1,000 samples.

Seidel, Olivia [Fermilab; Texas U., Arlington]↗