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Results for “Compact model”
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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Hammerstein-Wiener data-driven compact circuit modeling. Part2: extension to multiple loads and frequency domain training
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Data-driven compact photocurrent models: A system identification approach for non-intrusive reduced order modeling of dose-rate induced photocurrents.
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Hammerstein-Wiener data-driven compact circuit modeling. Part 1: Model formulation and time domain training
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Hammerstein-Wiener Data-Driven Compact Circuit Models
this is poster for the CoDA conference in Santa Fe starting February 25th
Data-driven compact photocurrent models
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Development and Demonstration of Data-driven Compact Device Models for Circuit Simulation and Analysis.
Abstract not provided.
Learning compact physics‐aware delayed photocurrent models using dynamic mode decomposition
Abstract Radiation‐induced photocurrent in semiconductor devices can be simulated using complex physics‐based models, which are accurate, but computationally expensive. This presents a challenge for implementing device characteristics in high‐level circuit simulations where it is computationally infeasible to evaluate detailed models for multiple individual circuit elements. In this work we demonstrate a procedure for learning compact delayed photocurrent models that are efficient enough to implement in large‐scale circuit simulations, but remain faithful to the underlying physics. Our approach utilizes dynamic mode decomposition (DMD), a system identification technique for learning reduced‐order discrete‐time dynamical systems from time series data based on singular value decomposition. To obtain physics‐aware device models, we simulate the excess carrier density induced by radiation pulses by solving numerically the ambipolar diffusion equation, then use the simulated internal state as training data for the DMD algorithm. Our results show that the significantly reduced‐order delayed photocurrent models obtained via this method accurately approximate the dynamics of the internal excess carrier density—which can be used to calculate the induced current at the device boundaries—while remaining compact enough to incorporate into larger circuit simulations.
PAO 1.0: A Python Library for Adversarial Optimization
PAO is a Python-based package for Adversarial Optimization. The goal of this package is to provide a general modeling and analysis capability for bilevel, trilevel and other multilevel optimization forms that express adversarial dynamics. PAO integrates two different modeling abstractions: 1. Algebraic models extend the modeling concepts in the Pyomo algebraic modeling language to express problems with an intuitive algebraic syntax. Thus, we expect that this modeling abstraction will commonly be used by PAO end-users. 2. Compact models express objective and constraints in a manner that is typically used to express the mathematical form of these problems (e.g. using vector and matrix data types). PAO denes custom Multilevel Problem Representations (MPRs) that simplify the implementation of solvers for bilevel, trilevel and other multilevel optimization problems.
Designing a repository in domal salt: The influence of design variants in different modelling environments
To understand the long-term environmental impact of disposing radioactive waste of in a deep geological repository and to optimise its design, performance assessments are used. In this study, four teams (COVRA, GRS, Quintessa, and DOE) modified the previously developed generic repository of DECOVALEX task F2 to identify commonalities and differences between the teams for specific changes in repository design. The teams tested six design modifications: (1) Replacing concrete abutments with run-of-mine salt; (2) Replacing the salt seal with a concrete abutment and using run-of-mine salt instead for the two concrete abutments in each drift seal; (3) Halving the size of the infrastructure area; (4) Using run-of-mine salt instead of gravel for backfilling the infrastructure area; (5) Disposal of spent nuclear fuel without the POLLUX-10 containers (6); Lower initial saturation of the spent nuclear fuel and vitrified waste disposal drifts. Despite different modelling strategies used, models agreed that a smaller infrastructure area has a limited effect on radionuclide transport. Responses to the absence of the two concrete abutments in each seal, the use of single large concrete abutments (200 m each), or the use of run-of-mine salt in the infrastructure area differ between teams due to differing modelling assumptions. Based on these results, the estimated efficacy of containment depends strongly on the model assumptions of each team. More specifically, it appears to depend on the compaction model used and therefore on the backfill material used in different areas of the repository. However, the drift seal appears to be a critical design element in all models, effectively limiting radionuclide transport by hydrologically disconnecting sections of the repository. Additional beneficial design choices include the use of dry salt in disposal drifts to limit radionuclide transport and reducing the infrastructure area costs and minimizing host rock damage.
Confinement and String Breaking in the Compact Abelian Higgs Model
While real-time simulation of Quantum Chromodynamics remains technologically out of reach, simplified models for studying elements of QCD phenomenology abound. This work presents a simple model, a spin-1 truncation of the Compact Abelian Higgs Model simulated on qutrit sites, in which confinement and string breaking is accessible to current simulation methods. In the low-energy regime of 1+1D scalar electrodynamics, the heavy modes are integrated out, producing a spin chain effective Hamiltonian in which Gauss' law is implicitly satisfied. We study the spectrum of string-like excitations using DMRG methods on the order of 100 sites. We demonstrate that an added, local chemical potential, playing a role analogous to external charges, permits parameter-dependent measurements of physical features of interest like the string tension and effective meson mass. Varying the chemical potential also permits a characterization of string stability not assessed in prior studies of confining lattice models.
“Understanding Robustness Lottery”: A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches
Deep learning approaches have provided state-of-the-art performance in many applications by relying on large and overparameterized neural networks. However, such networks are very brittle and are difficult to deploy on resource-limited platforms. Model pruning, i.e., reducing the size of the network, is a widely adopted strategy that can lead to a more robust and compact model. Many heuristics exist for model pruning, but our understanding of the pruning process remains limited due to the black-box nature of a neural network model. Empirical studies show that some heuristics improve performance whereas others can make models more brittle. Here, this work aims to shed light on how different pruning methods alter the network’s internal feature representation and the corresponding impact on model performance. To facilitate a comprehensive comparison and characterization of the high-dimensional model feature space, we introduce a visual geometric analysis of feature representations. We evaluated a set of critical geometric concepts decomposed from the commonly adopted classification loss and used them to design a visualization system to compare and highlight the impact of pruning on model performance and feature representation. The proposed tool provides an environment for an in-depth comparison of pruning methods and a comprehensive understanding of how the model responds to common data corruption. By leveraging the proposed visualization, machine learning researchers can reveal the similarities between pruning methods and redundancy in robustness evaluation benchmarks, obtain geometric insights about the differences between pruned models that achieve superior robustness performance, and identify samples that are robust or fragile to model pruning and common data corruption.
Comparison of point cloud and image-based models for calorimeter fast simulation
Score based generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets. Recent advances in generative models have used images with 3D voxels to represent and model complex calorimeter showers. Point clouds, however, are likely a more natural representation of calorimeter showers, particularly in calorimeters with high granularity. Furthermore, point clouds preserve all of the information of the original simulation, more naturally deal with sparse datasets, and can be implemented with more compact models and data files. In this work, two state-of-the-art score based models are trained on the same set of calorimeter simulation and directly compared.
Sound velocity and compressibility of melts along the hedenbergite (CaFeSi2O6)-diopside (CaMgSi2O6) join at high pressure : Implications for stability and seismic signature of Fe-rich melts in the mantle
Iron-rich silicate melts play an important role in the magmatic history of the Earth and the 16 Moon. However, their elastic properties at high pressures, especially the sound velocity, are poorly 17 understood. Here we determined the ultrasonic sound velocity for the first time of a hedenbergite 18 (Hd, CaFeSi2O6) melt and a melt mixture of 50 mol% hedenbergite + 50 mol% diopside (Hd50Di50) 19 at high pressure and temperature conditions up to 6 GPa and 2329 K, using high-pressure ultrasonic 20 technique combined with synchrotron radiation in a multi-anvil apparatus. Our results show that 21 Fe can significantly reduce the sound velocity while increase the density of silicate melts. 22 Comparing the Di, Hd, and Hd50Di50 melts, we find that the sound velocity does not mix linearly 23 2 for melts in the Hd-Di join, whereas the density for Hd-Di melts at high pressures can be well-24 described by linear mixing. Combined with melt geometry and melt compaction models, we 25 applied our results to study the stability and seismic signature of Fe-rich silicate melts in the Earth’s 26 upper mantle. For the low-velocity zone (LVZ) in mantle asthenosphere, although the degree of 27 seismic velocity reduction can be explained by the presence of a small amount of partial melt 28 distributed in film/band geometry along grain boundaries, Fe-rich melts formed at this depth are 29 unlikely to be gravitationally stable, but may be dynamically unextractable if the melt supply is 30 continuous. For the low-velocity layer (LVL) above the mantle transition zone, the presence of Fe-31 rich melts (with FeO>~10 wt%) distributed in textural equilibrium with the ambient mantle is a 32 plausible explanation.
Enhanced backscatter and unsaturated blue wavelength shifts in F-doped fused silica optical fibers exposed to extreme neutron radiation damage
Amorphous fused silica (a-SiO 2 ) optical fibers, with and without inscribed Bragg gratings, were interrogated using infrared (~1550 nm) optical backscatter reflectometry during ~25 days of intense neutron irradiation to a fast (energy > 0.1 MeV) neutron fluence of ~10 21 n/cm 2 , or ~1.5 atomic displacements per atom. The reflected light amplitudes in Ge-doped core telecommunications fiber dropped below detection limits (>15 dB attenuation) within 3 days of irradiation (~10 20 fast n/cm 2 ). Here, amplitudes from a pure silica core, F-doped silica cladding fiber reached equilibrium levels ~1.5–2 dB higher than pre-irradiation values, whereas Bragg gratings inscribed in the same fiber using a femtosecond laser (point-by-point method) suffered > 45 dB attenuation. Blue wavelength shifts were initially consistent with previous radiation-induced compaction models but increased linearly with increasing neutron fluence (no evidence of saturation) beyond 10 20 n/cm 2 and exceeded 0.6%, which corresponds to >1,000 °C drift if used to measure temperature changes.
Analog-to-digital converter based on voltage-controlled superconducting devices
The increasing demand for cryogenic electronics in superconducting and quantum computing systems calls for ultra-energy-efficient data conversion architectures that remain functional at deep cryogenic temperatures. Here, in this work, we present the first design of a voltage-controlled superconducting flash analog-to-digital converter (ADC) based on a voltage-controlled quantum-enhanced Josephson junction field-effect transistor (JJFET). Exploiting its strong gate tunability and transistor-like behavior, the JJFET offers a scalable alternative to conventional current-controlled superconducting devices while aligning naturally with CMOS-style design methodologies. Building on our previously developed Verilog-A compact model calibrated to experimental data, we design and simulate a three-bit JJFET-based flash ADC targeted for integration within cryogenic control and readout circuitry in quantum computing. The core comparator block is realized through careful bias current selection and augmented with a three-terminal nanocryotron to precisely define reference voltages. Cascaded JJFET comparators ensure robust voltage gain, cascadability, and logic-level restoration across stages. Simulation results demonstrate accurate quantization behavior with ultra-low power dissipation, underscoring the feasibility of voltage-driven superconducting mixed-signal circuits. This work establishes a critical step toward unifying superconducting logic and data conversion, paving the way for scalable cryogenic architectures in quantum–classical co-processors, low-power artificial intelligence accelerators, and next-generation energy-constrained computing platforms.
Optimal strategies for optical quantum memories using long-lived noble-gas spins
Nuclear spins of noble gases exhibit exceptionally long coherence times and can potentially serve as a long-lived storage medium for quantum information. We analyze and compare the performance of two mechanisms for mapping the quantum state of light onto the collective spin state of noble gases. The first mechanism utilizes collisional exchange with the electronic spin state of metastable noble-gas atoms, while the second relies on spin-exchange collisions with ground-state alkali-metal atoms. We describe the operation of an optical quantum memory relying on these two mechanisms using a compact model and study strategies that optimize the memory storage efficiency. Through numerical simulations, we identify optimal sequences for storing optical signals with different signal bandwidths and electronic spin relaxation rates. This work highlights the qualitative difference between the two approaches for using noble gases as long-lived quantum memories at noncryogenic conditions and outlines the regimes in which they are expected to be efficient.
Neuromorphic Accelerator for Deep Spiking Neural Networks with NVM Crossbar Arrays
In this paper, we present a scalable digital hardware accelerator based on non-volatile memory arrays capable of realizing deep convolutional spiking neural networks (SNNs). Our design studies are conducted using a compact model for spin-transfer torque random access memory (STT-RAM) devices. Large networks are realized by tiling multiple cores which communicate by transmitting spike packets via an on-chip routing network. Compared to an equivalent SRAM based core design, we show that the STT-RAM based design achieves nearly 15X higher GSOPS (Synaptic Operations per Second) per Watt per mm 2 making it a promising platform for realizing systems with significant area and power limitations.