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Results for “Robust experimental design”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Identifiability and characterization of transmon qutrits through Bayesian experimental design

Robust control of a quantum system is essential to utilize the current noisy quantum hardware to its full potential, such as quantum algorithms. To achieve such a goal, a systematic search for an optimal control for any given experiment is essential. The design of optimal control pulses requires accurate numerical models and, therefore, accurate characterization of the system parameters. We present an online Bayesian approach for quantum characterization of qutrit systems, which automatically and systematically identifies optimal experiments that provide maximum information on the system parameters, thereby greatly reducing the number of experiments that need to be performed on the quantum testbed. Unlike most characterization protocols that provide point-estimates of the parameters, the proposed approach is able to estimate their probability distribution. The applicability of the Bayesian experimental design technique was demonstrated on test problems, where each experiment was defined by a parameterized control pulse. In addition to this, we also present an approach for iterative pulse extension, which is robust under uncertainties in transition frequencies and coherence times, and shot noise, despite being initialized with wide uninformative priors. Furthermore, we provide a mathematical proof of the theoretical identifiability of the model parameters and present conditions on the quantum state under which the parameters are identifiable. The proof and conditions for identifiability are presented for both closed and open quantum systems using the Schrödinger equation and the Lindblad master equation, respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Robust Optimal Experimental Design of Infinite-Dimensional Bayesian Nonlinear Inverse Problems

Abstract. We consider robust optimal experimental design (ROED) for nonlinear Bayesian inverse problems governed by partial differential equations (PDEs). An optimal design is one that maximizes some utility quantifying the quality of the solution of an inverse problem. However, the optimal design is dependent on elements of the inverse problem such as the simulation model, the prior, or the measurement error model. ROED aims to produce an optimal design that is aware of the additional uncertainties encoded in the inverse problem and remains optimal even after variations in them. We follow a worst-case scenario approach to develop a new framework for robust optimal design of nonlinear Bayesian inverse problems. The proposed framework (a) is scalable and designed for infinite-dimensional Bayesian nonlinear inverse problems constrained by PDEs; (b) develops efficient approximations of the utility, namely the expected information gain; (c) employs eigenvalue sensitivity techniques to develop analytical forms and efficient evaluation methods of the gradient of the utility with respect to the uncertainties against which we wish to be robust; and (d) employs a probabilistic optimization paradigm that properly defines and efficiently solves the resulting combinatorial max-min optimization problem. The effectiveness of the proposed approach is illustrated for optimal sensor placement problem in an inverse problem governed by an elliptic PDE.

Chowdhary, Abhijit↗
DOE OSTI2025

Robust A-Optimal Experimental Design for Sensor Placement in Bayesian Linear Inverse Problems

Optimal design of experiments for Bayesian inverse problems has recently gained wide popularity and attracted much attention, especially in the computational science and Bayesian inversion communities. An optimal design maximizes a predefined utility function that is formulated in terms of the elements of an inverse problem, an example being optimal sensor placement for parameter identification. The state-of-the-art algorithmic approaches following this simple formulation generally overlook misspecification of the elements of the inverse problem, such as the prior or the measurement uncertainties. This work presents an efficient algorithmic approach for designing optimal experimental design schemes for Bayesian linear inverse problems such that the optimal design is robust to misspecification of elements of the inverse problem. Specifically, we consider a worst-case scenario approach for the uncertain or misspecified parameters, formulate robust objectives, and propose an algorithmic approach for optimizing such objectives. Furthermore, both relaxation and stochastic solution approaches are discussed with detailed analysis and insight into the interpretation of the problem and the proposed algorithmic approach. Extensive numerical experiments to validate and analyze the proposed approach are carried out for sensor placement in a parameter identification problem.

Bayesian inverse problems↗
DOE OSTI2019

VUV Photodissociation Induced by a Deuterium Lamp in an Ion Trap

Tandem mass spectrometry represents an important analytical tool to unravel molecular structures and to study the gas-phase behavior of organic molecules. Besides commonly used methods like collision-induced dissociation and electron capture or transfer dissociation, new ultraviolet light–based techniques have the potential to synergistically add to the activation methods. Here, we present a new simple, yet robust, experimental design for polychromatic activation of trapped ions using the 115–160 nm output of a commercially available deuterium lamp. The resulting continuous dissociative excitation with photons of a wide energy range from 7.7 to 10.8 eV is studied for a comprehensive set of analyte classes in both positive and negative ion modes. While being simple, affordable, compact, and of low maintenance, the new setup initiates fragmentation of most precursor ions via their known dissociation pathways. Additionally, some new fragmentation patterns were discovered. Especially, electron loss and electron capture reactions with subsequent fragmentations were observed. For oligonucleotides, peptides, carbohydrates, and organic dyes, in comparison to collision-induced dissociation, a significantly wider fragment distribution was obtained, resulting in an information increase. Since the individual photons carry enough energy to post-ionize the nascent fragments, a permanent vacuum ultraviolet light exposure inside the ion trap potentially goes along with a general increase in detection capability. .

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

TRACER-Tethersonde VOC data

One objective of TRACER-Tethersonde campaign was to deploy a volatile organic compound (VOC) sampler on the Tethered Balloon System (TBS) to create vertical profiles and characterize VOC composition. Volatile organic compounds (VOCs) are highly reactive precursor species that undergo atmospheric processing to form secondary products, including aerosol. VOC emissions are influenced by many factors that include but are not limited to temperature, time of day, local anthropogenic activities, and local vegetation, which result in large spatiotemporal variability. The vertical distribution of volatile species is crucial for understanding gas-phase processing for particle production but challenging to accomplish using currently available sampling devices. The standalone sampler built during the campaign enabled the execution of robust experimental designs, including sample collection using resin tubes at multiple altitudes on subsequent flights. Following field sampling, resin tubes were transported back to Baylor University for chemical analysis using a Markes International thermal desorption unit coupled with a gas chromatograph-tandem mass spectrometer (Thermo Scientific). The target analyte list includes biogenic and anthropogenic VOCs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Learning-Based Inversion-Free Model-Data Integration to Advance Ecosystem Model Prediction

Ecosystem model prediction is important for understanding ecosystem responses to climate change and for management support. Model prediction and quantification of predictive uncertainty of ecosystems have long been investigated. The traditional workflow, which calibrates models to match observations and then uses the calibrated models for predictions, relies heavily on inverse modeling to constrain uncertain parameters in complex forward models. This inversion-based prediction approach is infeasible for complex models with heterogeneous parameter uncertainties and incapable of rapid integration of streaming and multiple sources of data because of the difficulty and computational cost in the model inversion, which is typically ill-posed and can require hundreds of thousands of expensive forward simulations to be performed iteratively. We propose to circumvent inverse modeling by precomputing an ensemble of unconstrained forward simulations and then using machine learning (ML) methods to learn the statistical relationship between simulated observation and prediction quantities. Once the ML model has learned the relationship, it can be used to make predictions of future system behavior with uncertainty quantification based on observations. The proposed learningbased inversion-free model prediction (LIMP) framework is computationally efficient which only requires a few thousands of fully parallelizable forward simulations. Additionally, LIMP can continually update predictions based on streaming observations from multiple locations and sources without necessarily requiring extra model simulations. In this study, we apply LIMP to a regional terrestrial ecosystem model with 47 parameters for testing, refining, and evaluating the approach. We demonstrate that LIMP can be used for efficient model prediction, rapid data assimilation, and cost-effective experimental design for improving robust predictive understanding of ecosystems.

Lu, Dan↗
DOE OSTI2023

Angle-robust two-qubit gates in a linear ion crystal

In trapped-ion quantum computers, two-qubit entangling gates are generated by applying spin-dependent force which uses phonons to mediate interaction between the internal states of the ions. To maintain high-fidelity two-qubit gates under fluctuating experimental parameters, robust pulse-design methods are applied to remove the residual spin-motion entanglement in the presence of motional mode-frequency drifts. Here we propose an improved pulse-design method that also guarantees the robustness of the two-qubit rotation angle against uniform mode-frequency drifts by concatenating pulses with opposite sensitivity of the angle to mode-frequency drifts. Here we experimentally verify significantly improved robustness of the rotation angle against uniform mode-frequency drifts, as well as observe an improvement in gate fidelity from 97.84(10)% to 98.11(11)%, compared to a single frequency-modulated pulse.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Transfer learning driven design optimization for inertial confinement fusion

Transfer learning is a promising approach to create predictive models that incorporate simulation and experimental data into a common framework. In this technique, a neural network is first trained on a large database of simulations and then partially retrained on sparse sets of experimental data to adjust predictions to be more consistent with reality. Previously, this technique has been used to create predictive models of Omega [Humbird et al., IEEE Trans. Plasma Sci. 48, 61–70 (2019)] and NIF [Humbird et al., Phys. Plasmas 28, 042709 (2021); Kustowski et al., Mach. Learn. 3, 015035 (2022)] inertial confinement fusion (ICF) experiments that are more accurate than simulations alone. Here, in this work, we conduct a transfer learning driven hypothetical ICF campaign in which the goal is to maximize experimental neutron yield via Bayesian optimization. The transfer learning model achieves yields within 5% of the maximum achievable yield in a modest-sized design space in fewer than 20 experiments. Furthermore, we demonstrate that this method is more efficient at optimizing designs than traditional model calibration techniques commonly employed in ICF design. Such an approach to ICF design could enable robust optimization of experimental performance under uncertainty.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Comparison of chamber beam geometry robustness to mispointing, imbalance and target offset for direct-drive laser fusion facilities

This study focuses on the optimization of beam chamber geometry designs for future direct-drive laser facilities. It provides a review of leading target chamber geometries, with a particular emphasis on random errors. Through comprehensive solid-sphere illuminations and analysis, we identify an optimized beam geometry design, highlighting its robustness and performance under realistic experimental conditions. Three major sources of random errors are evaluated, closely linked to experimental evaluations at OMEGA. The findings underscore the importance of optimizing the irradiation system alongside beam pattern considerations to enhance the efficiency and reliability of inertial confinement fusion experiments. We conclude that for a desired illumination uniformity of 1% in the presence of system errors, the split icosahedron design is the most robust. However, for a 0.3% uniformity goal, the charged-particle, icosahedron, and t-sphere methods exhibit similar performance.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Passive Energy-Saving Solutions for Clothes Dryers: A Modeling and Experimental Study for Improved Efficiency and Affordability

Dryers are integral appliances in modern households, yet their significant power consumption remains a critical challenge for reducing energy bills and upgrades. This work presents the outcomes of a comprehensive investigation aimed at lowering the operating energy costs of the dryer through modeling and experimental approaches. Specifically, the objectives of this research are twofold: (1) to reduce energy consumption without negatively impacting drying performance or time; and (2) to ensure affordability by developing retrofittable solutions characterized by low cost and a quick payback period. These strategies promise universal applicability to all dryer categories, encompassing both gas and electric models, by tackling core issues such as unnecessary heat loss and excess energy supply, both prevalent across dryer designs. Our methodology combines robust modeling frameworks and experimental validation to target energy efficiency improvements through three primary pathways: (1) effective heat loss management using ultralow-cost insulation materials tailored for dryer systems; (2) heat management across the drying cycle, facilitated by passive heat transfer mechanisms, and (3) optimization of heat supply to the load to ensure precise energy delivery, minimizing waste. These innovations are designed to seamlessly integrate with existing dryer configurations, providing a scalable and retrofittable solution that ensures affordability without requiring substantial redesigns or expensive components. Using these methods, the study demonstrates improvement opportunities in dryer energy consumption while maintaining the desired performance. The modeling component utilizes computational simulations to evaluate the thermal and energy performance of these innovations under varied operational conditions, providing foundational insights for experimental design. By addressing critical areas such as heat loss, energy recovery, and supply optimization, this work proposes impactful solutions for lowering the energy costs in domestic clothes dryers. The ultralow-cost, retrofittable nature of the proposed strategies ensures widespread adoption potential across diverse dryer categories, making significant strides toward affordable household energy practices.

Cheekatamarla, Praveen [ORNL] (ORCID:0000000248827↗
DOE OSTI2023

High throughput discovery of ternary Cu-Fe-Ru alloy catalysts for photo-driven hydrogen production

Light driven hydrogen production from the water splitting reaction has the ability to reduce dependence on fossil fuels in a green energy future. Here, we highlight the discovery of Cu x Ru y Fe 1-x-y nanoparticle catalysts for photo-driven hydrogen production. Through a high throughput experimental setup, robust data management pipelines and intentional experimental design, this study uncovered three highly active bimetallic systems for photo-driven hydrogen and identified a new trimetallic catalyst for this system. In most cases, the multimetallic catalysts outperformed the monometallics. Furthermore, this study highlights the expansive catalytic screening capabilities of this system in contrast to traditional catalytic selection processes through the discovery of distributions of particle compositions in binary and ternary mixtures of metals with high activity for hydrogen evolution.

08 HYDROGEN↗