Search NASA⌕ Search

SEARCH · Search NASA

Results for “stochastic methods”

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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Rapidly convergent quantum Monte Carlo using a Chebyshev projector

The multireference coupled-cluster Monte Carlo (MR-CCMC) algorithm is a determinant-based quantum Monte Carlo (QMC) algorithm that is conceptually similar to Full Configuration Interaction QMC (FCIQMC). It has been shown to offer a balanced treatment of both static and dynamic correlation while retaining polynomial scaling, although application to large systems with significant strong correlation remained impractical. In this paper, we document recent algorithmic advances that enable rapid convergence and a more black-box approach to the multireference problem. These include a logarithmically scaling metric-tree-based excitation acceptance algorithm to search for determinants connected to the reference space at the desired excitation level and a symmetry-screening procedure for the reference space. We show that, for moderately sized reference spaces, the new search algorithm brings about an approximately 8-fold acceleration of one MR-CCMC iteration, while the symmetry screening procedure reduces the number of active reference space determinants with essentially no loss of accuracy. We also introduce a stochastic implementation of an approximate wall projector, which is the infinite imaginary time limit of the exponential projector, using a truncated expansion of the wall function in Chebyshev polynomials. Notably, this wall-Chebyshev projector can be used to accelerate any projector-based QMC algorithm. We show that it requires significantly fewer applications of the Hamiltonian to achieve the same statistical convergence. We benchmark these acceleration methods on the beryllium and carbon dimers, using initiator FCIQMC and MR-CCMC with basis sets up to cc-pVQZ quality.

Zhao, Zijun↗
DOE OSTI2025

Raptor

Raptor is an efficient Python-based tool for predicting the formation and morphology of stochastic lack of fusion defects in metal AM processes. A major obstacle for the qualification and certification of additively manufactured parts in critical applications continues to be performance variability caused in part by porosity-related defects. High-fidelity process models that could predict these defect features are currently too computationally expensive for component-level analysis. To address this, Raptor employs a high-performance geometric method to model the dynamic melt pool rather than relying on computationally intensive thermal fluid dynamics. This allows Raptor to rapidly identify regions of unmelted material that correspond to lack of fusion pores. The efficiency of this approach significantly reduces the time and resources needed for generating 3D defect predictions, which enables users to conduct large-scale parameter studies and evaluate how process variations affect part quality. The framework offers operational flexibility; users can execute simulations through a simple command line interface or integrate core functions as a library within larger computational workflows. Simulation outputs include 3D porosity maps for visualization and tools for quantitative morphological analysis. These results are suitable for direct comparison with experimental characterization data from methods such as X-ray computed tomography and can be used for statistical process optimization.

Subraveti, Vamsi [Vanderbilt Univ., Nashville, TN ↗
DOE OSTI2025

Quasiprobabilistic Readout Correction of Midcircuit Measurements for Adaptive Feedback via Measurement Randomized Compiling

Quantum measurements are a fundamental component of quantum computing. However, on present-day quantum computers, measurements can be more error prone than quantum gates and are susceptible to nonunital errors as well as nonlocal correlations due to measurement crosstalk. While readout errors can be mitigated in postprocessing, this is inefficient in the number of qubits due to a combinatorially large number of possible states that need to be characterized. In this work, we show that measurement errors can be tailored into a simple stochastic error model using randomized compiling, enabling the efficient mitigation of readout errors via quasiprobability distributions reconstructed from the measurement of a single preparation state in an exponentially large confusion matrix. We demonstrate the scalability and power of this approach by correcting readout errors without matrix inversion on a large number of different preparation states applied to a register of eight superconducting transmon qubits. Moreover, we show that this method can be extended to midcircuit measurements used for active feedback via quasiprobabilistic error cancellation, and we demonstrate the correction of measurement errors on an ancilla qubit used to detect and actively correct bit-flip errors on an entangled memory qubit. Our approach enables the correction of readout errors on large numbers of qubits and offers a strategy for correcting readout errors in adaptive circuits in which the results of midcircuit measurements are used to perform conditional operations on nonlocal qubits in real time.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Microstructure Clones

Background: A material’s microstructure drives its material performance. Contemporary crystal plasticity experiments compare full-field strain measurements of polycrystal specimens to models. Because each specimen is unique, it is impossible to know which features of the observed deformation are deterministic vs statistical; thus, differences between model and experiment may or may not be significant. Objective: This paper introduces the invention of microstructure clones. Microstructure clones are 2D oligocrystal specimens that have nearly identical microstructures to remedy the aforementioned experimental limitations. Having specimens with nearly identical microstructures will allow for multiple destructive tests of a microstructure (either as repeats or intentionally different experiments), an ability to “see the future” by providing insight into how a specimen will deform, variability quantification, and experimental investigations of response to small microstructural changes. Methods: This work introduces microstructure clones. Repeatability of these clones is demonstrated in tensile bars of pure nickel. Local strain measurements from digital image correlation are compared between clone specimens and compared to results from a crystal plasticity finite element model. Results: Two sets of microstructure clones were tested in this study and displayed very consistent deformation responses within each clone set. Small observed differences in deformation invite investigation into microstructure stochasticity and the effect of small microstructural and loading differences. Conclusions: Microstructure clones represent a significant shift in understanding structure–property relationships. This work reshapes experimental crystal plasticity to allow for experiments that control for specific variables, quantification of microstructural stochasticity (and other sources of stochasticity), and opportunities for replicating experiments.

Crystal Plasticity↗
DOE OSTI2026

Full-stack Quantification of Variability in Predicting Ion Transport Properties using Machine-learned Interatomic Potentials

Machine-learned interatomic potentials (MLIPs) have become the state-of-the-art for performing accurate, scalable molecular dynamics (MD) simulations. It is therefore crucial to understand and quantify the reliability of MLIPs for downstream property predictions. Uncertainty in predicted properties can arise from limitations in first-principles training data, intrinsic MLIP model errors in representing the data, and the statistical noise introduced during subsequent MD simulations. Using ion transport in Li7P3S11 as a case study, we systematically assess the impact of training set size and selection, neural network stochasticity, and MD sampling statistics on predicted diffusivity and activation energy. We find that when using equivariant MLIP architectures with standard MD protocols, uncertainty arising from MD sampling dominates over model-induced errors. In contrast, MLIP errors relative to the underlying first-principles data are consistently minor. Given this, there are two main routes to improving the accuracy of predictions based on MLIP potentials: adopting higher accuracy reference data generation methods, and improving the MD sampling statistics.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Mortality correlates with tree functional traits across a wood density gradient in the Central Amazon

Introduction: Understanding the mechanisms of tree mortality in tropical ecosystems remains challenging, in part due to the high diversity of tree species and the inherently stochastic nature of mortality. Plant functional traits offer a mechanistic link between plant physiology and performance, yet their ability to predict growth and mortality remains poorly understood. Given recent increases in tree mortality rates in the Amazon forest following extreme drought and wind events, we tested if lower wood density and acquisitive plant functional traits were associated with increased growth and mortality for common co-occurring trees in the Central Amazon. Methods: Seventeen trees of different species with similar sizes but a range in wood density (WD) and wood traits were felled, then assessed for 27 different individual functional parameters, including whole tree architecture, stem xylem anatomical and hydraulic traits and leaf traits. Traits of the individual trees were related to stand-level growth and mortality rates collected periodically over 30 years from nearby permanent inventory plots. Results: Higher wood density was associated with smaller leaf size, lower foliar base cations, lower stem water content and sapwood fraction, in agreement with the fast-slow plant economics spectrum. Lower wood density was associated with more acquisitive characteristics with greater hydraulic capacity and foliar nutrient concentrations, correlating with greater growth and mortality rates. Discussion: Our results show that lower wood density is part of a coordinated suite of traits linked to high resource acquisition, fast growth, and increased mortality risk, providing a functional framework for predicting species performance and forest vulnerability under future climate stress.

demographics↗
DOE OSTI2026

Hierarchical Multi-agent Large Language Model Reasoning for Autonomous Heterogeneous Catalyst Discovery

Artificial intelligence is reshaping scientific exploration, but most methods automate procedural tasks without engaging in scientific reasoning, limiting autonomy in discovery. We demonstrate that hierarchical agentic large language model reasoning can efficiently drive simulation and scientific exploration. Across two chemical applications, CO adsorption on Cu surface transition metal adatoms and on M–N–C catalysts, reasoning-guided exploration reduces required atomistic simulations by up to 90% relative to heuristic or random selection. Comparisons across single-agent, multi-agent, and stochastic baselines show that hierarchical strategies yield more coherent and information-efficient search trajectories. Reasoning traces reveal chemically grounded decisions that cannot be explained by semantic bias or stochastic sampling. We realize these agentic reasoning strategies in Materials Agents for Simulation and Theory in Electronic-structure Reasoning (MASTER), a multimodal system that translates natural language into density functional theory workflows. Altogether, multi-agent collaboration accelerates heterogeneous catalyst discovery and marks a step toward more autonomous, reasoning-guided scientific exploration.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

An analysis of parameter compression and Full-Modeling techniques with Velocileptors for DESI 2024 and beyond

In anticipation of forthcoming data releases of current and future spectroscopic surveys, we present the validation tests and analysis of systematic effects within velocileptors modeling pipeline when fitting mock data from the AbacusSummit N-body simulations. We compare the constraints obtained from parameter compression methods to the direct fitting (Full-Modeling) approaches of modeling the galaxy power spectra, and show that the ShapeFit extension to the traditional template method is consistent with the Full-Modeling method within the standard ΛCDM parameter space. We show the dependence on scale cuts when fitting the different redshift bins using the ShapeFit and Full-Modeling methods. We test the ability to jointly fit data from multiple redshift bins as well as joint analysis of the pre-reconstruction power spectrum with the post-reconstruction BAO correlation function signal. We further demonstrate the behavior of the model when opening up the parameter space beyond ΛCDM and also when combining likelihoods with external datasets, namely the Planck CMB priors. Finally, we describe different parametrization options for the galaxy bias, counterterm, and stochastic parameters, and employ the halo model in order to physically motivate suitable priors that are necessary to ensure the stability of the perturbation theory.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Kinetic modeling of hot tail runaway electron generation during plasma disruptions using the JOREK code

The generation of runaway electrons (REs) during disruptions poses a significant challenge for the operation of tokamaks. The production of these high-energy electrons can cause substantial damage, particularly when the plasma current is high, making it a critical concern for ITER. For the high-temperature plasmas anticipated in ITER, the primary generation of REs may be dominated by the hot tail mechanism, which consists of the acceleration of hot electrons from the pre-disruption population which have not yet thermalized with the bulk following the rapid cooling of the plasma. To account for the significant 3D effects on RE production, a hot tail modeling framework has been developed within the non-linear 3D extended MHD code JOREK. This paper presents the structure of this framework, which is based on test electrons evolving in MHD fields. The verification of the method shows good agreement with the reference DREAM code for 0D test cases, as well as for axisymmetric simulations of 15 MA ITER H-mode disruption scenarios. Furthermore, a proof-of-principle application to a DIII-D case demonstrates the framework’s capability to capture for the first time the hot tail generation in 3D MHD simulations in realistic geometry. Preliminary results suggest that the production of REs is significantly reduced by stochastic losses.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Software For Advanced Large-scale Analysis Of Magnetic Confinement For Numerical Design, Engineering & Research (salamander)

As magnetic confinement fusion energy gains traction internationally to enable abundant energy production, designing components for fusion systems is a pressing challenge. During the planned lifetime of a fusion device, components evolve in extreme environments and must withstand large, repeated thermal loads and bombardment by 14 MeV neutrons, plasma ions, and neutral particles (deuterium, tritium, and helium), corrosive conditions, etc. All these physical processes take place simultaneously, interact in intricate ways, and impose important constraints that can affect performance. Experimental data is rare and costly to obtain, making design particularly challenging. Predictive computational frameworks must be an integral part of an accelerated and cost-effective design process by modeling fusion system performance in simulated environments. To better understand component degradation and operational impacts on their performance, the Software for Advanced Large-scale Analysis of MAgnetic confinement for Numerical Design, Engineering & Research (SALAMANDER) is designed as an open-source, fully integrated, multiphysics, multiscale, NQA-1 compliant framework facilitating 3D, high-fidelity fusion system modeling. To that end, SALAMANDER is a MOOSE-based framework, and therefore leverages MOOSE upstream libraries such as PETSc and libMesh to deliver sophisticated finite element, finite volume, and nonlinear solver technology for fusion energy simulations. SALAMANDER couples MOOSE physics module capabilities—such as thermal hydraulics, heat conduction, Navier-Stokes, and thermomechanics—with tritium transport via TMAP8, neutronics via Cardinal, and nascent particle-in-cell capabilities. Direct simulation Monte Carlo methods will be used to address neutral transport near the walls. By coupling all these physics in an integrated application, SALAMANDER will enable high-fidelity modeling of irradiation levels and plasma exposure conditions of plasma facing components and their impact on heat and tritium distributions, as well as the resulting mechanical constraints experienced by the plasma facing components and performance of blanket systems. Furthermore, SALAMANDER will be particularly suited for engineering studies thanks to the stochastic tool module readily available in MOOSE, allowing for extended uncertainty quantification and risk analysis studies. It is also able to use computer-aided design (CAD) meshes to model complex geometries, which is indispensable for fusion systems. SALAMANDER therefore supports design, safety, engineering, and research projects for magnetic confinement fusion systems

Simon, Pierre-Clement [Idaho National Laboratory (↗
DOE OSTI2025

High-energy ion beams generated with high efficiency using laser-driven 3D microstructures

Laser-driven ion acceleration in plasma is being proposed as a source of ion beams with a high peak current that can be useful in many fields of science and medicine. Using this method, high proton energies have been achieved by increasing the laser power and by using ultrathin (≤ 200 nm) foils. However, this approach is limited by survivability of the nanotargets to laser prepulses and by difficulty in controlling the plasma acceleration properties. Here, we introduce a new target platform using two-photon polymerization, 3D laser-printed “clone” microstructures with average densities lower than solid that are relatively insensitive to the laser prepulse. Two types of microstructured targets consisting of either a multilayered log-pile or a stochastic arrangement of one micron diameter wires are used. Both demonstrate a higher energy and higher yield proton acceleration compared to thin solid-density foil targets by the robust target normal sheath acceleration (TNSA) mechanism. We find that when such 10–20 μm thick structures are irradiated with a petawatt laser, protons with energies up to 110 MeV and a laser-to-proton conversion efficiency of ~ 10% are obtained. Our work suggests that such microstructures optimized for 60–200 MeV compact proton accelerators are promising for future radiotherapy and other applications.

Physics - Plasma physics↗
DOE OSTI2026

Deterministic Quantum Trajectory via Imaginary Time Evolution

Stochastic quantum trajectories, such as pure state evolutions under unitary dynamics and random measurements, offer a crucial ensemble description of many-body open system dynamics. Recent studies have highlighted that individual quantum trajectories also encode essential physical information. Prominent examples include measurement-induced phase transitions, where a pure quantum state corresponding to fixed measurement outcomes (trajectories) exhibits distinct entanglement phases, depending on the measurement rate. However, direct observation of this effect is hindered by an exponential postselection barrier, whereby the probability of realizing a specific trajectory is exponentially small. We propose a deterministic method to efficiently prepare quantum trajectories in polynomial time using imaginary time evolution and, thus, overcome this fundamental challenge. Here, we demonstrate that our method applies to a certain class of quantum states, and argue that universal approaches do not exist for any quantum trajectories. Our result paves the way for experimentally exploring the physics of individual quantum trajectories at scale and enables direct observation of certain postselection-dependent phenomena.

Mittal, Shivan [Los Alamos National Laboratory (LA↗
DOE OSTI2026

Scaling kinetic Monte-Carlo simulations of grain growth with combined convolutional and graph neural networks

Graph neural networks (GNN) have emerged as a promising machine learning method for microstructure simulations such as grain growth. However, accurate modeling of realistic grain boundary networks requires large simulation cells, which GNN has difficulty scaling up to. To alleviate the computational costs and memory footprint of GNN, we suggest a hybrid architecture combining a convolutional neural network (CNN) based bijective autoencoder to compress the spatial dimensions, and a GNN that evolves the microstructure in the latent space of reduced spatial sizes. Our results demonstrate that the new design significantly reduces computational costs with using fewer message passing layer (from 12 down to 3) compared with GNN alone. The reduction in computational cost becomes more pronounced as the spatial size increases, indicating strong computational scalability. For the largest mesh evaluated (160 3 ), our method reduces memory usage and runtime in inference by 117× and 115×, respectively, compared with GNN-only baseline. More importantly, it shows higher accuracy and stronger spatiotemporal capability than the GNN-only baseline, especially in long-term testing. Such combination of scalability and accuracy is essential for simulating realistic material microstructures over extended time scales. The improvements can be attributed to the bijective autoencoder’s ability to compress information losslessly from spatial domain into a high dimensional feature space, thereby producing more expressive latent features for the GNN to learn from, while also contributing its own spatiotemporal modeling capability. Training data are generated from stochastic grain growth simulations, providing realistic variability for learning robust microstructure evolution. Comprehensive system validation confirms that the model is accurate, robust, and scalable.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Z-Target Radiography Postprocessing With A Deep Convolution Neural Network

Analyzing X-ray radiographs is crucial for understanding target behavior in Inertial Confinement Fusion (ICF) and High Energy Density (HED) platforms. However, the density of Magneto Raleigh Taylor (MRT) bands and limitations of target materials often obscure relevant spike growth and density information. To address this issue, machine learning postprocessing techniques can be applied to remove darkened regions in radiography images. In this study, a novel method is presented for removing MRT darkened regions from z-target radiographs using a convolutional neural network (CNN). The CNN, consisting of six layers, treats the darkened regions as noise and employs a mixed loss function and end-to-end frameworks to suppress them while preserving sharpness. The six-layer architecture is designed to effectively learn features when provided with a larger volume of learning space. Each layer is optimized using a mixed loss function that combines a standard loss pixel approach with a multi-scaled structural similarity index loss, which considers luminance, contrast, and structure in local neighborhoods. This approach is particularly beneficial for capturing the stochastic structure of MRT limbs. Due to the limited availability of experimental data, training is conducted using synthetic target radiography from 3D Alegra simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

A Principled Framework to Assess the Information-Theoretic Fitness of Brain Functional Sub-Circuits

In systems and network neuroscience, many common practices in brain connectomic analysis are often not properly scrutinized. One such practice is mapping a predetermined set of sub-circuits, like functional networks (FNs), onto subjects’ functional connectomes (FCs) without adequately assessing the information-theoretic appropriateness of the partition. Another practice that goes unchallenged is thresholding weighted FCs to remove spurious connections without justifying the chosen threshold. This paper leverages recent theoretical advances in Stochastic Block Models (SBMs) to formally define and quantify the information-theoretic fitness (e.g., prominence) of a predetermined set of FNs when mapped to individual FCs under different fMRI task conditions. Our framework allows for evaluating any combination of FC granularity, FN partition, and thresholding strategy, thereby optimizing these choices to preserve the important topological features of the human brain connectomes. By applying to the Human Connectome Project with Schaefer parcellations at multiple levels of granularity, the framework showed that the common thresholding value of 0.25 was indeed information-theoretically valid for group-average FCs, despite its previous lack of justification. Our results pave the way for the proper use of FNs and thresholding methods, and provide insights for future research in individualized parcellations.

Duong-Tran, Duy (ORCID:0009000944967575)↗
DOE OSTI2025

Uncontrolled Learning: Codesign of Neuromorphic Hardware Topology for Neuromorphic Algorithms

Neuromorphic computing has the potential to revolutionize future technologies and our understanding of intelligence, yet it remains challenging to realize in practice. The learning-from-mistakes algorithm, inspired by the brain's simple learning rules of inhibition and pruning, is one of the few brain-like training methods. This algorithm is implemented in neuromorphic memristive hardware through a codesign process that evaluates essential hardware trade-offs. While the algorithm effectively trains small networks as binary classifiers and perceptrons, performance declines significantly with increasing network size unless the hardware is tailored to the algorithm. This work investigates the trade-offs between depth, controllability, and capacity—the number of learnable patterns—in neuromorphic hardware. This highlights the importance of topology and governing equations, providing theoretical tools to evaluate a device's computational capacity based on its measurements and circuit structure. The findings show that breaking neural network symmetry enhances both controllability and capacity. Additionally, by pruning the circuit, neuromorphic algorithms in all-memristive circuits can utilize stochastic resources to create local contrasts in network weights. Through combined experimental and simulation efforts, the parameters are identified that enable networks to exhibit emergent intelligence from simple rules, advancing the potential of neuromorphic computing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Finding MIDDLE Ground: Scalable and Secure Distributed Learning

Edge computing methods allow devices to efficiently train a high-performing, robust, and personalized model for predictive tasks. However, these methods succumb to privacy and scalability concerns such as adversarial data recovery and expensive model communication. Furthermore, edge computing methods unrealistically assume that all devices train an identical model. In practice, edge devices have varying computational and memory constraints which may not allow certain devices to have the space or speed to train a specific model. To overcome these issues, we propose MIDDLE: a model independent distributed learning algorithm which allows heterogeneous edge devices to assist each other’s training while communicating only non-sensitive information. MIDDLE unlocks the ability for edge devices, regardless of computational or memory constraints, to assist each other even with completely different model architectures. Furthermore, MIDDLE does not require model or gradient communication which greatly reduces communication size and time. We prove that MIDDLE attains the optimal convergence rate O(1/sqrt(TM)) of stochastic gradient descent for convex and non-convex smooth optimization (for total iterations T and batch size M). Finally, our experimental results demonstrate that MIDDLE (even in non-IID data settings) attains robust and high-performing models without model or gradient communication.

Bornstein, Marc I.↗
DOE OSTI2024

MDLoader: A Hybrid Model-Driven Data Loader for Distributed Graph Neural Network Training

Scalable data management is essential for processing large scientific dataset on HPC platforms for distributed deep learning. In-memory distributed storage is preferred for its speed, enabling rapid, random, and frequent data access required by stochastic optimizers. Processes use one-sided or collective communication to fetch remote data, with optimal performance depending on (i) dataset characteristics, (ii) training scale, and (iii) interconnection network. Empirical analysis shows collective communication excels with larger mini-batch sizes and/or fewer processes, whereas one-sided communication outperforms at larger scales. We propose MDLoader, a hybrid in-memory data loader for distributed graph neural network training. MDLoader features a model-driven performance estimator that dynamically selects between one-sided and collective communication at the beginning of training using Tree of Parzen Estimators (TPE). Evaluations on NERSC Perlmutter and OLCF Summit show MDLoader outperforms single-backend loaders by up to 2.83 × and predicts the suitable communication method with 96.3% (Perlmutter) and 94.3% (Summit) success rate.

Bae, Jonghyun↗