Search NASA⌕ Search

SEARCH · Search NASA

Results for “Distributed Learning”

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 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

WE-AB-BRB-00: Session in Memory of Robert J. Shalek: High Resolution Dosimetry from 2D to 3D to Real-Time 3D

Despite widespread IMRT treatments at modern radiation therapy clinics, precise dosimetric commissioning of an IMRT system remains a challenge. In the most recent report from the Radiological Physics Center (RPC), nearly 20% of institutions failed an end-to-end test with an anthropomorphic head and neck phantom, a test that has rather lenient dose difference and distance-to-agreement criteria of 7% and 4 mm. The RPC report provides strong evidence that IMRT implementation is prone to error and that improved quality assurance tools are required. At the heart of radiation therapy dosimetry is the multidimensional dosimeter. However, due to the limited availability of water-equivalent dosimetry materials, research and development in this important field is challenging. In this session, we will review a few dosimeter developments that are either in the laboratory phase or in the pre-commercialization phase. 1) Radiochromic plastic. Novel formulations exhibit light absorbing optical contrast with very little scatter, enabling faster, broad beam optical CT design. 2) Storage phosphor. After irradiation, the dosimetry panels will be read out using a dedicated 2D scanning apparatus in a non-invasive, electro-optic manner and immediately restored for further use. 3) Liquid scintillator. Scintillators convert the energy from x-rays and proton beams into visible light, which can be recorded with a scientific camera (CCD or CMOS) from multiple angles. The 3D shape of the dose distribution can then be reconstructed. 4) Cherenkov emission imaging. Gated intensified imaging allows video-rate passive detection of Cherenkov emission during radiation therapy with the room lights on. Learning Objectives: To understand the physics of a variety of dosimetry techniques based upon optical imaging To investigate the strategies to overcome respective challenges and limitations To explore novel ideas of dosimeter design Supported in part by NIH Grants R01CA148853, R01CA182450, R01CA109558. Brian Pogue is founder and president of the company DoseOptics LLC, dedicated to developing and commercializing the first dedicated Cerenkov imaging camera and system for radiation dose imaging. Work reported in this talk does not involve the use of DoseOptics technology.; H. Li, this work was supported in part by NIH Grant No. R01CA148853; S. Beddar, NIH funding R01-CA182450.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Data associated with “Different methods of estimating riverbed sediment grain size diverge at the basin scale ” (v2)

This data package is associated with the publication “Different methods of estimating riverbed sediment grain size diverge at the basin scale” published in Frontiers in Earth Science (Regier et al., 2025). The distribution of sediment grain size in streams and rivers is often quantified by the median grain size (d50), a key metric for understanding and predicting hydrologic and biogeochemical function of streams and rivers. Manual methods to measure d50 are time-consuming and ignore larger grains, while model-based methods to estimate d50 often over-generalize basin characteristics, and therefore cannot accurately represent site-scale heterogeneity. Here, we apply a machine learning-enabled photogrammetry methodology (You Only Look Once, or YOLO) for estimating d50 for grains > 2 mm based on images collected from streams and rivers throughout the Yakima River Basin (YRB). To understand how such methods may help bridge the gaps in resolution and accuracy between manual and catchment characteristics model-based d50 estimates, we compared YOLO d50 values to manual and model-based estimates across the YRB. We found distinct differences among methods for d50 averages and variability, and relationships between d50 estimates and basin characteristics. Source images can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/1892052. This data package was originally published in May 2023. It was updated August 2025 (v2; new and modified files). File and folder names were not revised to indicate changes. See the change history section in the readme for more details. In addition to the readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) readme; (4) and subfolders containing data, figures, and scripts. The data folder contains datasets used for the analyses in the manuscript in image, text-delimited or geospatially-referenced formats. The figures folder contains the figures from the manuscript in different formats. The scripts folder contains all of the scripts used to complete the analyses in the manuscript. All files are .csv, .rds, .dbf, .prj, .shp, .shx, .jpg, .png, .R, .Rproj, or .pdf. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected some of these data. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

WE-AB-BRB-01: Memorial Introduction; Storage Phosphor Panels for Radiation Therapy Dosimetry

Despite widespread IMRT treatments at modern radiation therapy clinics, precise dosimetric commissioning of an IMRT system remains a challenge. In the most recent report from the Radiological Physics Center (RPC), nearly 20% of institutions failed an end-to-end test with an anthropomorphic head and neck phantom, a test that has rather lenient dose difference and distance-to-agreement criteria of 7% and 4 mm. The RPC report provides strong evidence that IMRT implementation is prone to error and that improved quality assurance tools are required. At the heart of radiation therapy dosimetry is the multidimensional dosimeter. However, due to the limited availability of water-equivalent dosimetry materials, research and development in this important field is challenging. In this session, we will review a few dosimeter developments that are either in the laboratory phase or in the pre-commercialization phase. 1) Radiochromic plastic. Novel formulations exhibit light absorbing optical contrast with very little scatter, enabling faster, broad beam optical CT design. 2) Storage phosphor. After irradiation, the dosimetry panels will be read out using a dedicated 2D scanning apparatus in a non-invasive, electro-optic manner and immediately restored for further use. 3) Liquid scintillator. Scintillators convert the energy from x-rays and proton beams into visible light, which can be recorded with a scientific camera (CCD or CMOS) from multiple angles. The 3D shape of the dose distribution can then be reconstructed. 4) Cherenkov emission imaging. Gated intensified imaging allows video-rate passive detection of Cherenkov emission during radiation therapy with the room lights on. Learning Objectives: To understand the physics of a variety of dosimetry techniques based upon optical imaging To investigate the strategies to overcome respective challenges and limitations To explore novel ideas of dosimeter design Supported in part by NIH Grants R01CA148853, R01CA182450, R01CA109558. Brian Pogue is founder and president of the company DoseOptics LLC, dedicated to developing and commercializing the first dedicated Cerenkov imaging camera and system for radiation dose imaging. Work reported in this talk does not involve the use of DoseOptics technology.; H. Li, this work was supported in part by NIH Grant No. R01CA148853; S. Beddar, NIH funding R01-CA182450.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-AB-BRB-02: Methods and Applications of 3D Radiochromic Dosimetry

Despite widespread IMRT treatments at modern radiation therapy clinics, precise dosimetric commissioning of an IMRT system remains a challenge. In the most recent report from the Radiological Physics Center (RPC), nearly 20% of institutions failed an end-to-end test with an anthropomorphic head and neck phantom, a test that has rather lenient dose difference and distance-to-agreement criteria of 7% and 4 mm. The RPC report provides strong evidence that IMRT implementation is prone to error and that improved quality assurance tools are required. At the heart of radiation therapy dosimetry is the multidimensional dosimeter. However, due to the limited availability of water-equivalent dosimetry materials, research and development in this important field is challenging. In this session, we will review a few dosimeter developments that are either in the laboratory phase or in the pre-commercialization phase. 1) Radiochromic plastic. Novel formulations exhibit light absorbing optical contrast with very little scatter, enabling faster, broad beam optical CT design. 2) Storage phosphor. After irradiation, the dosimetry panels will be read out using a dedicated 2D scanning apparatus in a non-invasive, electro-optic manner and immediately restored for further use. 3) Liquid scintillator. Scintillators convert the energy from x-rays and proton beams into visible light, which can be recorded with a scientific camera (CCD or CMOS) from multiple angles. The 3D shape of the dose distribution can then be reconstructed. 4) Cherenkov emission imaging. Gated intensified imaging allows video-rate passive detection of Cherenkov emission during radiation therapy with the room lights on. Learning Objectives: To understand the physics of a variety of dosimetry techniques based upon optical imaging To investigate the strategies to overcome respective challenges and limitations To explore novel ideas of dosimeter design Supported in part by NIH Grants R01CA148853, R01CA182450, R01CA109558. Brian Pogue is founder and president of the company DoseOptics LLC, dedicated to developing and commercializing the first dedicated Cerenkov imaging camera and system for radiation dose imaging. Work reported in this talk does not involve the use of DoseOptics technology.; H. Li, this work was supported in part by NIH Grant No. R01CA148853; S. Beddar, NIH funding R01-CA182450.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-AB-BRB-03: Real-Time Volumetric Scintillation Dosimetry for Radiation Therapy

Despite widespread IMRT treatments at modern radiation therapy clinics, precise dosimetric commissioning of an IMRT system remains a challenge. In the most recent report from the Radiological Physics Center (RPC), nearly 20% of institutions failed an end-to-end test with an anthropomorphic head and neck phantom, a test that has rather lenient dose difference and distance-to-agreement criteria of 7% and 4 mm. The RPC report provides strong evidence that IMRT implementation is prone to error and that improved quality assurance tools are required. At the heart of radiation therapy dosimetry is the multidimensional dosimeter. However, due to the limited availability of water-equivalent dosimetry materials, research and development in this important field is challenging. In this session, we will review a few dosimeter developments that are either in the laboratory phase or in the pre-commercialization phase. 1) Radiochromic plastic. Novel formulations exhibit light absorbing optical contrast with very little scatter, enabling faster, broad beam optical CT design. 2) Storage phosphor. After irradiation, the dosimetry panels will be read out using a dedicated 2D scanning apparatus in a non-invasive, electro-optic manner and immediately restored for further use. 3) Liquid scintillator. Scintillators convert the energy from x-rays and proton beams into visible light, which can be recorded with a scientific camera (CCD or CMOS) from multiple angles. The 3D shape of the dose distribution can then be reconstructed. 4) Cherenkov emission imaging. Gated intensified imaging allows video-rate passive detection of Cherenkov emission during radiation therapy with the room lights on. Learning Objectives: To understand the physics of a variety of dosimetry techniques based upon optical imaging To investigate the strategies to overcome respective challenges and limitations To explore novel ideas of dosimeter design Supported in part by NIH Grants R01CA148853, R01CA182450, R01CA109558. Brian Pogue is founder and president of the company DoseOptics LLC, dedicated to developing and commercializing the first dedicated Cerenkov imaging camera and system for radiation dose imaging. Work reported in this talk does not involve the use of DoseOptics technology.; H. Li, this work was supported in part by NIH Grant No. R01CA148853; S. Beddar, NIH funding R01-CA182450.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Signal-preserving CMB component separation with machine learning

Analysis of microwave sky signals, such as the cosmic microwave background, often requires component separation using multifrequency methods, whereby different signals are isolated according to their different frequency behaviors. Many so-called blind methods, such as the internal linear combination (ILC), make minimal assumptions about the spatial distribution of the signal or contaminants, and only assume knowledge of the frequency dependence of the signal. The ILC produces a minimum-variance linear combination of the measured frequency maps. In the case of Gaussian, statistically isotropic fields, this is the optimal linear combination, as the variance is the only statistic of interest. However, in many cases the signal we wish to isolate, or the foregrounds we wish to remove, are non-Gaussian and/or statistically anisotropic (in particular for the case of Galactic foregrounds). In such cases, it is possible that machine learning (ML) techniques can be used to exploit the non-Gaussian features of the foregrounds and thereby improve component separation. However, many ML techniques require the use of complex, difficult-to-interpret operations on the data. We propose a hybrid method whereby we train an ML model using only combinations of the data that , and combine the resulting ML-predicted foreground estimate with the ILC solution to reduce the error from the ILC. We demonstrate our methods on simulations of extragalactic temperature and Galactic polarization foregrounds and show that our ML model can exploit non-Gaussian features, such as point sources and spatially varying spectral indices, to produce lower-variance maps than ILC—e.g., reducing the variance of the B-mode residual by factors of up to 5—while preserving the signal of interest in an unbiased manner. Moreover, we often find improved performance even when applying our ML technique to foreground models on which it was not trained. Published by the American Physical Society 2025

McCarthy, Fiona (ORCID:0000000253893565)↗
DOE OSTI2016

WE-AB-BRB-04: Cherenkov Imaging for Radiation Therapy Dose Verification On Patients

Despite widespread IMRT treatments at modern radiation therapy clinics, precise dosimetric commissioning of an IMRT system remains a challenge. In the most recent report from the Radiological Physics Center (RPC), nearly 20% of institutions failed an end-to-end test with an anthropomorphic head and neck phantom, a test that has rather lenient dose difference and distance-to-agreement criteria of 7% and 4 mm. The RPC report provides strong evidence that IMRT implementation is prone to error and that improved quality assurance tools are required. At the heart of radiation therapy dosimetry is the multidimensional dosimeter. However, due to the limited availability of water-equivalent dosimetry materials, research and development in this important field is challenging. In this session, we will review a few dosimeter developments that are either in the laboratory phase or in the pre-commercialization phase. 1) Radiochromic plastic. Novel formulations exhibit light absorbing optical contrast with very little scatter, enabling faster, broad beam optical CT design. 2) Storage phosphor. After irradiation, the dosimetry panels will be read out using a dedicated 2D scanning apparatus in a non-invasive, electro-optic manner and immediately restored for further use. 3) Liquid scintillator. Scintillators convert the energy from x-rays and proton beams into visible light, which can be recorded with a scientific camera (CCD or CMOS) from multiple angles. The 3D shape of the dose distribution can then be reconstructed. 4) Cherenkov emission imaging. Gated intensified imaging allows video-rate passive detection of Cherenkov emission during radiation therapy with the room lights on. Learning Objectives: To understand the physics of a variety of dosimetry techniques based upon optical imaging To investigate the strategies to overcome respective challenges and limitations To explore novel ideas of dosimeter design Supported in part by NIH Grants R01CA148853, R01CA182450, R01CA109558. Brian Pogue is founder and president of the company DoseOptics LLC, dedicated to developing and commercializing the first dedicated Cerenkov imaging camera and system for radiation dose imaging. Work reported in this talk does not involve the use of DoseOptics technology.; H. Li, this work was supported in part by NIH Grant No. R01CA148853; S. Beddar, NIH funding R01-CA182450.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

The EGS Collab project: Outcomes and lessons learned from hydraulic fracture stimulations in crystalline rock at 1.25 and 1.5 km depth

With the goal of better understanding stimulation in crystalline rock for improving enhanced geothermal systems (EGS), the EGS Collab Project performed a series of stimulations and flow tests at 1.25 and 1.5 km depths. The tests were performed in two well-instrumented testbeds in the Sanford Underground Research Facility in Lead, South Dakota, United States. The testbed for Experiment 1 at 1.5 km depth contained two open wells for injection and production and six instrumented monitoring wells surrounding the targeted stimulation zone. Four multi-step stimulation tests targeting hydraulic fracturing and nearly year-long ambient temperature and chilled water flow tests were performed in Experiment 1. The testbed for Experiments 2 and 3 was at 1.25 km depth and contained five open wells in an outwardly fanning five-spot pattern and two fans of well-instrumented monitoring wells surrounding the targeted stimulation zone. Experiment 2 targeted shear stimulation, and Experiment 3 targeted low-flow, high-flow, and oscillating pressure stimulation strategies. Hydraulic fracturing was successful in Experiments 1 and 3 in generating a connected system wherein injected water could be collected. However, the resulting flow was distributed dynamically, and not entirely collected at the anticipated production well. Thermal breakthrough was not observed in the production well, but that could have been masked by the Joule-Thomson effect. Shear stimulation in Experiment 2 did not occur - despite attempting to pressurize the fractures most likely to shear - because of the inability to inject water into a mostly-healed fracture, and the low shear-to-normal stress ratio. The EGS Collab experiments are described to provide a background for lessons learned on topics including induced seismicity, the correlation between seismicity and permeability, distributed and dynamic flow systems, thermoelastic and pressure effects, shear stimulation, local geology, thermal breakthrough, monitoring stimulation, grouting boreholes, modeling, and system management.

15 - GEOTHERMAL ENERGY↗
DOE OSTI2019

Using Real-Time Electricity Prices to Leverage Electrical Energy Storage and Flexible Loads in a Smart Grid Environment Utilizing Machine Learning Techniques

With exposure to real-time market pricing structures, consumers would be incentivized to invest in electrical energy storage systems and smart predictive automation of their home energy systems. Smart home automation through optimizing HVAC (heating, ventilation, and air conditioning) temperature set points, along with distributed energy storage, could be utilized in the process of optimizing the operation of the electric grid. Using electricity prices as decision variables to leverage electrical energy storage and flexible loads can be a valuable tool to optimize the performance of the power grid and reduce electricity costs both on the supply and demand sides. Energy demand prediction is important for proper allocation and utilization of the available resources. Manipulating energy prices to leverage storage and flexible loads through these demand prediction models is a novel idea that needs to be studied. In this paper, different models for proactive prediction of the energy demand for an entire city using different machine learning techniques are presented and compared. The results of the machine learning techniques show that the proposed nonlinear autoregressive with exogenous inputs neural network model resulted in the most accurate predictions. These prediction models pave the way for the demand side to become an important asset for grid regulation by responding to variable price signals through battery energy storage and passive thermal energy storage using HVAC temperature set points.

25 ENERGY STORAGE↗
DOE OSTI2021

Adaptive critic design-based reinforcement learning approach in controlling virtual inertia-based grid-connected inverters

In this report, an adaptive critic design (ACD) approach is proposed to control the phase and voltage of a grid-connected virtual synchronous generator (VSG). The penetration of fast responding inertia-less power converters significantly affect the stability of the power system, especially weak systems such as micro grids. The concept of virtual inertia addresses this concern by virtually emulating the behavior of a synchronous generator. However, the conventional VSG is designed based on two conditions: (i) fixed operating point and (ii) inductive grid connections. The performance of VSGs in low-voltage semi-resistive microgrids is far from optimal. To overcome the aforementioned concerns, a heuristic dynamic programing (HDP) approach is proposed to optimally control grid-connected VSGs. The neural-network-based inherence of the HDP enables the proposed technique to adapt to any impedance angle. The HDP controller includes two subnetworks: (i) the action network that controls the system optimally and (ii) the critic network, which evaluates the effectiveness of the action network. The simulation and experimental results are provided to evaluate the effectiveness of the proposed technique. As shown, the HDP-based approach illustrates a better performance in comparison with the conventional PI-based VSG in various operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Reconstruction of Six-Dimensional Phase Space

A phase space is a mathematical representation of all possible physical states of a system. Particle beams at Fermilab exist within a six-dimensional (6D) phase space defined by three positional components, (x, y, z) and three momentum components, (px, py, pz). To reconstruct this space implies taking measurement data from detectors and mapping out particle behavior using computational methods. The beam detectors, however, are only able to detect spatial distribution among the events of the beam, therefore being limited to positional data. Also, due to the vast number of events in a particle beam, it is extremely difficult to analyze and differentiate every single one’s behavior. However, with Machine Learning (ML), which can distinguish between patterns and map out particle behavior more efficiently. We first used the particle beam software, G4beamline, to simulate a 10,000-event muon beam, adjusting parameters such as initial momentum magnitude (p¬0) and virtual detector position. Using ten virtual detectors, we analyzed p0 values such that minimum 9,990 events were analyzed by every detector. We then input the data from these beam simulations to a C++ program, that randomly selects 100 events, and creates a 2D histogram based on spatial distribution, detector position, and event intensity. This process is repeated 100 times to create 100 histograms per p0 value. These images were then input to a modified ResNet18 Convolutional Neural Network (CNN) for training, and to predict p0 from some unseen set of histograms. The model was accurate when trained on momentum increments of 5 MeV/c and provided with denser training samples around highly variable test values. These results displayed machine learning being able to accurately predict p0 from being trained on different particle behaviors.

Shirlee, Jermain [Fermilab]↗
DOE OSTI2017

Correcting Type Ia Supernova Distances for Selection Biases and Contamination in Photometrically Identified Samples

We present a new technique to create a bin-averaged Hubble diagram (HD) from photometrically identified SN Ia data. The resulting HD is corrected for selection biases and contamination from core-collapse (CC) SNe, and can be used to infer cosmological parameters. This method, called “BEAMS with Bias Corrections” (BBC), includes two fitting stages. The first BBC fitting stage uses a posterior distribution that includes multiple SN likelihoods, a Monte Carlo simulation to bias-correct the fitted SALT-II parameters, and CC probabilities determined from a machine-learning technique. The BBC fit determines (1) a bin-averaged HD (average distance versus redshift), and (2) the nuisance parameters α and β, which multiply the stretch and color (respectively) to standardize the SN brightness. In the second stage, the bin-averaged HD is fit to a cosmological model where priors can be imposed. We perform high-precision tests of the BBC method by simulating large (150,000 event) data samples corresponding to the Dark Energy Survey Supernova Program. Our tests include three models of intrinsic scatter, each with two different CC rates. In the BBC fit, the SALT-II nuisance parameters α and β are recovered to within 1% of their true values. In the cosmology fit, we determine the dark energy equation of state parameter w using a fixed value of Ω{sub M} as a prior: averaging over all six tests based on 6 × 150,000 = 900,000 SNe, there is a small w-bias of 0.006±0.002. Finally, the BBC fitting code is publicly available in the SNANA package.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

National Council on Electricity Policy MINI GUIDE: Engagement between States and Regional Transmission Organizations

The National Council on Electricity Policy (NCEP) is a platform for all state-level electricity decision-makers to share and learn from diverse perspectives on the evolving electricity sector. The NCEP mini guide series promotes this dialogue by highlighting examples of successful engagement across its members. Each mini guide features collaborative approaches, lessons learned, and interviews with leading state and local decision-makers.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning

This paper explores uncertainty quantification (UQ) methods in the context of Kolmogorov–Arnold Networks (KANs). We apply an ensemble approach to KANs to obtain a heuristic measure of UQ, enhancing interpretability and robustness in modeling complex functions. Building on this, we introduce Conformalized-KANs, which integrate conformal prediction, a distribution-free UQ technique, with KAN ensembles to generate calibrated prediction intervals with guaranteed coverage.} Extensive numerical experiments are conducted to evaluate the effectiveness of these methods, focusing particularly on the robustness and accuracy of the prediction intervals under various hyperparameter settings. We show that the conformal KAN predictions can be applied to recent extensions of KANs, including Finite Basis KANs (FBKANs) and multifideilty KANs (MFKANs). The results demonstrate the potential of our approaches to significantly improve the reliability and applicability of KANs in scientific machine learning.

• Artificial intelligence (AI) / machine learning ↗
DOE OSTI2022

DIGS: deep inference of galaxy spectra with neural posterior estimation

Abstract With the advent of billion-galaxy surveys with complex data, the need of the hour is to efficiently model galaxy spectral energy distributions (SEDs) with robust uncertainty quantification. The combination of simulation-based inference (SBI) and amortized neural posterior estimation (NPE) has been successfully used to analyse simulated and real galaxy photometry both precisely and efficiently. In this work, we utilise this combination and build on existing literature to analyse simulated noisy galaxy spectra. Here, we demonstrate a proof-of-concept study of spectra that is (a) an efficient analysis of galaxy SEDs and inference of galaxy parameters with physically interpretable uncertainties; and (b) amortized calculations of posterior distributions of said galaxy parameters at the modest cost of a few galaxy fits with Markov chain Monte Carlo (MCMC) methods. We utilise the SED generator and inference framework Prospector to generate simulated spectra, and train a dataset of 2 × 10 6 spectra (corresponding to a five-parameter SED model) with NPE. We show that SBI—with its combination of fast and amortized posterior estimations—is capable of inferring accurate galaxy stellar masses and metallicities. Our uncertainty constraints are comparable to or moderately weaker than traditional inverse-modelling with Bayesian MCMC methods (e.g. 0.17 and 0.26 dex in stellar mass and metallicity for a given galaxy, respectively). We also find that our inference framework conducts rapid SED inference (0.9–1.2 × 10 5 galaxy spectra via SBI/NPE at the cost of 1 MCMC-based fit). With this work, we set the stage for further work that focuses of SED fitting of galaxy spectra with SBI, in the era of JWST galaxy survey programs and the wide-field Roman Space Telescope spectroscopic surveys.

spectroscopy↗