Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “ensemble 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 235 records · Page 13

Enhancing Automotive Intrusion Detection Through Multi-Modal Fusion: A CAN FD-LiDAR Approach

As vehicles become smarter and more autonomous, they increasingly depend on advanced sensors and communication technologies to operate securely. However, such growing dependence on technology—whether it’s CAN (Controller Area Network) for internal communication or LiDAR (Light Detection and Ranging) for sensing the world around them—also expands the attack surface for the types of cyber attacks. Traditional intrusion detection systems (IDS) typically monitor these systems in isolation, limiting their ability to detect sophisticated, crosssystem attacks. To address this, we propose a multi-modal fusion approach that combines real-world CAN FD signals (from the HCRL dataset) with LiDAR features (from the nuScenes dataset) to enhance attack detection. Our method employs a twostage ensemble approach. Calibrated XGBoost and LightGBM models initially process CAN FD (Fuzzing Data) and LiDAR data independently, detecting timing anomalies and space abnormalities. They are subsequently logarithmically combined with a logistic regression meta-model along with 17 engineered features capturing cross-modal behavior, prediction conflicts, and nonlinear interactions. This approach achieves an AUC of 0.87 and an F1-score of 0.82, surpassing single-modality baselines and early fusion methods, at merely 2 ms inference latency. Compared with deep learning competitors, it is 3 times more efficient, providing a lightweight, interpretable, and real time solution to automotive cybersecurity.

97 MATHEMATICS AND COMPUTING↗

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↗

Designing robust energy policy packages under deep uncertainty: A multi-metric decision support framework

The complexity of transitioning to sustainable energy systems requires policy frameworks capable of balancing multiple objectives while addressing deep uncertainty. However, existing approaches often lack systematic methods to identify combinations of policy levers that remain effective across a wide range of uncertain futures. This paper presents a novel decision support framework that guides the selection of robust policy packages based on their performance across multiple objectives under uncertainty. Our method leverages a large ensemble of scenarios and applies scenario discovery techniques to identify influential policy levers. Here, we introduce new indicators to assess the robustness of policies by evaluating their ability to mitigate adverse outcomes across metrics. These indicators support an iterative process to build a robust policy package. Finally, we map the technological and energy pathways associated with the robust policy package by leveraging an energy system optimization model. We illustrate the application of this framework to the Spanish energy system, providing insights into how specific combinations of policy levers shape decarbonization pathways under uncertainty.

Decision-support method↗

Performance assessment of ensembles of in situ workflows under resource constraints

Summary Scientific breakthroughs in biomolecular methods and improvements in hardware technology have shifted from a long‐running simulation to a large set of shorter simulations running simultaneously, called an ensemble. In an ensemble, simulations are usually coupled with analyses of data produced by the simulations. In situ methods can be used to analyze large volumes of data generated by scientific simulations at runtime (i.e., simulations and analyses are performed concurrently). In this work, we study the execution of ensemble‐based simulations paired with in situ analyses using in‐memory staging methods. Using an ensemble of molecular dynamics in situ workflows with multiple simulations and analyses, we first show that collecting traditional metrics such as makespan, instructions per cycle, memory usage, or cache miss ratio is not sufficient to characterize complex behaviors of ensembles. We propose a method to evaluate the performance of ensembles of workflows that captures multiple resource usage aspects: resource efficiency, resource allocation, and resource provisioning. Experimental results demonstrate that the proposed method can effectively distinguish the performance of different component placements in an ensemble with up to 32 ensemble members. By evaluating different co‐location scenarios, our proposed performance indicators demonstrate benefits of co‐locating simulation and coupled analyses within a compute node.

Do, Tu Mai Anh↗

A deep learning-accelerated data assimilation and forecasting workflow for commercial-scale geologic carbon storage

Fast assimilation of monitoring data to update forecasts of pressure buildup and carbon dioxide (CO 2 ) plume migration under geologic uncertainties is a challenging problem in geologic carbon storage. The high computational cost of data assimilation with a high-dimensional parameter space impedes fast decision-making for commercial-scale reservoir management. We propose to leverage physical understandings of porous medium flow behavior with deep learning techniques to develop a fast data assimilation-reservoir response forecasting workflow. Applying an Ensemble Smoother Multiple Data Assimilation (ES-MDA) framework, the workflow updates geologic properties and predicts reservoir performance with quantified uncertainty from pressure history and CO 2 plumes interpreted through seismic inversion. As the most computationally expensive component in such a workflow is reservoir simulation, we developed surrogate models to predict dynamic pressure and CO 2 plume extents under multi-well injection. The surrogate models employ deep convolutional neural networks, specifically, a wide residual network and a residual U-Net. The workflow is validated against a flat threedimensional reservoir model representative of a clastic shelf depositional environment. Intelligent treatments are applied to bridge between quantities in a true-3D reservoir model and those in a single-layer reservoir model. The workflow can complete history matching and reservoir forecasting with uncertainty quantification in less than one hour on a mainstream personal workstation.

25 ENERGY STORAGE↗

Improving Prediction of Surface Solar Irradiance Variability by Integrating Observed Cloud Characteristics and Machine Learning

A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was produced that includes two metrics of the variability in surface solar irradiance due to cloud type and fractional sky cover. Multiple regression models were trained to fit observations of surface solar irradiance variability from those two cloud property predictors. We found that ensemble tree-based methods, Random Forest and Gradient Boosting Machine, have the least overfitting issues and showed the best performance with an R2 of 0.42. While the observational data trained in this study was only from one site, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, initial comparisons of the seasonality of the statistics suggest that these results are relatively weather regime independent; the generality of such a finding across sites will be tested in future work. The observational data and developed machine learning model are being used to create a numerical weather prediction model parameterization to enable day-ahead solar variability prediction in a computationally efficient way. This is a first step towards creating a new paradigm of predicting day-ahead variability with the potential to provide a new tool to improve grid operation, planning, and resilience.

Riihimaki, Laura↗

Machine Learning of Key Variables Impacting Extreme Precipitation in Various Regions of the Contiguous United States

Abstract Amplification in extreme precipitation intensity and frequency can cause severe flooding and impose significant social and economic consequences. Variations in extreme precipitation intensity, frequencies, and return periods can be attributed to many physical variables across spatial and temporal scales. Here we employ ensemble machine learning (ML) methods, namely random forest (RF), eXtreme Gradient Boosting (XGB), and artificial neural networks (ANN), to explore key contributing variables to monthly extreme precipitation intensity and frequency in six regions over the United States. We further establish emulators for return periods. Results show that the ML models for intensity perform better in regions with obvious seasonality (i.e., Northern Great Plains, Southern Great Plains, and West Coast) than the other three regions (Northeast, Southwest, and Rocky Mountains), while for frequency the models perform well for most regions. The Shapley additive explanation is used to help explain the relationships between extreme precipitation characteristics and identify top variables for RF and XGB. We find that latent heat flux, relative humidity, soil moisture, and large‐scale subsidence are key common variables across the regions for both monthly intensity and frequency, and their compound effects are non‐negligible. The developed ML models capture the probability and return period of extreme precipitation well for all regions and may be used for decision making (e.g., infrastructure planning and design).

54 ENVIRONMENTAL SCIENCES↗

Fluctuations in heavy ion collisions and global conservation effects

Subensemble is a type of statistical ensemble which is the generalization of grand canonical and canonical ensembles. The subensemble acceptance method (SAM) provides general formulas to correct the cumulants of distributions in heavy-ion collisions for the global conservation of all QCD charges. The method is applicable for an arbitrary equation of state and sufficiently large systems, such as those created in central collisions of heavy ions. The new fluctuation measures insensitive to global conservation effects are presented. The main results are illustrated in the hadron resonance gas and van der Waals fluid frameworks.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Antarctic ice sheet model comparison with uncurated geological constraints shows that higher spatial resolution improves deglacial reconstructions

Accurately reconstructing past changes to the shape and volume of the Antarctic ice sheet relies on the use of physically based and thus internally consistent ice sheet modeling, benchmarked against spatially limited geologic data. The challenge in model benchmarking against geologic data is diagnosing whether model-data misfits are the result of an inadequate model, inherently noisy or biased geologic data, and/or incorrect association between modeled quantities and geologic observations. In this work we address this challenge by (i) the development and use of a new model-data evaluation framework applied to an uncurated data set of geologic constraints, and (ii) nested high-spatial-resolution modeling designed to test the hypothesis that model resolution is an important limitation in matching geologic data. While previous approaches to model benchmarking employed highly curated datasets, our approach applies an automated screening and quality control algorithm to an uncurated public dataset of geochronological observations (specifically, cosmogenic-nuclide exposure-age measurements from glacial deposits in ice-free areas). This optimizes data utilization by including more geological constraints, reduces potential interpretive bias, and allows unsupervised assimilation of new data as they are collected. We also incorporate a nested model framework in which high-resolution domains are downscaled from a continent-wide ice sheet model. We highlight the application of this framework by applying these methods to a small ensemble of deglacial ice-sheet model simulations, and demonstrate that the nested approach improves the ability of model simulations to match exposure age data collected from areas of complex topography and ice flow. We develop a range of diagnostic model-data comparison metrics to provide more insight into model performance than possible from a single-valued misfit statistic, showing that different metrics capture different aspects of ice sheet deflation.

Geosciences↗

Analyses of Cometary Silicate Crystals: DDA Spectral Modeling of Forsterite

Comets are the Solar System's deep freezers of gases, ices, and particulates that were present in the outer protoplanetary disk. Where comet nuclei accreted was so cold that CO ice (approximately 50K) and other supervolatile ices like ethane (C2H2) were preserved. However, comets also accreted high temperature minerals: silicate crystals that either condensed (greater than or equal to 1400 K) or that were annealed from amorphous (glassy) silicates (greater than 850-1000 K). By their rarity in the interstellar medium, cometary crystalline silicates are thought to be grains that formed in the inner disk and were then radially transported out to the cold and ice-rich regimes near Neptune. The questions that comets can potentially address are: How fast, how far, and over what duration were crystals that formed in the inner disk transported out to the comet-forming region(s)? In comets, the mass fractions of silicates that are crystalline, f_cryst, translate to benchmarks for protoplanetary disk radial transport models. The infamous comet Hale-Bopp has crystalline fractions of over 55%. The values for cometary crystalline mass fractions, however, are derived assuming that the mineralogy assessed for the submicron to micron-sized portion of the size distribution represents the compositional makeup of all larger grains in the coma. Models for fitting cometary SEDs make this assumption because models can only fit the observed features with submicron to micron-sized discrete crystals. On the other hand, larger (0.1-100 micrometer radii) porous grains composed of amorphous silicates and amorphous carbon can be easily computed with mixed medium theory wherein vacuum mixed into a spherical particle mimics a porous aggregate. If crystalline silicates are mixed in, the models completely fail to match the observations. Moreover, models for a size distribution of discrete crystalline forsterite grains commonly employs the CDE computational method for ellipsoidal platelets (c:a:b=8.14x8.14xl in shape with geometrical factors of x:y:z=1:1:10, Fabian et al. 2001; Harker et al. 2007). Alternatively, models for forsterite employ statistical methods like the Distribution of Hollow Spheres (Min et al. 2008; Oliveira et al. 2011) or Gaussian Random Spheres (GRS) or RGF (Gielen et al. 200S). Pancakes, hollow spheres, or GRS shapes similar to wheat sheaf crystal habit (e.g., Volten et al. 2001; Veihelmann et al. 2006), however, do not have the sharp edges, flat faces, and vertices seen in images of cometary crystals in interplanetary dust particles (IDPs) or in Stardust samples. Cometary forsterite crystals often have equant or tabular crystal habit (J. Bradley). To simulate cometary crystals, we have computed absorption efficiencies of forsterite using the Discrete Dipole Approximation (DDA) DDSCAT code on NAS supercomputers. We compute thermal models that employ a size distribution of discrete irregularly shaped forsterite crystals (nonspherical shapes with faces and vertices) to explore how crystal shape affects the shape and wavelength positions of the forsterite spectral features and to explore whether cometary crystal shapes support either condensation or annealing scenarios (Lindsay et al. 2012a, b). We find forsterite crystal shapes that best-fit comet Hale-Bopp are tetrahedron, bricks or brick platelets, essentially equant or tabular (Lindsay et al. 2012a,b), commensurate with high temperature condensation experiments (Kobatake et al. 2008). We also have computed porous aggregates with crystal monomers and find that the crystal resonances are amplified. i.e., the crystalline fraction is lower in the aggregate than is derived by fitting a linear mix of spectral features from discrete subcomponents, and the crystal resonances 'appear' to be from larger crystals (Wooden et al. 2012). These results may indicate that the crystalline mass fraction in comets with comae dominated by aggregates may be lower than deduced by popular methods that only emoy ensembles of discrete crystals.

Wooden, Diane↗

The 27-Day Rotational Variations in Total Solar Irradiance Observations: from SORCE-TIM, ACRIMSAT-ACRIM III, and SOHO-VIRGO

During the last decade, observations from SORCE (Solar Radiation and Climate Experiment)/TIM (Total Irradiance Monitor), ACRIMSAT (Active Cavity Radiometer Irradiance Monitor Satellite)/ACRIM III, and SOHO (Solar and Heliospheric Observatory)VIRGO (Variability of IRradiance and Gravity Oscillations Sun PhotoMeter) provided the Total Solar Irradiance (TSI) measurements with unprecedented accuracy and stability to determine the amount of solar irradiance reaching the top of the atmosphere and how solar irradiance varies in different time scales. These three independent measurements are analyzed using the EEMD (Ensemble Empirical Mode Decomposition) method to characterize the phase and amplitude of 27-day solar rotational variation in TSI. The mode decomposition clearly identifies a 27-day solar rotational signature in TSI measurements. The rotational variations of TSI from the three independent observations are generally consistent with each other, despite different mean TSI values. During the declining phase of solar cycle 23, the amplitude of TSI 27-day variations is as high as 0.8 watts per square meter (approximately 0.05 percent), while during the rising phase of solar cycle 24, the amplitude is up to 0.4 watts per square meter (approximately 0.04 percent). During the minimum phase (2008-2009), the amplitude of the rotational mode is only 0.1 watts per square meter. The correlation of this rotational mode between TIM and ACRIM III is approximately 0.92 and the slope of the local peak values is approximately 0.98. The correlation between TIM and VIRGO is approximately 0.96 and the slope of the local peak values isapproximately 0.98, very similar to the slope with ACRIM III.

ACRIM III↗

Global Evolution of Solar Magnetic Fields and Prediction of Solar Activity Cycles

Prediction of solar activity cycles is challenging because the physical processes inside the Sun involve a broad range of multiscale dynamics that no model can reproduce, and the available observations are highly limited and cover mostly surface layers. Helioseismology makes it possible to probe solar dynamics in the convective zone, but variations in the differential rotation and meridional circulation are currently available for only two solar activity cycles. It has been demonstrated that sunspot observations, which cover over 400 years, can be used to calibrate the Parker-Kleeorin-Ruzmaikin model and that the Ensemble Kalman Filter (EnKF) method can be used to link the model magnetic fields to sunspot observations to make reliable predictions of a following cycle. However, for more accurate predictions, it is necessary to use actual observations of the solar magnetic fields, which are available for only four solar cycles. This raises the question of how limitations in observational data and model uncertainties affect predictive capabilities and implies the need for the development of new forecast methodologies and validation criteria. In this presentation, I will discuss the influence of the limited number of available observations on the accuracy of EnKF estimates of solar cycle parameters.

Kitiashvili, Irina N.↗

Application of Synoptic Magnetograms for Prediction of Solar Activity Using Ensemble Kalman Filter

Solar activity predictions using the data assimilation approach have demonstrated great potential to build reliable long-term forecasts of solar activity. In particular, it has been shown that the Ensemble Kalman Filter (EnKF) method applied to a non-linear dynamo model is capable of predicting solar activity up to one sunspot cycle ahead in time, as well as estimating the properties of the next cycle a few years before it begins. These developments assume an empirical relationship between the mean toroidal magnetic field flux and the sunspot number. Estimated from the sunspot number series, variations of the toroidal field have been used to assimilate the data into the Parker-Kleeorin-Ruzmakin (PKR) dynamo model by applying the EnKF method. The dynamo model describes the evolution of the toroidal and poloidal components of the magnetic field and the magnetic helicity. Full-disk magnetograms provide more accurate and complete input data by constraining both the toroidal and poloidal global field components, but these data are available only for the last four solar cycles. In this presentation, using the available magnetogram data, we discuss development of the methodology and forecast quality criteria (including forecast uncertainties and sources of errors). We demonstrate the influence of limited time series observations on the accuracy of solar activity predictions. We present EnKF predictions of the upcoming Solar Cycle 25 based on both the sunspot number series and observed magnetic fields and discuss the uncertainties and potential of the data assimilation approach.

Kitiashvili, Irina N.↗

Solar Activity Modeling: From Subgranular Dynamical Scales to the Solar Cycles

Dynamical effects of solar magnetoconvection span a wide range spatial and temporal scales that extends from the interior to the corona and from fast turbulent motions to the global-Sun magnetic activity. To study the solar activity on short temporal scales (from minutes to hours), we use 3D radiative MHD simulations that allow us to investigate complex turbulent interactions that drive various phenomena, such as plasma eruptions, spontaneous formation of magnetic structures, funnel-like structures and magnetic loops in the corona, and others. In particular, we focus on multi-scale processes of energy exchange across the different layers, which contribute to the corona heating and eruptive dynamics, as well as interlinks between different layers of the solar interior and atmosphere. For modeling the global-scale activity we use the data assimilation approach that has demonstrated great potential for building reliable long-term forecasts of solar activity. In particular, it has been shown that the Ensemble Kalman Filter (EnKF) method applied to the Parker-Kleeorin-Ruzmakin dynamo model is capable of predicting solar activity up to one sunspot cycle ahead in time, as well as estimating the properties of the next cycle a few years before it begins. In this presentation, using the available magnetogram data, we discuss development of the methodology and forecast quality criteria (including forecast uncertainties and sources of errors). We demonstrate the influence of observational limitation on the prediction accuracy. We present the EnKF predictions of the upcoming Solar Cycle 25 based on both the sunspot number series and observed magnetic fields, and discuss the uncertainties and potential of the data assimilation approach for modeling and forecasting the solar activity.

Kitiashvili, I. N.↗

Solar Activity Modeling: From Subgranular Dynamical Scales to the Solar Cycles

The dynamical effects of solar magnetoconvection span a wide range spatial and temporal scales that extend from the interior to the corona and from fast turbulent motions to global magnetic activity. To study the solar activity on short temporal scales (from minutes to hours), we use 3D radiative MHD simulations that allow us to investigate complex turbulent interactions that drive various phenomena, such as plasma eruptions, spontaneous formation of magnetic structures, funnel-like structures and magnetic loops in the corona, and others. In particular, we focus on multi-scale processes of energy exchange across layers of the solar interior and atmosphere, which contribute to coronal heating and eruptive dynamics. For modeling global-scale activity, we use a data assimilation approach that has demonstrated great potential for building reliable long-term forecasts of solar activity. In particular, it has been shown that the Ensemble Kalman Filter (EnKF) method applied to the Parker-Kleeorin-Ruzmakin dynamo model is capable of predicting solar activity up to one sunspot cycle ahead in time, as well as estimating the properties of the next cycle a few years before it begins. In this presentation, using the available magnetogram data, we discuss development of the methodology and forecast quality criteria (including forecast uncertainties and sources of errors). We demonstrate the influence of observational limitations on prediction accuracy, and we present the EnKF predictions of the upcoming Solar Cycle (25) based on both the sunspot number series and observed magnetic fields and discuss the uncertainties and potential of the data assimilation approach for modeling and forecasting solar activity.

Kitiashvili, I. N.↗

Measured and Modeled Vineyard Canopy Development and Water Use

Two publicly available applications relevant to vineyard irrigation management are described. OpenET is a satellite-based system that applies an ensemble of remote sensing methods to enable wide-area monitoring of evapotranspiration (ET) and related measures such as vegetation canopy development (via the NDVI spectral index). Data are freely available at one-quarter acre spatial resolution, and may be automatically aggregated to the individual block level. The satellite-based daily ET data were compared with in-situ eddy covariance measurements collected by micro-meteorological instrumentation in a Central Coast vineyard over a three-year period (2020-2022). Estimation uncertainties were reasonably consistent with prior reports for GRAPEX sites in the Central Valley and North Coast. The CropManage (CM) web application is a free software tool developed and operated U.C. Cooperative Extension for ET-based irrigation scheduling of major specialty crops. CM provides specific guidance for irrigation events in terms of irrigation system runtime. Applied water recommendations are based largely on estimated ET, derived from assumed canopy cover and associated crop coefficients, since the last irrigation or rainfall event. The application was recently adapted to vineyards by accounting for presence of winter/spring cover crop, and vine water stress imposed by regulated deficit irrigation. A 2022 field campaign involved 12 Central Coast and San Joaquin Valley commercial vineyards, and OpenET data were used to help evaluate CM output. Maximum percent vine cover was compared to estimates derived from average July satellite NDVI. The difference for 10 sites lacking midseason groundcover ranged from 0-7% between datasets with average agreement near 4%. Cumulative ET estimates agreed with OpenET to within 12% at the majority of sites, while larger discrepancies at the remaining sites may require additional data collection and analysis during the 2023 season. Satellite based systems such as OpenET have the potential to help parameterize CropManage and similar agricultural decision-support systems.

Measured↗

An Ensemble Score Filter for Tracking High-Dimensional Nonlinear Dynamical Systems

We propose an ensemble score filter (EnSF) for solving high-dimensional nonlinear filtering problems with superior accuracy. A major drawback of existing filtering methods, e.g., particle filters or ensemble Kalman filters, is the low accuracy in handling high-dimensional and highly nonlinear problems. EnSF attacks this challenge by exploiting the score-based diffusion model, defined in a pseudo-temporal domain, to characterizing the evolution of the filtering density. EnSF stores the information of the recursively updated filtering density function in the score function, in stead of storing the information in a set of finite Monte Carlo samples (used in particle filters and ensemble Kalman filters). Unlike existing diffusion models that train neural networks to approximate the score function, we develop a training-free score estimation that uses mini-batch-based Monte Carlo estimator to directly approximate the score function at any pseudo-spatial-temporal location, which provides sufficient accuracy in solving high-dimensional nonlinear problems as well as saves tremendous amount of time spent on training neural networks. Another essential aspect of EnSF is its analytical update step, gradually incorporating data information into the score function, which is crucial in mitigating the degeneracy issue faced when dealing with very high-dimensional nonlinear filtering problems. High-dimensional Lorenz systems are used to demonstrate the performance of our method. EnSF provides surprisingly impressive performance in reliably tracking extremely high-dimensional Lorenz systems (up to 1,000,000 dimension) with highly nonlinear observation processes, which is a well-known challenging problem for existing filtering methods.

97 MATHEMATICS AND COMPUTING↗

Random matrix theory of the isospectral twirling

We present a systematic construction of probes into the dynamics of isospectral ensembles of Hamiltonians by the notion of Isospectral twirling, expanding the scopes and methods of ref. [1]. The relevant ensembles of Hamiltonians are those defined by salient spectral probability distributions. The Gaussian Unitary Ensembles (GUE) describes a class of quantum chaotic Hamiltonians, while spectra corresponding to the Poisson and Gaussian Diagonal Ensemble (GDE) describe non chaotic, integrable dynamics. We compute the Isospectral twirling of several classes of important quantities in the analysis of quantum many-body systems: Frame potentials, Loschmidt Echos, OTOCs, Entanglement, Tripartite mutual information, coherence, distance to equilibrium states, work in quantum batteries and extension to CP-maps. Moreover, we perform averages in these ensembles by random matrix theory and show how these quantities clearly separate chaotic quantum dynamics from non chaotic ones.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗