Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “ensemble 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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Dataset from Manuscript: CO2 Electroreduction on Borated Copper Surfaces: Boron Active Sites, not Copper

(1) hcoverage-gcga: .db files with structures and data from GCGA sampling at various potentials (2) hcoverage-gcdft/vaspSC-finished.db: .db file with GCDFT calculations and structures on the sub-ensemble of various H coverages on CuB(3) int_ts: Optimized structures of intermediates and transition states for HER (H26.db and H39.db for LC and HC respectively), B migration (H33.db and H34.db), and CORR (H26.db and H39.db for LC and HC respectively)

Boron↗
NASA NTRS1991

Timescale algorithms combining cesium clocks and hydrogen masers

The United States Naval Observatory (USNO) atomic timescale, formerly based on an ensemble of cesium clocks, is now produced by an ensemble of cesium clocks and hydrogen masers. In order to optimize stability and reliability, equal clock weighting has been replaced by a procedure reflecting the relative, time-varying noise characteristics of the two different types of clocks. Correlation of frequency drift is required, and residual drift is avoided by the eventual complete deweighting of the masers.

Lee A. Breakiron↗
DOE OSTI2020

Optimized recursion relation for the computation of partition functions in the superconfiguration approach

Partition functions of a canonical ensemble of non-interacting bound electrons are a key ingredient of the super-transition-array approach to the computation of radiative opacity. A few years ago, we published a robust and stable recursion relation for the calculation of such partition functions. In this work, we propose an optimization of the latter method and explain how to implement it in practice. The formalism relies on the evaluation of elementary symmetric polynomials, which opens the way to further improvements.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Differentiable Multiphysics Codes: A Breakthrough Technology for Simulation and Computing

This document summarizes the findings of a strategic planning exercise commissioned by the Weapons Simulation and Computing, Computational Physics (WSC/CP) program at the Lawrence Livermore National Laboratory (LLNL) in FY24. During the year, the committee met with multiple stakeholder communities to gather input, opinions, suggestions and concerns which have been incorporated throughout this document. The key findings from this exercise are summarized: • The development of multiphysics modelling and simulation (mod/sim) codes and software technologies, their deployment on exascale compute platforms, and their broad adoption across the NNSA is a major success of the Advanced Simulation and Computing (ASC) program and the Exascale Computing Project (ECP). Sustained investment in these core technologies is essential. • Today’s state of the art involves running ensembles of O(100K) simulations to perform uncertainty quantification (UQ) and design studies using multiple statistical methods such as Bayesian optimization to understand sensitivities of our models and explore parameterized design spaces. Even with exascale computing, we are practically limited to O(10) parameters in these studies since the number of simulations required to sample the space scales exponentially with the number of design parameters. • The data from these simulation ensembles is increasingly being used to train machine learned (ML) surrogates (or reduced order models, ROMs) which can then be used for optimization or real time design exploration. However, the trained surrogates are still limited in the number of parameters they can represent due to the sampling limitations previously noted. • Augmenting our suite of integrated multiphysics simulation codes, both current and emerging, with the ability to compute gradients (solution derivatives) of arbitrary simulation outputs with respect to (some or all) simulation inputs would be a breakthrough technology, opening the door to a new era of efficient and automated inverse design based on verified and validated mod/sim capabilities. • This capability, which we refer to as differentiable multiphysics codes (DMCs), would revolutionize both UQ and optimization studies by breaking the curse of dimensionality that presently limits our “gradient-free” ensemble based computing approach. A similar breakthrough occurred in the AI/ML community once the ability to compute gradients of arbitrary loss functions using back-propagation became commonplace. Gradient information from the multiphysics codes can also be used to dramatically improve the efficiency and scale of training of ML/ROM surrogates for rapid assessments. • Achieving this in our suite of codes will be a grand challenge, similar to the amount of effort that was required to transition from CPU to GPU computing. It will require buy-in from the entire WSC/CP program and beyond, including all integrated codes, physics and engineering models, third-party library dependencies and performance portability abstractions. It will also require investment in research and development of numerical methods for computing adjoints of coupled physics across multiple adaptively refined moving meshes and of stochastic (Monte Carlo) and mesh free (SPH) methods. • New software and numerical techniques, largely pioneered by the AI/ML community, make this feasible. Chief among these is automatic differentiation (AD), the ability to employ AD at point-wise locations in a physics calculation (instead of traditional black-box approaches) and the ability to perform “back-propagation in time” (or reverse mode AD) for non-linear partial differential equations (PDEs). Fundamentally, the conclusion of this strategic planning exercise is that the time is right to undertake a large scale effort in WSC, centered on the existing integrated codes, to continue the natural evolution of mod/sim in the age of AI/ML. Instead of attempting to replace mod/sim with purely data driven AI/ML models, we believe the key to success is to integrate AI/ML by building on top of the decades of hard-won knowledge and the verified/validated multiphysics modelling capability that is the hallmark of the ASC program.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles

Classical problems in computational physics such as data-driven forecasting and signal reconstruction from sparse sensors have recently seen an explosion in deep neural network (DNN) based algorithmic approaches. However, most DNN models do not provide uncertainty estimates, which are crucial for establishing the trustworthiness of these techniques in downstream decision making tasks and scenarios. In recent years, ensemble-based methods have achieved significant success for the uncertainty quantification in DNNs on a number of benchmark problems. However, their performance on real-world applications remains under-explored. In this work, we present an automated approach to DNN discovery and demonstrate how this may also be utilized for ensemble-based uncertainty quantification. Specifically, we propose the use of a scalable neural and hyperparameter architecture search for discovering an ensemble of DNN models for complex dynamical systems. We highlight how the proposed method not only discovers high-performing neural network ensembles for our tasks, but also quantifies uncertainty seamlessly. This is achieved by using genetic algorithms and Bayesian optimization for sampling the search space of neural network architectures and hyperparameters. Subsequently, a model selection approach is used to identify candidate models for an ensemble set construction. Afterwards, a variance decomposition approach is used to estimate the uncertainty of the predictions from the ensemble. We demonstrate the feasibility of this framework for two tasks — forecasting from historical data and flow reconstruction from sparse sensors for the sea-surface temperature. In conclusion, we demonstrate superior performance from the ensemble in contrast with individual high-performing models and other benchmarks.

Deep ensembles↗
DOE OSTI2020

A Pattern-Recognition-Based Ensemble Data Imputation Framework for Sensors from Building Energy Systems

Building operation data are important for monitoring, analysis, modeling, and control of building energy systems. However, missing data is one of the major data quality issues, making data imputation techniques become increasingly important. There are two key research gaps for missing sensor data imputation in buildings: the lack of customized and automated imputation methodology, and the difficulty of the validation of data imputation methods. In this paper, a framework is developed to address these two gaps. First, a validation data generation module is developed based on pattern recognition to create a validation dataset to quantify the performance of data imputation methods. Second, a pool of data imputation methods is tested under the validation dataset to find an optimal single imputation method for each sensor, which is termed as an ensemble method. The method can reflect the specific mechanism and randomness of missing data from each sensor. The effectiveness of the framework is demonstrated by 18 sensors from a real campus building. The overall accuracy of data imputation for those sensors improves by 18.2% on average compared with the best single data imputation method.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Assessment of enzyme active site positioning and tests of catalytic mechanisms through X-ray–derived conformational ensembles

How enzymes achieve their enormous rate enhancements remains a central question in biology, and our understanding to date has impacted drug development, influenced enzyme design, and deepened our appreciation of evolutionary processes. While enzymes position catalytic and reactant groups in active sites, physics requires that atoms undergo constant motion. Numerous proposals have invoked positioning or motions as central for enzyme function, but a scarcity of experimental data has limited our understanding of positioning and motion, their relative importance, and their changes through the enzyme’s reaction cycle. To examine positioning and motions and test catalytic proposals, we collected “room temperature” X-ray crystallography data for Pseudomonas putida ketosteroid isomerase (KSI), and we obtained conformational ensembles for this and a homologous KSI from multiple PDB crystal structures. Here, ensemble analyses indicated limited change through KSI’s reaction cycle. Active site positioning was on the 1- to 1.5-Å scale, and was not exceptional compared to noncatalytic groups. The KSI ensembles provided evidence against catalytic proposals invoking oxyanion hole geometric discrimination between the ground state and transition state or highly precise general base positioning. Instead, increasing or decreasing positioning of KSI’s general base reduced catalysis, suggesting optimized Ångstrom-scale conformational heterogeneity that allows KSI to efficiently catalyze multiple reaction steps. Ensemble analyses of surrounding groups for WT and mutant KSIs provided insights into the forces and interactions that allow and limit active-site motions. Most generally, this ensemble perspective extends traditional structure–function relationships, providing the basis for a new era of “ensemble–function” interrogation of enzymes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Control Design for Inhomogeneous-Broadening Compensation in Single-Photon Transducers

We report a transducer of single photons between microwave and optical frequencies can be used to realize quantum communication over optical fiber links between distant superconducting quantum computers. A promising scalable approach to constructing such a transducer is to use ensembles of quantum emitters interacting simultaneously with electromagnetic fields at optical and microwave frequencies. However, inhomogeneous broadening in the transition frequencies of the emitters can be detrimental to this collective action. In this paper, we utilize a gradient-based optimization strategy to design the temporal shape of the laser field driving the transduction system to mitigate the effects of inhomogeneous broadening. We study the improvement of transduction efficiencies as a function of inhomogeneous broadening in different single-emitter cooperativity regimes and correlate it with a restoration of super-radiance effects in the emitter ensembles. Furthermore, to assess the optimality of our pulse designs, we provide certifiable bounds on the design problem and compare them to the achieved performance.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Evaluation and Optimization of Snow Albedo Scheme in Noah-MP Land Surface Model Using In Situ Spectral Observations in the Colorado Rockies

The Biosphere-Atmosphere Transfer Scheme (BATS) ground snow albedo algorithm is commonly used in land-surface models (LSM), weather forecasting and research applications. This study addresses key uncertainties in BATS simulated ground snow albedo within the Noah-MP LSM framework through evaluation and optimization of the Noah-MP BATS ground snow albedo formulation using 2-band (visible and near-infrared (NIR)) in situ albedo observations at Rocky Mountain field stations. The Noah-MP BATS ground snow albedo scheme is extremely sensitive to its input parameters. Namely, an ensemble generated by varying BATS input parameters within potentially plausible ranges provides an average daily range (maximum ensemble member minus minimum ensemble member) of ground snow albedo exceeding 0.45 in visible and NIR bands. Parameter optimization improves agreement between simulated and in situ observed ground snow albedo in visible, NIR and broadband spectrums. Importantly, optimized parameters result in reduced biases relative to observed fresh-snow albedo and better agreement with observed albedo decay. Our analysis across different sites supports that the optimized BATS ground snow albedo parameters are appropriate to transfer in space and time, at least within the region studied (the central-southern Rocky Mountains). The primary error source remaining after parameter optimization is that observed fresh-snow albedo is highly variable, particularly in the NIR spectrum, whereas BATS fresh-snow albedo is constant, an issue which requires further investigation. This study shows significant correlations between observed fresh-snow albedo and surface meteorological conditions (e.g., downward shortwave radiation and temperature) which can support future model development that attempts to include a time-varying formulation for fresh-snow albedo.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1996

Upper Limit of Weights in TAI Computation

The international reference time scale International Atomic Time (TAI) computed by the Bureau International des Poids et Mesures (BIPM) relies on a weighted average of data from a large number of atomic clocks. In it, the weight attributed to a given clock depends on its long-term stability. In this paper the TAI algorithm is used as the basis for a discussion of how to implement an upper limit of weight for clocks contributing to the ensemble time. This problem is approached through the comparison of two different techniques. In one case, a maximum relative weight is fixed: no individual clock can contribute more than a given fraction to the resulting time scale. The weight of each clock is then adjusted according to the qualities of the whole set of contributing elements. In the other case, a parameter characteristic of frequency stability is chosen: no individual clock can appear more stable than the stated limit. This is equivalent to choosing an absolute limit of weight and attributing this to to the most stable clocks independently of the other elements of the ensemble. The first technique is more robust than the second and automatically optimizes the stability of the resulting time scale, but leads to a more complicated computatio. The second technique has been used in the TAI algorithm since the very beginning. Careful analysis of tests on real clock data shows that improvement of the stability of the time scale requires revision from time to time of the fixed value chosen for the upper limit of absolute weight. In particular, we present results which confirm the decision of the CCDS Working Group on TAI to increase the absolute upper limit by a factor of 2.5. We also show that the use of an upper relative contribution further helps to improve the stability and may be a useful step towards better use of the massive ensemble of HP 507IA clocks now contributing to TAI.

Thomas, Claudine↗
DOE OSTI2020

Coherent control and high-fidelity readout of chromium ions in commercial silicon carbide

Abstract Transition metal ions provide a rich set of optically active defect spins in wide bandgap semiconductors. Chromium (Cr 4+ ) in silicon-carbide (SiC) produces a spin-1 ground state with a narrow, spectrally isolated, spin-selective, near-telecom optical interface. However, previous studies were hindered by material quality resulting in limited coherent control. In this work, we implant Cr into commercial 4H-SiC and show optimal defect activation after annealing above 1600 °C. We measure an ensemble optical hole linewidth of 31 MHz, an order of magnitude improvement compared to as-grown samples. An in-depth exploration of optical and spin dynamics reveals efficient spin polarization, coherent control, and readout with high fidelity (79%). We report T 1 times greater than 1 s at cryogenic temperatures (15 K) with a T 2 * = 317 ns and a T 2 = 81 μs, where spin dephasing times are currently limited by spin–spin interactions within the defect ensemble. Our results demonstrate the potential of Cr 4+ in SiC as an extrinsic, optically active spin qubit.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Lieb-Mattis States for Robust Entangled Differential Phase Sensing

We explore a two-node, entanglement-enhanced sensor network for differential phase sensing that exploits decoherence-free subspaces to suppress common-mode noise, a primary limitation of many state-of-the-art quantum sensors. We identify a class of entangled states that, while not strictly optimal, achieve the same asymptotic sensitivity scaling as optimal states and can be prepared efficiently from initially unentangled atomic ensembles. Importantly, the preparation time decreases with increasing system size, which makes the states compatible with realistic noise processes in present-day quantum sensors that operate with large particle numbers but lack full error correction. We illustrate these ideas using two cavity-mediated preparation protocols: (i) coherent, unitary entanglement generation analogous to bosonic two-mode squeezing, yielding Heisenberg scaling, and (ii) dissipative preparation through collective emission into a shared cavity mode, providing a square-root improvement beyond the standard quantum limit. Numerical simulations show that both approaches remain effective at experimentally realistic cavity cooperativities, establishing a practical path toward scalable, quantum-enhanced differential phase sensing.

Kaubruegger, Raphael [National Institute of Standa↗
DOE OSTI2025

In-Transit Data Transport Strategies for Coupled AI-Simulation Workflow Patterns

Coupled AI-Simulation workflows are becoming the major workloads for HPC facilities, and their increasing complexity necessitates new tools for performance analysis and prototyping of new in-situ workflows. We present SimAI-Bench, a tool designed to both prototype and evaluate these coupled workflows. In this paper, we use SimAI-Bench to benchmark the data transport performance of two common patterns on the Aurora supercomputer: a one-to-one workflow with co-located simulation and AI training instances, and a many-to-one workflow where a single AI model is trained from an ensemble of simulations. For the one-to-one pattern, our analysis shows that node-local and DragonHPC data staging strategies provide excellent performance compared Redis and Lustre file system. For the many-to-one pattern, we find that data transport becomes a dominant bottleneck as the ensemble size grows. Our evaluation reveals that file system is the optimal solution among the tested strategies for the many-to-one pattern.

Tummalapalli, Harikrishna [Argonne National Labora↗
DOE OSTI2026

High-Field Quantum Diamond Magnetometers for Fusion Diagnostics (Final Technical Report)

Summary of research performed to enable all-optical quantum diamond magnetometers for fusion diagnostics, including: the physical mechanisms enabling all-optical photoluminescence contrast as a function of magnetic field magnitude and direction for dense ensembles of nitrogen vacancy (NV) centers in diamond; the characterization and optimization of NV-diamond material; and related investigations and student training.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Ensemble Simulations and Experimental Free Energy Distributions: Evaluation and Characterization of Isoxazole Amides as SMYD3 Inhibitors

Optimization of binding affinities for ligands to their target protein is a primary objective in rational drug discovery. Herein, we report on a collaborative study that evaluates various compounds designed to bind to the SET and MYND domain-containing protein 3 (SMYD3). SMYD3 is a histone methyltransferase and plays an important role in transcriptional regulation in cell proliferation, cell cycle, and human carcinogenesis. Experimental measurements using the scintillation proximity assay show that the distributions of binding free energies from a large number of independent measurements exhibit non-normal properties. We use ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) and TIES (thermodynamic integration with enhanced sampling) protocols to predict the binding free energies and to provide a detailed chemical insight into the nature of ligand-protein binding. Our results show that the 1-trajectory ESMACS protocol works well for the set of ligands studied here. Although one unexplained outlier exists, we obtain excellent statistical ranking across the set of compounds from the ESMACS protocol and good agreement between calculations and experiments for the relative binding free energies from the TIES protocol. ESMACS and TIES are again found to be powerful protocols for the accurate comparison of the binding free energies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Quantum Logic Enhanced Sensing in Solid-State Spin Ensembles

We demonstrate quantum logic enhanced sensitivity for a macroscopic ensemble of solid-state, hybrid two-qubit sensors. We achieve over a factor of 30 improvement in the single-shot signal-to-noise ratio, translating to an ac magnetic field sensitivity enhancement exceeding an order of magnitude for time-averaged measurements. Using the electronic spins of nitrogen vacancy (NV) centers in diamond as sensors, we leverage the on-site nitrogen nuclear spins of the NV centers as memory qubits, in combination with homogeneous and stable bias and control fields, ensuring that all of the ~10 9 two-qubit sensors are sufficiently identical to permit global control of the NV ensemble spin states. We find quantum logic sensitivity enhancement for multiple measurement protocols with varying optimal sensing intervals, including XY8 and DROID-60 dynamical decoupling, as well as correlation spectroscopy, using an applied ac magnetic field signal. The results are independent of the nature of the target signal and broadly applicable to measurements using NV centers and other solid-state spin ensembles. Furthermore, this work provides a benchmark for macroscopic ensembles of quantum sensors that employ quantum logic or quantum error correction algorithms for enhanced sensitivity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Quantifying Northern High Latitude Gross Primary Productivity (GPP) Using Carbonyl Sulfide (OCS)

The northern high latitude (NHL, 40°N to 90°N) is where the second peak region of gross primary productivity (GPP) other than the tropics. The summer NHL GPP is about 80% of the tropical peak, but both regions are still highly uncertain. Carbonyl sulfide (OCS) provides an important proxy for photosynthetic carbon uptake. Here we optimize the OCS plant uptake fluxes across the NHL by fitting atmospheric concentration simulation with the GEOS-CHEM global transport model to the aircraft profiles acquired over Alaska during NASA's Carbon in Arctic Reservoirs Vulnerability Experiment (2012–2015). We use the empirical biome-specific linear relationship between OCS plant uptake flux and GPP to derive the six plant uptake OCS fluxes from different GPP data. Such GPP-based fluxes are used to drive the concentration simulations. We evaluate the simulations against the independent observations at two ground sites of Alaska. The optimized OCS fluxes suggest the NHL plant uptake OCS flux of -247 Gg S year -1 , about 25% stronger than the ensemble mean of the six GPP-based OCS fluxes. GPP-based OCS fluxes systematically underestimate the peak growing season across the NHL, while a subset of models predict early start of season in Alaska, consistent with previous studies of net ecosystem exchange. The OCS optimized GPP of 34 PgC yr -1 for NHL is also about 25% more than the ensembles mean from six GPP data. Further work is needed to fully understand the environmental and biotic drivers and quantify their rate of photosynthetic carbon uptake in Arctic ecosystems.

54 ENVIRONMENTAL SCIENCES↗