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At least 145 records · Page 8

Probing proton cross-shell excitations through the two-neutron removal from 38 Ca

Bound states of the neutron-deficient, near-dripline nucleus 36 Ca were populated in two-neutron removal from the ground state of 38 Ca, a direct reaction sensitive to the single-particle configurations and couplings of the removed neutrons in the projectile wave function. Final-state exclusive cross sections for the formation of 36 Ca and the corresponding longitudinal momentum distributions, both determined through the combination of particle and γ-ray spectroscopy, are compared to predictions combining eikonal reaction theory and shell-model two-nucleon amplitudes from the USDB, USDC, and ZBM2 effective interactions. The final-state cross-section ratio σ($2$$^{+}_{1}$) / σ (0 + ) shows particular sensitivity and is approximately reproduced only with the two-nucleon amplitudes from the ZBM2 effective interaction that includes proton cross-shell excitations into the pf shell. Characterizing the proton pf-shell occupancy locally and schematically, an increase of the sd – pf shell gap by 250 keV yields an improved description of this cross-section ratio and simultaneously enables a reproduction of the B(E2;$0$$^{+}_{1}$ → $2$$^{+}_{1}$) excitation strength of 36 Ca. Furthermore, this highlights an important aspect if a new shell-model effective interaction for the region was to be developed on the quest to model the neutron-deficient Ca isotopes and surrounding nuclei whose structure is impacted by proton cross-shell excitations.

20 ≤ A ≤ 38↗

Laboratory Study of Magnetic Reconnection in Lunar-relevant Mini-magnetospheres

Abstract Mini-magnetospheres are small ion-scale structures that are well suited to studying kinetic-scale physics of collisionless space plasmas. Such ion-scale magnetospheres can be found on local regions of the Moon, associated with the lunar crustal magnetic field. In this paper, we report on the laboratory experimental study of magnetic reconnection in laser-driven, lunar-like ion-scale magnetospheres on the Large Plasma Device at the University of California, Los Angeles. In the experiment, a high-repetition rate (1 Hz), nanosecond laser is used to drive a fast-moving, collisionless plasma that expands into the field generated by a pulsed magnetic dipole embedded into a background plasma and magnetic field. The high-repetition rate enables the acquisition of time-resolved volumetric data of the magnetic and electric fields to characterize magnetic reconnection and calculate the reconnection rate. We notably observe the formation of Hall fields associated with reconnection. Particle-in-cell simulations reproducing the experimental results were performed to study the microphysics of the interaction. By analyzing the generalized Ohm’s law terms, we find that the electron-only reconnection is driven by kinetic effects through the electron pressure anisotropy. These results are compared to recent satellite measurements that found evidence of magnetic reconnection near the lunar surface.

Astronomy & Astrophysics↗

Characterizing x-ray transmission through filters used in high energy density physics diagnostics

We report on the design and implementation of a new system used to characterize the energy-dependent x-ray transmission curve, Θ(E), through filters used in high-energy density physics diagnostics. Using an Amptek X-123-CdTe x-ray spectrometer together with a partially depleted silicon surface barrier detector, both the energy spectrum and total emission of an x-ray source have been accurately measured. By coupling these detectors with a custom PROTO-XRD x-ray source with interchangeable cathodes, accurate characterizations of Θ(E) for filters of varying materials and thicknesses have been obtained. The validity of the technique has been confirmed by accurately reproducing areal densities for high-purity filters with known x-ray transmission properties. In this paper, the experimental setup is described and the results of absorption calibrations performed on a variety of different filters are presented.

Pearcy, J.↗

Operating advanced scientific instruments with AI agents that learn on the job

Advanced scientific user facilities, such as next generation X-ray light sources and self-driving laboratories, are revolutionizing scientific discovery by automating routine tasks and enabling rapid experimentation and characterizations. However, these facilities must continuously evolve to support new experimental workflows, adapt to diverse user projects, and meet growing demands for more intricate instruments and experiments. This continuous development introduces significant operational complexity, necessitating a focus on usability, reproducibility, and intuitive human-instrument interaction. In this work, we explore the integration of agentic AI, powered by Large Language Models (LLMs), as a transformative tool to achieve this goal. We present our approach to developing a human-in-the-loop pipeline for operating advanced instruments including an X-ray nanoprobe beamline and an autonomous robotic station dedicated to the design and characterization of materials. Specifically, we evaluate the potential of various LLMs as trainable scientific assistants for orchestrating complex, multi-task workflows, which also include multimodal data, optimizing their performance through optional human input and iterative learning. We demonstrate the ability of AI agents to bridge the gap between advanced automation and user-friendly operation, paving the way for more adaptable and intelligent scientific facilities.

Large Language Models↗

Probing the rapid formation of black holes and their Galaxy hosts in QSOs

Using the modelling code x-cigale, we reproduced the spectral energy distributions (SEDs) of 1359 SDSS quasi-stellar objects (QSOs) within the redshift range 0 < z < 4, for which we have NIR/MIR fluxes with the highest quality and spectral data characterizing their supermassive black hole (SMBHs). Consistent with a rapid formation of the host galaxies, the star formation histories (SFHs) have small e-folding, at most 750 Myr using an SFH function for spiral or 1000 Myr using one for elliptical. Above z ∼ 1.6, the two solutions are degenerate, the SEDs being dominated by the active galactic nucleus (AGN) continuum and high star formation rates (SFRs), typical of starburst galaxies, while at lower redshifts the starburst nature of the host, independent from its morphology, is better reproduced by a spiral SFH. In general, the SFR increases with the redshift, the mass of the bulge, the AGN luminosity, and Eddington ratio, suggesting there is no evidence of AGN quenching of star formation. Comparing the specific BH accretion rate (BHAR) with specific SFR, all the QSOs at any redshift trace a linear sequence below the Eddington luminosity, in parallel and above the one-to-one relation, implying that QSOs are in a special phase of evolution during which the growth in mass of their SMBH is more rapid than the growth in mass of their galaxy hosts. This particular phase is consistent with a scenario where the galaxy hosts of QSOs in the past grew in mass more rapidly than their SMBHs, suggesting that a high star formation efficiency during their formation was responsible in limiting their masses.

79 ASTRONOMY AND ASTROPHYSICS↗

3D Play Fairway Analysis for Examining of Superhot Reservoir Production Scenarios

The DEEPEN (DE-risking Exploration for geothermal Plays in magmatic ENvironments) project was a multi-laboratory, international effort to reduce uncertainty and improve resource characterization in superhot geothermal systems. Building on this foundation, this work advances open-source tools designed to lower the exploration risk and cost of superhot geothermal projects while promoting transparency, reproducibility, and efficiency in exploration workflows. These tools are being tested at two key sites: (1) the Nesjavellir Geothermal Area in Iceland, where the Icelandic Deep Drilling Project (IDDP) will drill its third well, and (2) Newberry Volcano in Oregon, USA, where Mazama Energy will pilot the first superhot enhanced geothermal system (EGS). A major outcome is the creation of a modular, open-source Python framework for play fairway analysis (PFA) in 2D and 3D, called geoPFA. The PFA workflow has been expanded to produce pseudo conceptual models, and will soon be refined to assess reservoir components through integration with the thermo-hydraulic-mechanical-chemical (THMC) simulator TReactMech, to enable iterative coupling between PFA and THMC models, improving characterization of superhot systems. All three of the Icelandic Deep Drilling Project's production scenarios were analyzed via this framework: (1) a superhot deep injection well paired with conventional production wells at Nesjavellir, (2) a superhot deep production well at Nesjavellir, and (3) superhot enhanced geothermal system at Newberry Volcano. This analysis provides useful insights around conceptual modeling of these production scenarios, helping to inform decisions around which scenario is best suited for which types of environments.

15 GEOTHERMAL ENERGY↗

3D Play Fairway Analysis for Examining of Superhot Drilling Production Scenarios: Preprint

The DEEPEN (DE-risking Exploration for geothermal Plays in magmatic ENvironments) project was a multi-laboratory, international effort to reduce uncertainty and improve resource characterization in superhot geothermal systems. Building on this foundation, this work advances open-source tools designed to lower the exploration risk and cost of superhot geothermal projects while promoting transparency, reproducibility, and efficiency in exploration workflows. These tools are being tested at two key sites: (1) the Nesjavellir Geothermal Area in Iceland, where the Icelandic Deep Drilling Project (IDDP) will drill its third well, and (2) Newberry Volcano in Oregon, USA, where Mazama Energy will pilot the first superhot enhanced geothermal system (EGS). A major outcome is the creation of a modular, open-source Python framework for play fairway analysis (PFA) in 2D and 3D, called geoPFA. The PFA workflow has been expanded to produce pseudo conceptual models, and will soon be refined to assess reservoir components through integration with the thermo-hydraulic-mechanical-chemical (THMC) simulator TReactMech, to enable iterative coupling between PFA and THMC models, improving characterization of superhot systems. All three of the Icelandic Deep Drilling Project's production scenarios were analyzed via this framework: (1) a superhot deep injection well paired with conventional production wells at Nesjavellir, (2) a superhot deep production well at Nesjavellir, and (3) superhot enhanced geothermal system at Newberry Volcano. This analysis provides useful insights around conceptual modeling of these production scenarios, helping to inform decisions around which scenario is best suited for which types of environments.

15 GEOTHERMAL ENERGY↗

On the local measurement of electric currents and magnetic fields using Thomson scattering in Weibel-unstable plasmas

Here, we demonstrate the capability of the Thomson Scattering (TS) diagnostic to measure locally the microscopic electron and ion currents in counter-streaming plasmas unstable to the Weibel or current-filamentation instability. Synthetic TS spectra are calculated with particle distribution functions obtained from particle-in-cell simulations and used to accurately reproduce the simulated currents. We show that this technique allows accurate local measurements of the magnetic field, thus opening the way for the complete experimental characterization of the growth rate, saturation, and nonlinear dynamics of electromagnetic instabilities in plasmas. We illustrate the application of this diagnostic to experimental TS data, which yields local measurements of the magnetic field in Weibel-unstable plasmas and indicates that the magnetic energy density reaches ~1% of the kinetic energy density of the flows, in agreement with previous numerical studies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

16 O + 16 O fusion at sub-coulomb energies

A simple optical model (OPM) method using non-monotonic (NM) potentials characterized by a repulsive core from the microscopic theory of the Pauli-led energy-density functional (EDF) has been developed to investigate the 16 O + 16 O fusion at sub-Coulomb energies relevant to the oxygen burning. Here, the study involves the analysis of the experimental fusion cross-section (FCS) data in energy range 6.92 ≤ E cm ≤ 13.83 MeV, which includes the Coulomb barrier region E cm = 10.0–11.92 MeV. Apart from an excellent description of the existing FCS data in the studied energy range, the associated hindrance, characterized by the S-factor reaching a maximum and then gradually falling off at lower energies, so far suggested empirically for the system, is reproduced down to 4 MeV for the first time in the simple OPM. An S-factor maximum of S 0 = 3.15 × 10 25 MeV.b at E 0 = 7.14 MeV is observed with T ≃ 2.6 GK, which conforms to the values reported for quiescent and explosive burning. Our reaction rate, deduced from the NM potential, compares well with the Caughlan and Fowler data. Dominant partial waves implicit in the observed maximum S-factor in the studied Gamow range are also explored. Our present findings, with the success of NM potentials, suggest that the nucleus–nucleus potential is non-monotonic.

16O+16O↗

Old-Aged groundwater contributes to mountain hillslope hydrologic dynamics

Understanding connectivity between the soil and deeper bedrock groundwater is needed to accurately predict a watershed’s response to perturbation, such as drought. Yet, the bedrock groundwater dynamics in mountainous environments are typically under-constrained and excluded from watershed hydrologic models. Here, we investigate the role of groundwater characterized with decadal and longer water ages on the hydrologic and mass-transport processes within a steep snow-dominated mountain hillslope in the Central Rocky Mountains (USA). We quantify subsurface and surface water mass-balance, groundwater flowpaths, and age distributions using the ParFlow-CLM integrated hydrologic and EcoSLIM particle tracking models, which are compared to hydrometric and environmental tracer observations. An ensemble of models with varied soil and hydrogeologic parameters reproduces observed groundwater levels and century-scale mean ages inferred from environmental tracers. The numerical models suggest soil water near the toe of the hillslope contains considerable (>60 % of the mass-flux) contributions from bedrock flowpaths characterized with water ages >10 years. Flowpath connectivity between the deeper bedrock and soil systems is present throughout the year, highlighting the potentially critical role of groundwater with old ages on processes such as evapotranspiration and streamflow generation. The coupled numerical model and groundwater age observations show the bedrock groundwater system influences the hillslope hydrodynamics and should be considered in mountain watershed conceptual and numerical models.

54 ENVIRONMENTAL SCIENCES↗

Challenges and Opportunities in State‐of‐the‐Art Proteomics Analysis for Biomarker Development From Plasma Extracellular Vesicles

Extracellular vesicles (EVs) are membrane-bound particles secreted by cells, playing crucial roles in intercellular communication. The composition of EVs can undergo changes in response to stress and disease conditions, making them excellent biomarker candidates. However, extracting protein information from EVs can be challenging due to their low abundance in complex biofluids and copurification with contaminant proteins and particles. Techniques to enrich EVs have their strengths and limitations, without one being able to purify EVs to complete homogeneity. This can lead to compromised recovery rates and increased complexity, making data interpretation difficult. In this viewpoint article, we explore the concept that better characterization of EV composition, followed by quantification of EV proteins in complex samples, might be a more viable route for biomarker development. Mass spectrometers can provide reproducible deep coverage of the EV proteome, despite sample impurities. This paradigm shift presents opportunities to integrate advanced bioinformatics tools to refine the EV proteome landscape, identify novel biomarkers, and streamline validation processes in biomarker development. By focusing on leveraging technology rather than achieving absolute purity, this approach can transform current practices and open opportunities for robust biomarker discovery. Herein, we highlight not only such opportunities but also challenges to implement this concept.

Dakup, Panshak P. [Pacific Northwest National Labo↗

Wire, hybrid, and laser-cut X-pinches as Talbot–Lau backlighters for electron density diagnostics

Talbot–Lau x-ray deflectometry (TXD) enables refraction-based imaging for high-energy-density physics experiments, and thus, it has been studied and developed with the goal of diagnosing plasmas relevant to inertial confinement and magnetic liner inertial fusion. X-pinches, known for reliably generating fast (~1 ns), small (~1 µm) x-ray sources, were driven on the compact current driver generator for ablation structure and implosion studies (~200 kA, 150 ns) as a potential backlighter source for TXD. Considering that different X-pinch configurations have characteristic advantages and drawbacks as x-ray generating loads, three distinct copper X-pinch configurations were studied: the wire X-pinch, the hybrid X-pinch, and the laser-cut X-pinch. Furthermore, the Cu K-shell emission from each configuration was characterized and analyzed regarding the specific backlighter requirements for an 8 keV TXD system: spatial and temporal resolution, number of sources, time of emission, spectrum, and reproducibility. Recommendations for future experimental improvements and applications are presented. The electron density of static objects was retrieved from Moiré images obtained through TXD. This allowed to calculate the mass density of static samples within 4% of the expected value for laser-cut X-pinches, which were found to be the optimal X-pinch configuration for TXD due to their high reproducibility, small source size (≤5 µm), short duration (~1 ns), and up to 106 W peak power near 8 keV photon energy. Plasma loads were imaged through TXD for the first-time using laser-cut X-pinch backlighting. Experimental images were compared with simulations from the x-ray wave-front propagation code, demonstrating that TXD can be a powerful x-ray refraction-based diagnostic for dense Z-pinch loads. Future plans for Talbot–Lau interferometry diagnostics in the pulsed-power environment are described.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

AuriDESI: mock catalogues for the DESI Milky Way Survey

The Dark Energy Spectroscopic Instrument Milky Way Survey (DESI MWS) will explore the assembly history of the Milky Way by characterizing remnants of ancient dwarf galaxy accretion events and improving constraints on the distribution of dark matter in the outer halo. We present mock catalogues that reproduce the selection criteria of MWS and the format of the final MWS data set. These catalogues can be used to test methods for quantifying the properties of stellar halo substructure and reconstructing the Milky Way’s accretion history with the MWS data, including the effects of halo-to-halo variance. The mock catalogues are based on a phase-space kernel expansion technique applied to star particles in the Auriga suite of six high-resolution lambda-cold dark matter magnetohydrodynamic zoom-in simulations. They include photometric properties (and associated errors) used in DESI target selection and the outputs of the MWS spectral analysis pipeline (radial velocity, metallicity, surface gravity, and temperature). They also include information from the underlying simulation, such as the total gravitational potential and information on the progenitors of accreted halo stars. We discuss how the subset of halo stars observable by MWS in these simulations corresponds to their true content and properties. These mock Milky Ways have rich accretion histories, resulting in a large number of substructures that span the whole stellar halo out to large distances and have substantial overlap in the space of orbital energy and angular momentum.

dynamics↗

The Anatomy of an Unusual Edge-on Protoplanetary Disk. I. Dust Settling in a Cold Disk

As the earliest stage of planet formation, massive, optically thick, and gas-rich protoplanetary disks provide key insights into the physics of star and planet formation. When viewed edge-on, high-resolution images offer a unique opportunity to study both the radial and vertical structures of these disks and relate this to vertical settling, radial drift, grain growth, and changes in the midplane temperatures. In this work, we present multi-epoch Hubble Space Telescope and Keck scattered light images, and an Atacama Large Millimeter/submillimeter Array 1.3 mm continuum map for the remarkably flat edge-on protoplanetary disk SSTC2DJ163131.2–242627, a young solar-type star in ρ Ophiuchus. We model the 0.8 μm and 1.3 mm images in separate Markov Chain Monte Carlo (MCMC) runs to investigate the geometry and dust properties of the disk using the MCFOST radiative transfer code. In scattered light, we are sensitive to the smaller dust grains in the surface layers of the disk, while the submillimeter dust continuum observations probe larger grains closer to the disk midplane. An MCMC run combining both data sets using a covariance-based log-likelihood estimation was marginally successful, implying insufficient complexity in our disk model. The disk is well characterized by a flared disk model with an exponentially tapered outer edge viewed nearly edge-on, though some degree of dust settling is required to reproduce the vertically thin profile and lack of apparent flaring. A colder than expected disk midplane, evidence for dust settling, and residual radial substructures all point to a more complex radial density profile to be probed with future, higher-resolution observations.

47 OTHER INSTRUMENTATION↗

Importance of multimodal characterization and influence of residual Li 2 S impurity in amorphous Li 3 PS 4 inorganic electrolytes

Amorphous Li 3 PS 4 (LPS) solid-state electrolytes are promising for energy-dense lithium metal batteries. LPS glass, synthesized from a 3:1 mol ratio of Li 2 S and P 2 S 5 , has high ionic conductivity and can be synthesized by ball milling or solution processing. Ball milling has been attractive because it provides the easiest route to access amorphous LPS with a conductivity of 3.5 × 10 –4 S cm –1 (20 °C). However, achieving the complete reaction of precursors via ball milling can be difficult, and most literature reports use X-ray diffraction (XRD) or Raman spectroscopy to confirm sample purity, both of which have limitations. Furthermore, the effect of residual precursors on ionic conductivity and lithium metal cycling is unknown. In this work, we illustrate the importance of multimodal characterization to determine LPS phase and chemical purity. To determine the residual Li 2 S content in LPS, we show that (1) XRD and 31 P solid state nuclear magnetic resonance (ssNMR) are insufficient and (2) Raman loses sensitivity at concentrations below 12 mol% Li 2 S. Most importantly, we show that 7 Li ssNMR is highly sensitive. Using 7 Li ssNMR, we investigate the effect of ball milling parameters and develop a robust and highly reproducible procedure for pure LPS synthesis. We find that as the residual Li 2 S precursor content increases, LPS conductivity decreases and lithium metal batteries exhibit higher overpotentials and poor cycle life. Furthermore, our work reveals the importance of multimodal characterization techniques for amorphous solid-state electrolyte characterization and will enable better synthetic strategies for highly conductive electrolytes for efficient energy-dense solid-state lithium metal batteries.

25 ENERGY STORAGE↗

Thermal property characterization of phase change materials in building applications: A systematic review of fundamentals, recent progress, and future directions

Phase change materials (PCMs) can reduce building peak loads and enable demand-responsive thermal energy storage (TES), but their deployment depends on reliable measurement and interpretation of thermal properties across laboratory, intermediate, and application scales. Here, this review systematically examines characterization methods, testing protocols, and recent advances for neat PCMs and PCM composites, emphasizing thermal conductivity, enthalpy-related properties (phase change temperature, latent heat, specific heat), and cycling stability. For thermal conductivity, we compare steady-state and transient techniques and note limitations when phase transition and contact resistance affect measurements. For enthalpy–temperature characterization, we discuss differential scanning calorimetry together with intermediate- and bulk-scale methods, including T-history, heat flow meter testing, and three-layer calorimetry (3LC), to generate application-relevant enthalpy–temperature profiles. Cycling stability is organized into four experimental families: thermoelectric–air, fully thermoelectric, water-bath, and in situ chamber approaches, with attention to separating reversible supercooling from true degradation such as phase segregation. We highlight emerging noncontact diagnostics, including infrared thermography and embedded sensing, for spatially resolved validation and multiscale interpretation. Finally, we review the growing use of AI and machine learning for property prediction, inverse characterization from experimental signals, and real-time state estimation in building-integrated TES. Key needs include harmonized protocols, interlaboratory benchmarking, uncertainty reporting, and metadata-rich datasets to accelerate reproducible PCM qualification for grid-flexible buildings.

AI↗

Dynamic Rupture Simulations of the Mw7.2 1992 Landers,California, Earthquake

An important part of the simulation technique implemented in the Recipe is the earthquake rupture characterization based on established empirical scaling relationships between source parameters and seismic moment. The scaling relationships used in the rupture characterization are derived from recorded data and are tested against dynamic and kinematic rupture models of crustal earthquakes. Currently, a revised three-stage source scaling relationship between the source rupture area and seismic moment of inland crustal earthquakes, initially proposed by Irikura and Miyake, is being used. Recent analysis of results obtained with the Recipe for the 2016 M7.1 Kumamoto earthquake performed by several researchers, including the PI, Arben Pitarka, indicate that in order to reproduce the near-fault long-period ground motions and the permanent displacements, observed during surface-rupturing earthquakes, the recipe needs modifications to the kinematic rupture parametrization in the shallow crustal weak zone. In particular, improvements are required in characterizing the slip rate function at shallow depths, above the sesimogenic zone. The main objective of this research project was the development of a dynamic rupture model for the Mw7.2 1992 Landers, California earthquake. The dynamic rupture model was used to derive kinematic rupture parameters, such as slip, slip-rate, rupture time distributions, and slip rate functions in the weak (0-4km) and seismogenic crustal zones. In addition to the knowledge gained from the dynamic rupture modeling of Chi-chi, Ridgecrest, Kumamoto and other crustal earthquakes, studied by the PI, the rupture characterization of the Landers earthquake can be helpful in updating the conventional recipe for broadband ground motion simulations of crustal earthquakes with shallow rupture. The project emphasizes the need for performing physics-based earthquake rupture modeling which ultimately will inform further improvements of rupture characterization. In particular, the modeling of rupture dynamics for recorded earthquakes, and numerical simulations for different rupture scenarios, provides physical constrains on slip evolution in time and space, used in adjustments of empirical source scaling relationships for large crustal earthquakes.

58 GEOSCIENCES↗

A comparative study of machine learning models for predicting the state of reactive mixing

Mixing phenomena are important mechanisms controlling flow, species transport, and reaction processes in fluids and porous media. Accurate predictions of reactive mixing are critical for many Earth and environmental science problems such as contaminant fate and remediation, macroalgae growth, and plankton biomass evolution. Here, to investigate the evolution of mixing dynamics under different scenarios (e.g., anisotropy, fluctuating velocity fields), a finite-element-based numerical model was built to solve the fast, irreversible bimolecular reaction-diffusion equations to simulate a range of reactive-mixing scenarios. A total of 2,315 simulations were performed using different sets of model input parameters comprising various spatial scales of vortex structures in the velocity field, time-scales associated with velocity oscillations, the perturbation parameter for the vortex-based velocity, anisotropic dispersion contrast (i.e., ratio of longitudinal-to-transverse dispersion), and molecular diffusion. The outputs comprised concentration profiles of reactants and products. The inputs to and outputs from these simulations were concatenated into feature and label matrices, respectively, to train 20 different machine learning (ML) models intended to emulate system behavior. These 20 ML emulators, based on linear methods, Bayesian methods, ensemble learning methods, and multilayer perceptrons (MLPs), were trained to classify the state of mixing and predict three quantities of interest (QoIs) characterizing species production, decay (i.e., average concentration, square of average concentration), and degree of mixing (i.e., variances of species concentration). Unsurprisingly, linear classifiers and regressors failed to reproduce the QoIs; however, ensemble methods (classifiers and regressors) and the MLP model accurately classified the state of reactive mixing and the QoIs. Among ensemble methods, random forest and decision-tree-based AdaBoost faithfully predicted the QoIs. At run time, trained ML emulators produced results times faster than the finite-element simulations. Due to their low computational expense and high accuracy, ensemble and MLP models are excellent emulators for these numerical simulations and great utilities in uncertainty quantification exercises, which can require 1,000s of forward model runs.

97 MATHEMATICS AND COMPUTING↗