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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine learning models for PDE constrained optimization

Partial differential equation (PDE)-constrained optimization problems arise in a variety of scientific and engineering applications, such as topology optimization, electrodynamics, fluid dynamics, and structural dynamics. However, these problems are often challenging and computationally expensive to solve, due to the need to solve the PDEs within the optimization loop. One approach to reducing the computational cost of these methods while providing convergence guarantees is through inexact trust region methods; this method uses lower fidelity solutions of the PDE at early stages of the optimization and adjusts the required accuracy of inexact PDE solvers as the optimization progresses. In this work, we explore the use of machine learning based surrogate models with these inexact trust region methods. We first demonstrate the potential of this approach by using Gaussian processes as the surrogate model and test this on a simple PDE-constrained optimization problem. We then document explorations into improving the computational costs of evolutional deep neural network / neural Galerkin methods, with the eventual goal of using these methods with the inexact trust region algorithms. We are able to speed up these approaches, albeit at the cost of lower accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Internal consistency and diversity scenario development: A comparative framework to evaluate energy model scenarios

Energy modeling frameworks and scenario analysis help us explore the potential impact of our actions and uncertainties in future energy systems. Despite their importance, there is no systematic procedure for evaluating the scenario development process. In a literature review, we identify two core elements of the scenario development process: internal consistency and diversity which are oftentimes missing from scenarios. Here, to address this gap, we create the Internal consistency and Diversity Scenario Development (IDSD) comparative framework which aims to assess the feasibility and diversity of scenarios for a given energy model. With this framework, we review commonly used energy models and demonstrate our framework on their scenarios. The IDSD comparative framework can serve several purposes absent from previous scenario development work by aiding energy modelers and report writers in crafting high-quality scenarios. First, the IDSD is a reflective tool which can improve the quality of the scenario development process, enabling a comparative assessment of energy models and scenarios. Second, the IDSD can provide guidance to modeling frameworks with existing scenarios and those still in development; this feedback will enable modelers to improve the development and the communication of the limitations of their scenarios. Third, this study has highlighted areas for improvement in the scenario development of some commonly used energy model frameworks. Finally, there is a complete lack of explanation regarding the stakeholder selection process. Addressing these identified items could increase opportunities for advanced energy technology uptake and improve our options for achieving a more resilient energy system.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Magnetic properties of the Ising-like rare earth pyrosilicate: D-Er 2 Si 2 O 7

Ising-like spin-1/2 magnetic materials are of interest for their ready connection to theory, particularly in the context of quantum critical behavior. In this work we report detailed studies of the magnetic properties of a member of the rare earth pyrosilicate family, D-Er 2 Si 2 O 7 , which is known to display a highly anisotropic Ising-like g-tensor and effective spin-1/2 magnetic moments. We used powder neutron diffraction, powder inelastic neutron spectroscopy (INS), and single crystal AC susceptibility to characterize its magnetic properties. Neutron diffraction enabled us to determine the magnetic structure below the known transition temperature (T N = 1.9 K) in zero field, confirming that the magnetic state is a four-sublattice antiferromagnetic structure with two non-collinear Ising axes, as was previously hypothesized. Our powder INS data revealed a gapped excitation at zero field, consistent with anisotropic (possibly Ising) exchange. An applied field of 1 T produces a mode softening, which is consistent with a field-induced second order phase transition. To assess the relevance of D-Er 2 Si 2 O 7 to the transverse field Ising model, we performed AC susceptibility measurements on a single crystal with the magnetic field oriented in the direction transverse to the Ising axes. This revealed a transition at 2.65 T at 0.1 K, a field significantly higher than the mode-softening field observed by powder INS, showing that the field-induced phase transitions are highly field-direction dependent as expected. Furthermore, these measurements suggest that D-Er 2 Si 2 O 7 may be a candidate for further exploration related to the transverse field Ising model.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Non-uniform active learning for Gaussian process models with applications to trajectory informed aerodynamic databases

The ability to non-uniformly weight the input space is desirable for many applications, and has been explored for space-filling approaches. Increased interests in linking models, such as in a digital twinning framework, increases the need for sampling emulators where they are most likely to be evaluated. In particular, here we apply non-uniform sampling methods for the construction of aerodynamic databases. This paper combines non-uniform weighting with active learning for Gaussian Processes (GPs) to develop a closed-form solution to a non-uniform active learning criterion. We accomplish this by utilizing a kernel density estimator as the weight function. We demonstrate the need and efficacy of this approach with an atmospheric entry example that accounts for both model uncertainty as well as the practical state space of the vehicle, as determined by forward modeling within the active learning loop.

42 ENGINEERING↗
DOE OSTI2025

Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties

This project developed machine learning (ML) methods, lab data sets, and field data to advance geothermal exploration and geothermal energy production. The work had three focus areas. One involved the development of ML methods to use microearthquakes (MEQs) for imaging geothermal reservoir properties and improving subsurface characterization – most importantly the evolution of permeability within the evolving reservoir. This part of the work included development of ML approaches for automated MEQ location, focal mechanism determination and identification of earthquake precursors. The second area focused on using MEQ signals generated by geothermal exploration and production to predict the relationship between fluid injection and seismicity. Here, we extended to reservoir scale our success in using ML to predict laboratory earthquakes and fault zone stress state. The third focus area was on lab experiments. Here, we developed new ML models for lab earthquake prediction and identification of precursors to failure to improve earthquake forecasting and early warning in geothermal settings. Major outcomes of our work include ML models that learn from MEQ signals during geothermal exploration and production to predict induced seismicity. MEQs occur naturally in connection with drilling and energy production. We developed ML methods to use the seismic waves from these events to characterize the elastic, hydraulic and poromechanical properties of reservoirs. Our work illuminated fracture geometry and the evolution of fracture permeability by incorporating seismic coda wave analysis and ML methods to relate fluid injection and seismicity. We significantly expanded laboratory earthquake prediction to include methods that use both passive measurements of microearthquakes within the lab fault zones and also active source acoustic measurements of fault zone elastic properties. These methods can now predict fault zone stress state, time to failure and the magnitude of lab earthquakes. Our work showed that repetitive stick- slip failure events during frictional sliding (the lab equivalent of earthquakes) are preceded by a cascade of micro-failure events that radiate energy in a manner that foretells unstable failure – manifest as laboratory MEQs. We documented a mapping between fracture properties and statistical attributes of elastic radiation. We extended existing works to geothermal reservoir scale and developed ML methods to determine reservoir permeability, fracture properties, and their evolution during geothermal energy production. An attractive feature of ML algorithms is their ability to handle big datasets and reveal patterns and correlations that may remain invisible to conventional analyses. Our work connected data from field, laboratory and intermediate scales to study permeability, stress, strength, fracture stiffness and geometry. At the field scale we used data from the Newberry Volcano field site, UtahFORGE, EGS Collab, and also the Bedretto underground research lab in Switzerland. These data sets are bridging the gap between the lab scale, theory, and reservoir scale. Our work produced plain language summaries to improve public understanding of DOE research. We also developed openly distributed ML and seismicity datasets for use by all researchers and we published connections between induced seismicity in geothermal areas and reservoir properties including permeability, fracture properties, and stress state. Our models are designed for the large data sets of induced seismicity typically associated with geothermal sites. We produced labeled event catalogs and used them on geothermal data to assess how ML can facilitate geothermal production and exploration. All datasets are available on the GDR Productivity: The project produced 32 publications in peer reviewed journals (two are in review). It supported the work of 6 PhD students, 40 conference presentations, 6 keynote talks at national meetings, and mentoring and professional development for 4 postdoctoral fellows.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Theoretical modeling of a bottom-raised oscillating surge wave energy converter structural loadings and power performances

Here, this study presents theoretical formulations to evaluate the fundamental parameters and performance characteristics of a bottom-raised oscillating surge wave energy converter (OSWEC) device. Employing a flat plate assumption and potential flow formulation in elliptical coordinates, closed-form equations for the added mass, radiation damping, and excitation forces/torques in the relevant pitch-pitch and surge-pitch directions of motion are developed and used to calculate the system's response amplitude operator and the forces and moments acting on the foundation. The model is benchmarked against numerical simulations using WAMIT and WEC-Sim, showcasing excellent agreement. The sensitivity of plate thickness on the analytical hydrodynamic solutions is investigated over several thickness-to-width ratios ranging from 1:80 to 1:10. The results show that as the thickness of the benchmark OSWEC increases, the deviation of the analytical hydrodynamic coefficients from the numerical solutions grows from 3% to 25%. Differences in the excitation forces and torques, however, are contained within 12%. While the flat plate assumption is a limitation of the proposed analytical model, the error is within a reasonable margin for use in the design space exploration phase before a higher-fidelity (and thus more computationally expensive) model is employed. A parametric study demonstrates the ability of the analytical model to quickly sweep over a domain of OSWEC dimensions, illustrating the analytical model's utility in the early phases of design.

13 HYDRO ENERGY↗
DOE OSTI2026

Modeling rf sheath formation in turbulent tokamak boundary plasma

During ICRF antenna operation, complex interactions between turbulent density profiles, nonlinear RF sheaths, and RF-induced convective transport are observed to alter plasma density in the tokamak edge [D’Ippolito et al., Nucl. Fusion 38, 1543 (1998)]. In this work, we explore the physics of such interactions via numerical modeling, using a nonlinear EM/plasma/sheath code (VSim) and profiles obtained from a fluid plasma turbulence code (Hermes) in a 3D slab domain containing biased side-wall limiters. RF-rectified sheath formation on antenna and limiter surfaces is observed as electromagnetic waves launched by the antenna are refracted through the turbulent density profile. On transport timescales, such sheath potentials have been shown to influence both the mean species density and its RMS fluctuation spectrum [Smithe et al., these proceedings]. On the faster RF timescales, we demonstrate that the converse is also true – regions of high plasma density near material surfaces give rise to the highest sheath potential amplitudes. When density is turbulent and spatially nonuniform, localized regions of high sheath potential (hotspots) may develop where high-density filaments intersect material surfaces. Such hotspots are of particular concern as sources of impurity sputtering, and we explore their behavior in response to changes both to the local plasma density and to antenna operating parameters and structure. Related results exploring the role of Faraday shields and/or enclosing structures in suppressing high sheath potentials for other devices (e.g. SPARC) will also be shown.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful, scalable, and operationally reliable. Artificial intelligence and machine learning (AI/ML) are already embedded across DESC science workflows, from photometric redshifts and transient classification to weak lensing inference and cosmological simulations. Yet their utility for precision cosmology hinges on trustworthy uncertainty quantification, robustness to covariate shift and model misspecification, and reproducible integration within scientific pipelines. This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses, revealing that the same core methodologies and fundamental challenges recur across disparate science cases. Since progress on these cross-cutting challenges would benefit multiple probes simultaneously, we identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery. With an eye on emerging techniques, we also explore the potential of the latest foundation model methodologies and LLM-driven agentic AI systems to reshape DESC workflows, provided their deployment is coupled with rigorous evaluation and governance. Finally, we discuss critical software, computing, data infrastructure, and human capital requirements for the successful deployment of these new methodologies, and consider associated risks and opportunities for broader coordination with external actors.

Aubourg, Eric [APC, Paris] (ORCID:000000025592023X↗
DOE OSTI2024

Health and air pollutant emission impacts of net zero CO2 by 2050 scenarios from the energy modeling forum 37 study

Carbon dioxide and non-greenhouse gas air pollutants are emitted from many of the same sources. Decarbonization actions thus typically yield air pollutant emission reductions, resulting in significant air quality benefits. Although several studies have highlighted this connection, including in the context of net zero carbon emission targets, substantial uncertainty remains regarding how alternative technological pathways to this goal will affect the spatial distribution and magnitude of air pollutants. Comprehensive multi-model and multi-scenario analyzes are needed to explore the relative impacts of alternative pathways. Here, our study begins to address this gap by leveraging the results from the recent Energy Modeling Forum 37 inter-model comparison exercise on U.S. decarbonization pathways. Comparing the results of the six teams who submitted air pollutant emissions suggests that strategies that target net zero U.S. carbon emissions would yield significant reductions in many air pollutants, and that this finding is generally robust across pathways. However, some energy sources, such as biomass and fossil fuels with carbon capture, will emit air pollutants and can potentially influence the magnitude, spatial distribution, and even sign of localized air pollutant emission changes. In the second part of this analysis, a simplified air quality and health impacts screening model is used to evaluate the air quality impacts in 2035 of sectoral emission changes from the three models that provided sectoral detail. Relative to a reference scenario, a net zero pathway is estimated to reduce fine particulate matter concentrations across the contiguous U.S., with health benefits from reduced mortality ranging from $\$$65 billion to $\$$250 billion in 2035 alone (2023$\$$s). These benefits would be expected to grow over time as the net zero trajectory becomes more stringent. Both the magnitude of potential benefits and the substantial variation of the projections across models underscore the need for an EMF-like inter-model comparison exercise focused on air quality.

Air pollutants↗
DOE OSTI2024

AEOLUS: Advances in Experimental Design, Optimal Control, and Learning for Uncertain Complex Systems

The AEOLUS Center is dedicated to developing a unified optimization-under-uncertainty framework for (1) learning predictive models from data and (2) optimizing experiments, processes, and designs governed by these models, all driven by complex, uncertain energy systems. AEOLUS addressed the critical need for principled, rigorous, scalable, and structure-exploiting capabilities for exploring parameter and decision spaces of complex forward simulation models---the so-called outer loop. This report summarizes the work done under DE-SC0021077 on (1) nonlocal models for solidification problems, (2) a multifidelity method for a nonlocal diffusion model, and (3) multifidelity Monte Carlo methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Advances in Experimental Design, Optimal Control, and Learning for Uncertain Complex Systems (Final Report for AEOLUS)

The AEOLUS Center is dedicated to developing a unified optimization-under-uncertainty framework for: (1) learning predictive models from data; and (2) optimizing experiments, processes, and designs governed by these models, all driven by complex, uncertain energy systems. AEOLUS addresses the critical need for principled, rigorous, scalable, and structure-exploiting capabilities for exploring parameter and decision spaces of complex forward simulation models. This report summarizes the key highlights of our research during the period of performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

The Development of Kinetic and Radiation Hydrodynamics Modeling of Thermonuclear Burn Propagation in Isochoric p - 11 B Through the Support of the INFUSE Program

The report summarizes DOE INFUSE-supported work between HB11 Energy and the University of Rochester’s TriForce Institute to improve computational modeling of advanced fusion fuels, especially proton–boron-11 (p- 11 B). The project extended the TriForce particle-in-cell/Monte Carlo collision code to include physics needed for dense, high-temperature p- 11 B burn studies, including p- 11 B fusion reactions, three-alpha-particle reaction products, relativistic Coulomb collisions, large-angle nuclear scattering, bremsstrahlung radiation, inverse bremsstrahlung absorption, and photon transport. The upgraded models were verified against focused physics tests and against known deuterium–tritium burn behavior. The study then used one-dimensional spherical simulations to estimate the conditions required for thermonuclear burn propagation in isochoric p- 11 B fuel. The calculations found that burn propagation is possible in the model, but only under very extreme hot-spot conditions, such as about 7000 g/cm 3 at 500 keV or 9000 g/cm 3 at 300 keV for a 20-micron hot spot. These conditions are much more demanding than current demonstrated inertial confinement fusion hot spots. The report concludes that the INFUSE collaboration successfully advanced kinetic and radiation modeling capabilities for p- 11 B fusion and provided useful estimates of ignition requirements. However, the simulated fuel gains remain below what would be needed for practical inertial fusion energy, and further work is needed to reconcile differences among kinetic, radiation-hydrodynamic, and analytic models and to identify more achievable target designs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Quantum simulations in effective model spaces: Hamiltonian-learning variational quantum eigensolver using digital quantum computers and application to the Lipkin-Meshkov-Glick model

Quantum simulations offer the potential to predict the structure and dynamics of nuclear many-body systems that are beyond the capabilities of classical computing. Generally, preparing the ground state of strongly-interacting many-body systems relevant to nuclear physics is however inefficient, even using ideal quantum computers. In addition, currently available NISQ-era quantum devices possess modest numbers of qubits, limiting the size of quantum many-body systems that can be simulated. In this context, a reformulation of the quantum many-body problems using truncated model spaces and Hamiltonians is desirable to make them more amenable to near-term quantum computers. The importance of symmetries in low-energy theories, including effective field theories (EFTs), lattice quantum chromodynamics (QCD), and effective model spaces for nuclear systems, in particular their interplay with the reduction of active Hilbert spaces, is well known. Lesser known is the fact that the non-commutivity of some symmetries and truncations of the model space can be profitably combined with variational calculations to rearrange the entanglement into localized structures and enable more efficient simulations. Here, the goal of the present study is to explore and utilize the non-commutivity of symmetries and model-space truncations of quantum many-body systems important to nuclear physics, particularly in combination with variational algorithms for quantum simulations and effective Hamiltonian learning. We introduce an iterative hybrid classical-quantum algorithm, Hamiltonian learning variational quantum eigensolver (HL-VQE), that simultaneously optimizes an effective Hamiltonian, thereby rearranging entanglement into the effective model space, and the associated ground-state wavefunction. Quantum simulations, using classical computers and IBM's superconducting-qubit quantum computers, are performed to demonstrate the HL-VQE algorithm, in the context of the Lipkin-Meshkov-Glick (LMG) model of interacting fermions, where the Hamiltonian transformation corresponds to an orbital rotation. We use a mapping where the number of qubits scales with the $\log$ of the size of the effective model space, rather than the particle number. HL-VQE is found to provide an exponential improvement in LMG-model calculations of the ground-state energy and wavefunction, compared to naive truncations without Hamiltonian learning, throughout a significant fraction of the Hilbert space. In the context of EFT, this corresponds to counterterms scaling exponentially with the cut-off as opposed to power law. Implementations on IBM's QExperience quantum computers and simulators for 1- and 2-qubit effective model spaces are shown to provide accurate and precise results, reproducing classical predictions. For a range of parameters defining the LMG model, the HL-VQE algorithm is found to have better scaling of quantum resources requirements than previously explored algorithms. In particular, the HL-VQE scales efficiently over a large fraction of the model space, in contrast to VQE alone. This work constitutes a step in the development of entanglement-driven quantum algorithms for descriptions of nuclear many-body systems. This, in part, leverages the potential of noisy intermediate-scale quantum (NISQ) devices. The exponential scaling of counterterms observed in this study suggests the possibility of more general applicability to other non-perturbative EFTs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Charting the state of GEMs in microalgae: progress, challenges, and innovations

Genome-scale metabolic models (GEMs) provide a systems-level framework for understanding and engineering microalgal metabolism. This review explores the evolution of GEMs in microalgae, highlighting advances in light modeling, automation, and multi-omics integration. Special emphasis is placed on Chlamydomonas reinhardtii as a model species. Limitations of current models, particularly for microalgae, are discussed, alongside promising developments in dynamic modeling and machine learning. Together, these innovations chart a path toward more predictive, adaptable GEMs that can accelerate biotechnological applications of microalgae in sustainable production systems.

Plant Sciences↗
DOE OSTI2019

Deep Direct-Use Feasibility Study Numerical Modeling and Uncertainty Analysis using iTOUGH2 for West Virginia University

To reduce the geothermal exploration risk, a feasibility study is performed for a deep direct-use system proposed at the West Virginia University (WVU) Morgantown campus. This study applies numerical simulations to investigate reservoir impedance and thermal production. Because of the great depth of the geothermal reservoir, few data are available to characterize reservoir features and properties. As a result, the study focuses on the following three aspects: 1. model choice for predicting reservoir impedance and thermal breakthrough: after investigating three potential models (one single permeability model and two dual permeability models) for flow through fractured rock, it is decided to use single permeability model for further analysis; 2. well placement (horizontal vs. vertical) options: horizontal well placement seems to be more robust to heterogeneity and the impedance is more acceptable; 3. Prediction uncertainty: the most influential parameters are identified using a First-Order-Second-Moment uncertainty propagation analysis, and the uncertain range of the model predictions is estimated by performing a Monte Carlo simulation. Heterogeneity has a large impact on the perdition, therefore, is considered in the predictive model and uncertainty analysis. The numerical model results and uncertainty analysis are used for economic analysis. The dataset submitted here support the described study. Manuscript is submitted to Geothermics, will be linked once paper is accepted.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Manning condensation in ion exchange membranes: A review on ion partitioning and diffusion models

Here, the rational design of ion exchange membranes (IEMs) is becoming more pertinent as their usage becomes broader and as their staple applications (i.e., electrodialysis, flow batteries, and fuel cells) improve in commercial viability. Such efforts would be catalyzed by an improved fundamental understanding of ion transport in IEMs. This review discusses recent progress in modeling ion partitioning and diffusion in IEMs in an effort to relate IEM performance metrics to fundamental membrane properties over which researchers and membrane manufacturers possess direct and sometimes precise control. Central focus is given to the Donnan-Manning model for ion partitioning and the Manning-Meares model for ion diffusion in IEMs. These two frameworks, which are derived from Manning's counter-ion condensation theory for polyelectrolyte solutions, have been widely used within the IEM literature since their recent introduction. To explore this topic, the mathematical derivation of both models is revisited, followed by a survey of experimental and computational discussions of counter-ion condensation in IEMs. Alternative models which fulfill similar roles in predicting IEM transport properties are compared. This review concludes by highlighting the uniquely favorable positions of the Donnan-Manning and Manning-Meares models and discussing their prospects as leading predictors of IEM partitioning and diffusive properties.

25 ENERGY STORAGE↗
DOE OSTI2020

Electronic structure calculations of twisted multi-layer graphene superlattices

Quantum confinement endows two-dimensional (2D) layered materials with exceptional physics and novel properties compared to their bulk counterparts. Although certain two- and few-layer configurations of graphene have been realized and studied, a systematic investigation of the properties of arbitrarily layered graphene assemblies is still lacking. We introduce theoretical concepts and methods for the processing of materials information, and as a case study, apply them to investigate the electronic structure of multi-layer graphene-based assemblies in a high-throughput fashion. We provide a critical discussion of patterns and trends in tight binding band structures and we identify specific layered assemblies using low-dispersion electronic bands as indicators of potentially interesting physics like strongly correlated behavior. A combination of data-driven models for visualization and prediction is used to intelligently explore the materials space. This work more generally aims to increase confidence in the combined use of physics-based and data-driven modeling for the systematic refinement of knowledge about 2D layered materials, with implications for the development of novel quantum devices.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Deep Learning Scene Classification Experiments in Automatic Detection of Slums on Planetscope Imagery

Population growth is increasingly happening in slum settlements of the large urban centers in the Global South. The term "slum" encompasses a wide range of communities, located mostly in underserved areas, and often exhibiting distinct structural and functional informalities with a relatively high concentration of marginalized populations. To address the issues confronting slums for effective planning and development, including the realistic estimation of the resident population, identifying them accurately is fundamental. Given the disagreements over a universal definition, diverse characteristic features, and socio-political limitations, global detection of slums is a veritable challenge. In this paper, we present experiments in slum detection using a scene classification algorithm and 3-meter spatial resolution satellite imagery. We train and evaluate the model for slum detection in Mumbai, India for the year 2023 and test the temporal generalization of the trained model on Mumbai in 2020 and 2018. In addition, we explore the pathways toward geographic generalization to Kolkata and Delhi (India). We discuss several limitations in the workflow and model, situate our findings in the existing literature, and suggest improvements and alternatives. With this, we establish baseline methods and experiments as a first step towards developing an image-based global slum detection framework and algorithm. This work adds to the community discussion on methods, data challenges, and open questions related to the detection of slums globally. With this research, we hope to improve our understanding of human settlements, especially in critical areas, improve population estimates, and help measure progress towards the sustainable development goals.

Arndt, Jacob↗