Search NASA⌕ Search

SEARCH · Search NASA

Results for “Dynamic ensembles”

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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Situation awareness and dynamic ensemble forecasting of abnormal behavior in cyber-physical system

A plurality of monitoring nodes may each generate a time-series of current monitoring node values representing current operation of a cyber-physical system. A feature-based forecasting framework may receive the time-series of and generate a set of current feature vectors using feature discovery techniques. The feature behavior for each monitoring node may be characterized in the form of decision boundaries that separate normal and abnormal space based on operating data of the system. A set of ensemble state-space models may be constructed to represent feature evolution in the time-domain, wherein the forecasted outputs from the set of ensemble state-space models comprise anticipated time evolution of features. The framework may then obtain an overall features forecast through dynamic ensemble averaging and compare the overall features forecast to a threshold to generate an estimate associated with at least one feature vector crossing an associated decision boundary.

Abbaszadeh, Masoud↗
DOE OSTI2021

libEnsemble: A Library to Coordinate the Concurrent Evaluation of Dynamic Ensembles of Calculations

Almost all applications stop scaling at some point; those that don't are seldom performant when considering time to solution on anything but aspirational/unicorn resources. Recognizing these tradeoffs as well as greater user functionality in a near-term exascale computing era, we present libEnsemble, a library aimed at particular scalability- and capability-stretching uses. libEnsemble enables running concurrent instances of an application in dynamically allocated ensembles through an extensible Python library. Here, we highlight the structure, execution, and capabilities of the library on leading pre-exascale environments as well as advanced capabilities for exascale environments and beyond.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

libEnsemble: A complete Python toolkit for dynamic ensembles of calculations

Almost all science and engineering applications eventually stop scaling: their runtime no longer decreases as available computational resources increase. Therefore, many applications will struggle to efficiently use emerging extreme-scale high-performance, parallel, and distributed systems. libEnsemble is a complete Python toolkit and workflow system for intelligently driving ensembles of experiments or simulations at massive scales. It enables and encourages multidisciplinary design, decision, and inference studies portably running on laptops, clusters, and supercomputers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Porting Adaptive Ensemble Molecular Dynamics Workflows to the Summit Supercomputer

Molecular dynamics (MD) simulations must take very small (femtosecond) integration steps in simulation-time to avoid numerical errors. Efficient use of parallel programming models and accelerators in state-of-the art MD programs now is pushing Moore’s limit for time-per-MD step. As a result, directly simulating timescales beyond milliseconds will not be attainable directly, even at exascale. However, concepts from statistical physics can be used to combine many parallel simulations to provide information about longer timescales and to adequately sample the simulation space, while preserving details about the dynamics of the system. Implementing such an approach requires a workflow program that allows adaptable steering of task assignments based on extensive statistical analysis of intermediate results. Here we report the implementation of such an adaptable workflow program to drive simulations on the Summit IBM Power System AC922, a pre-exascale supercomputer at the Oak Ridge Leadership Computing Facility (OLCF). We compare to experiences on Titan, Summit’s predecessor, report the performance of the workflow and its components, and describe the porting process. We find that using a workflow program managed by a Mongo database can provide the fault tolerance, scalable performance, task dispatch rate, and reconfigurability required for robust and portable implementation of ensemble simulations such as are used in enhanced-sampling molecular dynamics. This type of workflow generator can also be used to provide adaptive steering of ensemble simulations for other applications in addition to MD.

Ossyra, John R.↗
DOE OSTI2022

Generative Ensemble Regression: Learning Particle Dynamics from Observations of Ensembles with Physics-informed Deep Generative Models

Here, we propose a new method for inferring the governing stochastic ordinary differential equations (SODEs) by observing particle ensembles at discrete and sparse time instants, i.e., multiple “snapshots.” Particle coordinates at a single time instant, possibly noisy or truncated, are recorded in each snapshot but are unpaired across the snapshots. By training a physics-informed generative model that generates “fake” sample paths, we aim to fit the observed particle ensemble distributions with a curve in the probability measure space, which is induced from the inferred particle dynamics. We employ different metrics to quantify the differences between distributions, e.g., the sliced Wasserstein distances and the adversarial losses in generative adversarial networks. We refer to this method as generative “ensemble-regression” (GER), in analogy to the classic “point-regression,” where we infer the dynamics by performing regression in the Euclidean space. We illustrate the GER by learning the drift and diffusion terms of particle ensembles governed by SODEs with Brownian motions and Lévy processes up to 100 dimensions. We also discuss how to treat cases with noisy or truncated observations. Apart from systems consisting of independent particles, we also tackle nonlocal interacting particle systems with unknown interaction potential parameters by constructing a physics-informed loss function. Finally, we investigate scenarios of paired observations and discuss how to reduce the dimensionality in such cases by proving a convergence theorem that provides theoretical support.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Trajectory Ensemble Methods Provide Single-Molecule Statistics for Quantum Dynamical Systems

The emergence of experiments capable of probing quantum dynamics at the single-molecule level requires the development of new theoretical tools capable of simulating and analyzing these dynamics beyond an ensemble-averaged description. In this article, we present an efficient method for sampling and simulating the dynamics of the individual quantum systems that make up an ensemble and apply it to study the nonequilibrium dynamics of the ubiquitous spin-boson model. We generate an ensemble of single-system trajectories, and we analyze this trajectory ensemble using tools from classical statistical mechanics. Our results demonstrate that the dynamics of quantum coherence is highly heterogeneous at the single-system level due to variations in the initial bath configuration, which significantly affects the transient exchange of coherence between the system and its bath. Here, we observe that single systems tend to retain coherence over time scales longer than that of the ensemble. We also compute a novel thermodynamic entanglement entropy that quantifies a thermodynamic driving force favoring system–bath entanglement.

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

Direct observation of tRNA-chaperoned folding of a dynamic mRNA ensemble

T-box riboswitches are multi-domain noncoding RNAs that surveil individual amino acid availabilities in most Gram-positive bacteria. T-boxes directly bind specific tRNAs, query their aminoacylation status to detect starvation, and feedback control the transcription or translation of downstream amino-acid metabolic genes. Most T-boxes rapidly recruit their cognate tRNA ligands through an intricate three-way stem I-stem II-tRNA interaction, whose establishment is not understood. Using single-molecule FRET, SAXS, and time-resolved fluorescence, we find that the free T-box RNA assumes a broad distribution of open, semi-open, and closed conformations that only slowly interconvert. tRNA directly binds all three conformers with distinct kinetics, triggers nearly instantaneous collapses of the open conformations, and returns the T-box RNA to their pre-binding conformations upon dissociation. This scissors-like dynamic behavior is enabled by a hinge-like pseudoknot domain which poises the T-box for rapid tRNA-induced domain closure. This study reveals tRNA-chaperoned folding of flexible, multi-domain mRNAs through a Venus flytrap-like mechanism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

The multi-assumption architecture and testbed (MAAT v1.0): R code for generating ensembles with dynamic model structure and analysis of epistemic uncertainty from multiple sources

Abstract. Here, computer models are ubiquitous tools used to represent systems across many scientific and engineering domains. For any given system, many computer models exist, each built on different assumptions and demonstrating variability in the ways in which these systems can be represented. This variability is known as epistemic uncertainty, i.e. uncertainty in our knowledge of how these systems operate. Two primary sources of epistemic uncertainty are (1) uncertain parameter values and (2) uncertain mathematical representations of the processes that comprise the system. Many formal methods exist to analyse parameter-based epistemic uncertainty, while process-representation-based epistemic uncertainty is often analysed post hoc, incompletely, informally, or is ignored. In this model description paper we present the multi-assumption architecture and testbed (MAAT v1.0) designed to formally and completely analyse process-representation-based epistemic uncertainty. MAAT is a modular modelling code that can simply and efficiently vary model structure (process representation), allowing for the generation and running of large model ensembles that vary in process representation, parameters, parameter values, and environmental conditions during a single execution of the code. MAAT v1.0 approaches epistemic uncertainty through sensitivity analysis, assigning variability in model output to processes (process representation and parameters) or to individual parameters. In this model description paper we describe MAAT and, by using a simple groundwater model example, verify that the sensitivity analysis algorithms have been correctly implemented. The main system model currently coded in MAAT is a unified, leaf-scale enzyme kinetic model of C 3 photosynthesis. In the Appendix we describe the photosynthesis model and the unification of multiple representations of photosynthetic processes. The numerical solution to leaf-scale photosynthesis is verified and examples of process variability in temperature response functions are provided. For rapid application to new systems, the MAAT algorithms for efficient variation of model structure and sensitivity analysis are agnostic of the specific system model employed. Therefore MAAT provides a tool for the development of novel or toy models in many domains, i.e. not only photosynthesis, facilitating rapid informal and formal comparison of alternative modelling approaches.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Portable, heterogeneous ensemble workflows at scale using libEnsemble

libEnsemble is a Python-based toolkit for running dynamic ensembles, developed as part of the DOE Exascale Computing Project. The toolkit utilizes a unique generator–simulator–allocator paradigm, where generators produce input for simulators, simulators evaluate those inputs, and allocators decide whether and when a simulator or generator should be called. The generator steers the ensemble based on simulation results. Generators may, for example, apply methods for numerical optimization, machine learning, or statistical calibration. libEnsemble communicates between a manager and workers. Flexibility is provided through multiple manager–worker communication substrates each of which has different benefits. These include Python’s multiprocessing, mpi4py, and TCP. Multisite ensembles are supported using Balsam or Globus Compute. We overview the unique characteristics of libEnsemble as well as current and potential interoperability with other packages in the workflow ecosystem. We highlight libEnsemble’s dynamic resource features: libEnsemble can detect system resources, such as available nodes, cores, and GPUs, and assign these in a portable way. These features allow users to specify the number of processors and GPUs required for each simulation; and resources will be automatically assigned on a wide range of systems, including Frontier, Aurora, and Perlmutter. Such ensembles can include multiple simulation types, some using GPUs and others using only CPUs, sharing nodes for maximum efficiency. We also describe the benefits of libEnsemble’s generator–simulator coupling, which easily exposes to the user the ability to cancel, and portably kill, running simulations based on models that are updated with intermediate simulation output. We demonstrate libEnsemble’s capabilities, scalability, and scientific impact via a Gaussian process surrogate training problem for the longitudinal density profile at the exit of a plasma accelerator stage. In conclusion, the study uses gpCAM for the surrogate model and employs either Wake-T or WarpX simulations, highlighting efficient use of resources that can easily extend to exascale.

Dynamic ensembles↗
DOE OSTI2024

Unlocking the unfolded structure of ubiquitin: Combining time-resolved x-ray solution scattering and molecular dynamics to generate unfolded ensembles

The unfolding dynamics of ubiquitin were studied using a combination of x-ray solution scattering (XSS) and molecular dynamics (MD) simulations. The kinetic analysis of the XSS ubiquitin signals showed that the protein unfolds through a two-state process, independent of the presence of destabilizing salts. In order to characterize the ensemble of unfolded states in atomic detail, the experimental XSS results were used as a constraint in the MD simulations through the incorporation of x-ray scattering derived potential to drive the folded ubiquitin structure toward sampling unfolded states consistent with the XSS signals. We detail how biased MD simulations provide insight into unfolded states that are otherwise difficult to resolve and underscore how experimental XSS data can be combined with MD to efficiently sample structures away from the native state. Our results indicate that ubiquitin samples unfolded in states with a high degree of loss in secondary structure yet without a collapse to a molten globule or fully solvated extended chain. Finally, we propose how using biased-MD can significantly decrease the computational time and resources required to sample experimentally relevant nonequilibrium states.

Chemistry↗
DOE OSTI2018

Forecasting and Early Warning for Adversarial Targeting in Industrial Control Systems

A new feature-based forecasting framework of abnormalities is presented for early warning for cyber-physical control systems. The abnormalities may refer to intelligent cyber-attacks or naturally occurring faults and failures. Techniques presented here are aimed at protecting against unauthorized intrusions as well as fault prevention. Time series signals from system monitoring nodes are converted to features using feature discovery techniques. The feature behavior for each monitoring node is characterized in the form of decision boundaries, separating normal and abnormal space with operating data collected from the plant or by running virtual models of the plant. A set of ensemble state-space models are constructed for representing feature evolution in time-domain, where the ensembles are selected using Gaussian Mixture Model (GMM) clustering. The forecasted outputs are anticipated time-evolution of features, computed by applying an adaptive Kalman predictor to each ensemble model. The overall features forecast is then obtained through dynamic ensemble averaging. This is done by projecting evolution of feature vector to future times in a receding horizon fashion. The features forecast are compared to the decision boundary to estimate if/when the feature vectors will cross the boundary. Simulation results in a high fidelity GE gas turbine platform, show the effectiveness of our approach for forecasting abnormalities.

42 ENGINEERING↗
DOE OSTI2025

Early events in G-quadruplex folding captured by time-resolved small-angle X-ray scattering

Abstract Time-resolved small-angle X-ray experiments are reported here that capture and quantify a previously unknown rapid collapse of the unfolded oligonucleotide as an early step in the folding of hybrid 1 and hybrid 2 telomeric G-quadruplex structures. The rapid collapse, initiated by a pH jump, is characterized by an exponential decrease in the radius of gyration from 24.3 to 12.6 Å. The collapse is monophasic and is complete in <600 ms. Additional hand-mixing pH-jump kinetic studies show that slower kinetic steps follow the collapse. The folded and unfolded states at equilibrium were further characterized by SAXS studies and other biophysical tools, showing that G4 unfolding was complete at alkaline pH, but not in LiCl solution as is often claimed. The SAXS Ensemble Optimization Method analysis reveals models of the unfolded state as a dynamic ensemble of flexible oligonucleotide chains with a variety of transient hairpin structures. These results suggest a G4 folding pathway in which a rapid collapse, analogous to molten globule formation seen in proteins, is followed by a confined conformational search within the collapsed particle to form the native contacts ultimately found in the stable folded form.

Biochemistry & Molecular Biology↗
DOE OSTI2023

Causes and evolution of winter polynyas north of Greenland

During the 42-year period (1979–2020) of satellite measurements, four major winter (December–March) polynyas have been observed north of Greenland: one in December 1986 and three in the last decade, i.e., February of 2011, 2017, and 2018. The 2018 polynya was unparalleled in its magnitude and duration compared to the three previous events. Given the apparent recent increase in the occurrence of these extreme events, this study aims to examine their evolution and causality, in terms of forced versus natural variability. The limited weather station and remotely sensed sea ice data are analyzed combining with output from the fully coupled Regional Arctic System Model (RASM), including one hindcast and two ensemble simulations. We found that neither the accompanying anomalous warm surface air intrusion nor the ocean below had an impact (i.e., no significant ice melting) on the evolution of the observed winter open-water episodes in the region. Instead, the extreme atmospheric wind forcing resulted in greater sea ice deformation and transport offshore, accounting for the majority of sea ice loss in all four polynyas. Our analysis suggests that strong southerly winds (i.e., northward wind with speeds greater than 10 m s –1 ) blowing persistently over the study region for at least 2 d or more were required over the study region to mechanically redistribute some of the thickest Arctic sea ice out of the region and thus to create open-water areas (i.e., a latent heat polynya). To assess the role of internal variability versus external forcing of such events, we carried out and examined results from the two RASM ensembles dynamically downscaled with output from the Community Earth System Model (CESM) Decadal Prediction Large Ensemble (DPLE) simulations. Out of 100 winters in each of the two ensembles (initialized 30 years apart: one in December 1985 and another in December 2015), 17 and 16 winter polynyas were produced north of Greenland, respectively. The frequency of polynya occurrence had no apparent sensitivity to the initial sea ice thickness in the study area pointing to internal variability of atmospheric forcing as a dominant cause of winter polynyas north of Greenland. We assert that dynamical downscaling using a high-resolution regional climate model offers a robust tool for process-level examination in space and time, synthesis with limited observations, and probabilistic forecasts of Arctic events, such as the ones being investigated here and elsewhere.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Reconfigurable structure and tunable transport in synchronized active spinner materials

Ensembles of actuated colloids are excellent model systems to explore emergent out-of-equilibrium structures, complex collective dynamics, and design rules for the next generation materials. Here, we demonstrate that ferromagnetic microparticles suspended at an air-water interface and energized by an external rotating magnetic field spontaneously form dynamic ensembles of synchronized spinners in a certain range of the excitation field parameters. Each spinner generates strong hydrodynamic flows, and collective interactions of the multiple spinners promote a formation of dynamic lattices. On the basis of experiments and simulations, we reveal structural transitions from liquid to nearly crystalline states in this novel active spinner material and demonstrate that dynamic spinner lattices are reconfigurable, capable of self-healing behavior and that the transport of embedded inert cargo particles can be remotely tuned by the parameters of the external excitation field. Our findings provide insights into the behavior of active spinner materials with reconfigurable structural order and tunable functionalities.

36 MATERIALS SCIENCE↗