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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Sensitive dependence on initial conditions in a formation of magnetic vortices

The magnetic vortex exhibits promise as a true random number generator for hardware-based encryption and probabilistic computing due to its stochastic formation of energetically equivalent fourfold degenerate states, characterized by two topologies: polarity and chirality. However, a comprehensive understanding of the stochastic formation of magnetic vortices remains elusive. In this work, we show that the magnetization relaxation in asymmetric Permalloy disks evolves along a pitchfork bifurcation, with both bifurcation paths leading to the formation of magnetic vortices with the same chirality. In the bifurcation, one formation path is always chosen under weak in-plane magnetic fields, ultimately determining the final magnetic vortex state. By delaying the in-plane magnetic field, we quantitatively investigate when the final vortex state is determined and find that it is closely associated with the initial conditions rather than the bifurcation point itself. Our findings provide valuable insights into future spintronic-based encryption and probabilistic computing.

Jeong, Suyeong↗
DOE OSTI2026

Efficient many-jet event generation with flow matching

We apply for the first time, to the best of our knowledge, the flow matching method to the problem of phase-space sampling for event generation in high-energy collider physics. By training the model to remap the random numbers used to generate the momenta and helicities of the scattering matrix elements as implemented in the portable partonic event generator pepper, we find substantial efficiency improvements in the studied processes. We focus our study on the highest final-state multiplicities in Drell-Yan and top-antitop pair production used in simulated samples for the Large Hadron Collider, which computationally are the most relevant ones. We find that the unweighting efficiencies improve by factors of 184 and 25, respectively, when compared to the standard approach of using a vegas-based optimization. We also compare continuous normalizing flows trained with flow matching against the previously studied normalizing flows based on coupling layers and find that the former leads to better results, faster training and a better scaling behavior across the studied multiplicity range, while the latter evaluate faster. When combining the advantages of both methods using the regflow approach, we find parton-level unweighted event generation walltime gains of about a factor of 10 at the highest final-state multiplicities.

Bothmann, E. [CERN; Gottingen U.] (ORCID:000000016↗
DOE OSTI2026

Monte Carlo Event Generation with Continuous Normalizing Flows

We apply continuous normalizing flows trained with the flow matching method to the problem of phase-space sampling in Monte Carlo event generation for high-energy collider physics. Focusing on lepton-pair and top-quark pair production with multiple jets, the two computationally most expensive processes at the Large Hadron Collider, we train helicity-conditioned continuous normalizing flows to remap the random numbers used in matrix element evaluation. Compared to standard methods, we achieve unweighting efficiency improvements by factors of up to 184 and 25 for the two processes at their respective highest jet number, at the cost of an increased evaluation time. When combining the advantages of continuous normalizing flows with the fast evaluation times of coupling-layer-based flows, using the RegFlow approach, we find parton-level unweighted event generation walltime gains of about a factor of 10 at the highest jet numbers. These substantial gains highlight the promise of samplers based on machine learning for next-generation collider experiments.

Bothmann, Enrico [CERN; Gottingen U.] (ORCID:00000↗
DOE OSTI2026

3D Deep Learning Joint Inversion of Active Seismic Full Waveform and Passive Seismic Traveltime Data for Reservoir Imaging and Uncertainty Quantification

Here, we present deep learning (DL) networks for three-dimensional (3D) joint inversion of active seismic full waveform and passive seismic traveltime data to image reservoirs and their properties and quantify imaging uncertainties. Active seismic full-waveform data can provide high-resolution monitoring images but are collected only intermittently because of their high acquisition cost. In contrast, passive seismic data can be gathered at relatively low cost between regular active surveys, although their imaging quality can be compromised by factors such as low signal-to-noise ratios and limited ray coverage of the target. Although these datasets are routinely acquired together at CO 2 storage sites, their combined inversion within a 3D DL framework has not been previously demonstrated. To our knowledge, this is the first study to address this gap, combining the strength of both data types. For efficient data storage and DL training with large 3D seismic datasets, we use a 3D data matrix in which a random number of passive seismic traveltime data are stored as parabolic envelopes using one-hot encoding and a 3D full-waveform data matrix in which multiple shot gathers are summed. Two network architectures are evaluated: a single-encoder U-Net for single-data type inversion and a dual-encoder U-Net for joint inversion of active and passive seismic data. We also evaluate the single-encoder U-Net for joint inversion by concatenating full-waveform data and traveltime data. We propose a systematic approach for selecting an optimal dropout rate that balances regularization during training and Monte Carlo dropout-based uncertainty quantification during prediction by examining the correlation coefficient between standard deviation and prediction error, along with the training misfit, across a range of dropout rates. 3D DL inversion experiments include five different network configurations, with evaluations under ideal, noisy and dropout-enabled conditions. Both model and data uncertainties are assessed, as well as their combined effects. Across all conditions, the networks consistently predict accurate CO 2 saturation models with low prediction errors, such as a structural similarity index of 0.993 and CO 2 difference of 1.1%. Uncertainty estimates show strong spatial correlation with prediction errors, confirming the effectiveness of the proposed dropout selection approach. The results demonstrate that our DL approach, utilizing compact data representations and appropriate uncertainty quantification, yields accurate subsurface images under various inversion conditions and provides valuable insights into the reliability of predictions.

Um, Evan Schankee [Lawrence Berkeley National Labo↗
DOE OSTI2025

Double Bootstrapping

This code performs a simulation experiment that involves (i) drawing random numbers from the normal distribution, (ii) resampling elements from arrays with replacement, and (iii) computing various quantities like mean, standard deviation, etc. Further information is available in section 4 of FERMILAB-FN-1273-ETD [https://inspirehep.net/literature/2925453].

Shyamsundar, Prasanth [Fermi National Accelerator ↗
DOE OSTI2024

Passive continuous variable quantum key distribution scheme using a thermal source

A passive continuous variable quantum key distribution scheme, where Alice splits the output of a thermal source into two beams, measures one locally and transmits the other mode to Bob after applying attenuation. A secure key can be established based on measurements of the two beams without the use of a random number generator or an optical modulator.

Qi, Bing↗
DOE OSTI2024

Improved multifidelity Monte Carlo estimators based on normalizing flows and dimensionality reduction techniques

Here, we study the problem of multifidelity uncertainty propagation for computationally expensive models. In particular, we consider the general setting where the high-fidelity and low-fidelity models have a dissimilar parameterization both in terms of number of random inputs and their probability distributions, which can be either known in closed form or provided through samples. We derive novel multifidelity Monte Carlo estimators which rely on a shared subspace between the high-fidelity and low-fidelity models where the parameters follow the same probability distribution, i.e., a standard Gaussian. We build the shared space employing normalizing flows to map different probability distributions into a common one, together with linear and nonlinear dimensionality reduction techniques, active subspaces and autoencoders, respectively, which capture the subspaces where the models vary the most. We then compose the existing low-fidelity model with these transformations and construct modified models with an increased correlation with the high-fidelity model, which therefore yield multifidelity estimators with reduced variance. A series of numerical experiments illustrate the properties and advantages of our approaches.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Coexistence of Synchronization and Stochasticity in Thermally Coupled Mott Oscillators

Synchronization is conventionally regarded as a mechanism for suppressing variability and enforcing order in coupled systems, from pendula and lasers to neurons and electronic oscillators. Here, we show that synchronization can also embed stochasticity at finer scales. We observe this phenomenon in thermally coupled VO 2 neuristors, where robust in-phase synchronization at the microsecond scale coexists with spike onset fluctuations at the nanosecond scale, with no fixed leader. The coexistence of order and disorder originates from stochastic domain-level physics of the insulator–metal and metal–insulator transitions, where local variations in transition temperature drive cycle-to-cycle randomness in nucleation, percolation, and relaxation. A stochastic domain model reproduces this effect by generating synchronized spike trains with random lead–lag jitter, and experimental interspike interval statistics confirm the persistence of fine-scale variability despite macroscopic phase locking. These findings establish that synchronization and stochasticity can coexist within the same physical platform, revealing hidden disorder within collective order. Furthermore, this insight reframes synchronization as not purely deterministic, but as a universal context where microscopic variability can persist, with implications for electronics, cryptography, and the fundamental physics of order–disorder coexistence.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Bistable random momentum transfer in a linear on-chip resonator

Optical switches and bifurcation rely on the nonlinear response of materials. Here, we demonstrate linear temporal bifurcation responses in a passive multimode microresonator, with strongly coupled chaotic and whispering gallery modes (WGMs). In microdisks, the chaotic modes exhibit broadband transfer within the deformed cavities, but their transient response is less explored and yields a random output of the analog signal distributed uniformly from “0” to “1.” Here, we build chaotic states by perturbing the multimode microring resonators with densely packed silicon nanocrystals on the waveguide surface. In vivo measurements reveal random and “digitized” output that ONLY populates around 0 and 1 intensity levels. The bus waveguide mode couples first to chaotic modes, then either dissipates or tunnels into stable WGMs. This binary pathway generates high-contrast, digitized outputs. In conclusion, the fully passive device enables real-time conversion of periodic clock signals into binary outputs with contrasts exceeding 12.3 dB, data rates of up to 10 7 · bits per second, and 20 dB dynamic range.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Derivative-free stochastic optimization via adaptive sampling strategies

In this paper, we present a novel derivative-free framework for solving unconstrained stochastic optimization problems. Many problems in fields ranging from simulation optimization to reinforcement learning to quantum computing involve settings where only stochastic function values are obtained via a zeroth-order oracle, which has no available gradient information and necessitates the usage of derivative-free optimization methodologies. Our approach includes estimating gradients using stochastic function evaluations and integrating adaptive sampling techniques to control the accuracy in these stochastic approximations. Our framework encapsulates several gradient estimation techniques, including standard finite-difference, Gaussian smoothing, sphere smoothing, randomized coordinate finite-difference, and randomized subspace finite-difference methods. We provide theoretical convergence guarantees for our framework and analyze the worst-case iteration and sample complexities associated with each gradient estimation method. Finally, we demonstrate the empirical performance of the methods on logistic regression and nonlinear least squares problems.

Adaptive sampling↗
DOE OSTI2025

I/O in Machine Learning Applications on HPC Systems: A 360-degree Survey

Growing interest in Artificial Intelligence (AI) has resulted in a surge in demand for faster methods of Machine Learning (ML) model training and inference. This demand for speed has prompted the use of high performance computing (HPC) systems that excel in managing distributed workloads. Because data is the main fuel for AI applications, the performance of the storage and I/O subsystem of HPC systems is critical. In the past, HPC applications accessed large portions of data written by simulations or experiments or ingested data for visualizations or analysis tasks. ML workloads perform small reads spread across a large number of random files. This shift of I/O access patterns poses several challenges to modern parallel storage systems. In this paper, we survey I/O in ML applications on HPC systems, and target literature within a 6-year time window from 2019 to 2024. We define the scope of the survey, provide an overview of the common phases of ML, review available profilers and benchmarks, examine the I/O patterns encountered during offline data preparation, training, and inference, and explore I/O optimizations utilized in modern ML frameworks and proposed in recent literature. Lastly, we seek to expose research gaps that could spawn further R&D.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Simulation of a Wireless Authentication Protocol

Abstract—A wireless authentication protocol that employs timeslots and associated frequency-channels (APEC) is simulated using Python as the simulation environment and implemented into radio hardware as a proof of concept. The APEC protocol does not rely on the use of challenge-response, multifactor authentication schemes but relies instead on the physical properties of a wireless signal. The APEC protocol provides opportunities for real-time deployment in cell phone network infrastructure, as well as in adverse civil and military applications. Description of the APEC implementation and the corresponding results of the simulation study are presented.

42 - ENGINEERING↗
DOE OSTI2024

APEC Poster

A wireless authentication protocol that employs timeslots and associated frequency- channels (APEC) is simulated using Python as the simulation environment and implemented into radio hardware as a proof of concept. The APEC protocol does not rely o n the use of challenge -response, multifactor authentication schemes but relies instead on the physical properties of a wireless signal. The APEC protocol provides opportunities for real-time deployment in cell phone network infrastructure, as well as in adverse civil and military applications. Description of the APEC implementation and the corresponding results of the simulation study are presented.

42 - ENGINEERING↗
DOE OSTI2026

Toward memory-efficient melt pool monitoring: a classification framework using event-based imaging and sparse sensing technique

Vision sensors like CMOS and CCD cameras are often used for in-process monitoring of melt pools in laser-based additive and welding processes, but they require transferring large amounts of data and computational processing resources. Event-based neuromorphic imagery, on the other hand, detects only the change in pixel intensity, thus potentially reducing the data amount and latency. With an event imager, this study develops a framework for melt pool condition classification, including image construction, time scale selection, optimal pixel selection, and sparse classification, to achieve a highly memory-efficient scheme. These are based on sparse sensing techniques with singular value decomposition (SVD) and QR pivoting, the two fundamental matrix transformations for linear dimensionality reduction. The framework is then validated by classifying a controlled experiment by exciting various mode shapes of liquid gallium pools of varying depths (3, 6, and 8 mm). At 200 pixels, the classifier can reach overall accuracy of 75%, while at 2000 pixels (0.013% of the total possible pixels), the accuracy is nearly 90% (89.86%). At the same number of pixels, random selection can only achieve 46% and 67%, respectively. The memory savings of the sparsely sampled event data compared to a conventional imager is about 500 times. In addition to performance, implementation and limitations of the framework are also discussed.

42 ENGINEERING↗
DOE OSTI2026

Understanding the Influence of Chain Architecture on the Transport Quantities of Polymer Electrolytes with Covalently Bonded Anions

Here, we use a combination of experiments and coarse-grained molecular dynamics simulations to elucidate the structure–property relationships in polymer electrolytes obtained by the copolymerization of poly(vinyl ethylene carbonate─lithium styrene bis(trifluoromethanesulfonyl)imide) or p(VEC-LiSTFSI). Experiments show that the conductivity reduces with increasing anion (i.e., STFSI) fraction on the chain, and the cation transference number (t + ) is found to be dependent on the anion fraction. Furthermore, a significant fraction of unpolymerized VEC monomers are observed. Since it is inherently difficult to experimentally control the chain architecture and the amount of unpolymerized VEC in these systems, we perform coarse-grained molecular dynamics simulations on model polymer systems with different chain architectures to mimic the plausible experimental systems. Specifically, we look at the differences in transference numbers arising from (i) a random copolymer of VEC and STFSI monomers; (ii) a blend of VEC-STFSI copolymer with VEC monomers; and (iii) a ternary blend of the VEC homopolymer, STFSI homopolymers, and VEC monomers. The ternary blend model demonstrates the closest resemblance with the experimental transference numbers and diffusivities. The lithium diffusivity obtained from the coarse-grained models with VEC monomers (plasticizers) is about 1.5 times that of the model without VEC monomers, showing that the plasticizing effect of VEC monomers is modest. We rationalize the experimental observations based on aggregate and cluster analyses obtained from molecular simulations. This work reveals that polymer electrolyte chain architecture and plasticizers can critically influence the transport properties, and these parameters should be considered when designing single ion conducting polymeric electrolytes.

cluster distribution↗
DOE OSTI2024

Clustering and Cliques in Preferential Attachment Random Graphs with Edge Insertion

In this paper, we investigate the global clustering coefficient (a.k.a transitivity) and clique number of graphs generated by a preferential attachment random graph model with an additional feature of allowing edge connections between existing vertices. Specifically, at each time step t, either a new vertex is added with probability f(t), or an edge is added between two existing vertices with probability 1 – f(t). We establish concentration inequalities for the global clustering and clique number of the resulting graphs under the assumption that f(t) is a regularly varying function at infinity with index of regular variation –$\gamma$, where $\gamma$ $\in$ [0, 1). Finally, we also demonstrate an inverse relation between these two statistics: the clique number is essentially the reciprocal of the global clustering coefficient.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Staircases of passive and active scalar concentration in cellular flow

This paper develops a unified model for staircase formation in both passive and active scalar systems, building upon prior numerical studies by offering new heuristic and physical insights. While prior studies primarily reported numerical results, they did not explore the underlying unifying physics that governs both types of scalar transport; this work addresses that gap by identifying shared mechanisms across both cases. Results of studies of passive and active scalar staircase formation in cellular flows are presented. Staircase formation in cellular flows occurs due to the interplay of fast mixing within cells and slow transport across the inter-cell boundary. The cell boundary emerges as a de facto transport barrier. Special attention is focused on the effects of cellular fluctuations and noise upon staircase structure. A forced, fluctuating vortex array model is used to drive the underlying flow structure. Cellular Peclet number and staircase profile curvature are identified as figures-of-merit to quantify the resiliency of layering. These are related to simple, multi-scatterer scalar random walk models. Results for Peclet number and curvature scaling with flow excitation are presented. We also study staircases of magnetic potential evolving in two-dimensional magnetohydrodynamics as examples of layering of active scalar concentration. Formation of magnetic potential staircases is indeed observed. Flux expulsion inhibits the intercellular transport of magnetic potential and strengthens staircase barriers. Magnetic staircases can be supported against resistive decay by magnetic potential noise forcing. Implications for staircase formation in magnetic confinement experiments are discussed.

Control theory↗
DOE OSTI2024

Quantum chaos on edge

Recently, the physics of many-body quantum chaotic systems close to their ground states has come under intensified scrutiny. Such studies are motivated by the emergence of model systems exhibiting chaotic fluctuations throughout the entire spectrum [the Sachdev-Ye-Kitaev (SYK) model being a renowned representative] as well as by the physics of holographic principles, which likewise unfold close to ground states. Interpreting the edge of the spectrum as a quantum critical point, here we combine a wide range of analytical and numerical methods to the identification and comprehensive description of two different universality classes: the near edge physics of “sparse” and the near edge of “dense” chaotic systems. The distinction lies in the ratio between the number of a system's random parameters and its Hilbert space dimension, which is exponentially small or algebraically small in the sparse and dense case, respectively. Notable representatives of the two classes are generic chaotic many-body models (sparse) and invariant random matrix ensembles or chaotic gravitational systems (dense). While the two families share identical spectral correlations at energy scales comparable to the level spacing, the density of states and its fluctuations near the edge are different. Considering the SYK model as a representative of the sparse class, we apply a combination of field theory and exact diagonalization to a detailed discussion of its edge spectrum. Conversely, Jackiw-Teitelboim gravity is our reference model for the dense class, where an analysis of the gravitational path integral and random matrix theory reveal universal differences to the sparse class, whose implications for the construction of holographic principles we discuss. Published by the American Physical Society 2024

Altland, Alexander (ORCID:0000000229914805)↗