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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Covalently-Bonded Laminar Assembly of Van der Waals Semiconductors with Polymers: Toward High-Performance Flexible Devices

Van der Waals semiconductors (vdWS) offer superior mechanical and electrical properties and are promising for flexible microelectronics when combined with polymer substrates. However, the self-passivated vdWS surfaces and their weak adhesion to polymers tend to cause interfacial sliding and wrinkling, and thus, are still challenging the reliability of vdWS-based flexible devices. Here, in this study, an effective covalent vdWS–polymer lamination method with high stretch tolerance and excellent electronic performance is reported. Using molybdenum disulfide (MoS 2 ) and polydimethylsiloxane (PDMS) as a case study, gold–chalcogen bonding and mercapto silane bridges are leveraged. The resulting composite structures exhibit more uniform and stronger interfacial adhesion. This enhanced coupling also enables the observation of a theoretically predicted tension-induced band structure transition in MoS 2 . Moreover, no obvious degradation in the devices’ structural and electrical properties is identified after numerous mechanical cycle tests. This high-quality lamination enhances the reliability of vdWS-based flexible microelectronics, accelerating their practical applications in biomedical research and consumer electronics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Stochastic Optimization and Uncertainty Quantification of Natrium-based Nuclear-Renewable Energy Systems for Flexible Power Applications in Deregulated Markets

Rapid integration of variable renewable energy sources (VRES) has made modeling and stochastic optimization of hybrid energy systems crucial for studying their long-term performance and viability. However, most studies have focused on just historical data, which may be unreliable for capturing short-term fluctuations, rare events, and long-term patterns of energy demand, price, and the variability of renewable energy sources. For this study, optimal synthetic time series models were developed using Wasserstein distance. The models were validated by comparing the key statistical measures against those of the historical data. They were then used to optimize the integrated Natrium-style advanced energy systems and their long-term (30 years) economics. The stochastic model performs bi-level optimization to find the optimal sizes for the balance of plant and thermal energy storage, while also optimizing energy dispatch to achieve the maximum net present value. In studies of two deregulated markets (California ISO and the Electric Reliability Council of Texas), the integrated Natrium-style system performed better in CAISO than in ERCOT, given higher and more consistent electricity prices during peak-demand periods. The potentially enlarged cost associated with the variable operation and maintenance of the TES system also plays a significant role in driving the system sizing, thus its impacts on the system are investigated in detail through comparison against a baseline case. The study also finds that the bi-level optimization results based on stochastic gradient descent closely match the grid search results. The uncertainty quantification of the stochastic signals provides further NPV-related insights and probability distributions for the case studies. The normal standard error of the mean of NPV for the case with and without TES VOM for CAISO were found to be 7.73M (plus-minus sign) 1.09M USD and 104.99M (plus-minus sign) 1.25M USD, respectively based on a 95% confidence. Given the relatively small NPV variance based on 150 samples, the analysis affords the most robust possible prediction of the techno-economic performance of the integrated Natrium-style energy systems.

25 ENERGY STORAGE↗
DOE OSTI2021

Power Grid Reliability Estimation via Adaptive Importance Sampling

Electricity production currently generates approximately 25% of greenhouse gas emissions in the USA. Thus, increasing the amount of renewable energy is a key step to carbon neutrality. However, integrating a large amount of fluctuating renewable generation is a significant challenge for power grid operating and planning. Grid reliability, i.e., an ability to meet operational constraints under power fluctuations, is probably the most important of them. In this letter, we propose computationally efficient and accurate methods to estimate the probability of line overflow, i.e., reliability constraints violation, under a known distribution of renewable energy generation. To this end, we investigate an importance sampling approach, a flexible extension of Monte-Carlo methods, which adaptively changes the sampling distribution to generate more samples near the reliability boundary. The approach allows to estimate overload probability in real-time based only on a few dozens of random samples, compared to thousands required by the plain Monte-Carlo. Our study focuses on high voltage direct current power transmission grids with linear reliability constraints on power injections and line currents. Herein, we propose a novel theoretically justified physics-informed adaptive importance sampling algorithm and compare its performance to state-of-the-art methods on multiple IEEE power grid test cases.

power system faults↗
DOE OSTI2023

Recent Beam Stability Analysis for the EIC

This document summarizes work done in WBS 6.02.02 during FY23. Additional details can be found in the EIC overview paper presented at IPAC’23 [1], and the references therein. Progress has been made in the ESR and HSR lattice design, dynamic aperture optimization, and the RCS lattice design. The impedance and Collective effects are progressing and include the impedance optimization process of the vacuum systems for RCS, HSR, and ESR; collective effects studies; collective effects and beam-beam interaction; coupled bunch instabilities and the crab cavities; low-level RF feedback system design and beam-ion instability. The reversed phasing RF system has been numerically studied for the ESR to mitigate Robinson instability and demonstrate reliable stable beam operation. Various codes, including C++, SPACE [2], ELEGANT [3], and Mbtrack2 [4] have been employed to benchmark the results. The simulation of HSR bunch splitting with beam loading has been performed at 275 GeV energy. The evaluation of Beam Position Monitors aimed to validate their expected performance, with the primary objective being to verify their accuracy. Calculations of Electron Polarization in the ESR, RCS, and HSR are showing good progress. In the RCS, preliminary studies indicate excellent polarization transmission over intrinsic spin resonances, achieving over 90% transmission with improved performance compared to the previous lattice. Preliminary simulations in the ESR indicate encouraging results in minimizing depolarization and improving equilibrium polarization.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Future Low-Carbon Technology Options for a 100 Percent Decarbonized Power Sector

Recent studies have modeled 100 percent decarbonized or renewable power sectors and proposed technical solutions to address costs, reliability, and other challenges, specifically related to meeting the last 10 percent of energy demand and eliminating carbon dioxide (CO 2 ) and other greenhouse gas (GHG) emissions. Here, this article summarizes multiple studies and provides references to the detailed and nuanced scenarios of reliably and cost-effectively operating 100 percent clean and/or renewable power systems. It also outlines future technology solutions that might be needed to meet this simply stated but deeply complex goal.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Towards time-resolved MicroED grid preparation using mix-and-inject gas dynamic virtual nozzles

Recent progress in gas dynamic virtual nozzle (GDVN) technologies in combination with high-brilliance synchrotron and X-ray free-electron lasers (XFELs) has allowed the visualization of protein dynamics in crystallo by mixing macromolecular protein crystals with a substrate using tunable mixing times on the order of milliseconds to seconds prior to serial X-ray diffraction data collection. This has become the method of choice for high-resolution structure determination of intermediate states. However, such experiments require large counts of crystals of proper sizes for high-resolution data collection, and premium beam times for screening efforts. Cryogenic microcrystal electron diffraction (MicroED) represents a complementary technique that may be a more accessible avenue for time-resolved nanocrystallography compared with serial X-ray diffraction experiments. MicroED can produce full diffraction datasets from just a few submicrometre-thick crystals, and the approach is more readily accessible, requiring standard cryogenic transmission electron microscopy (TEM) equipment available at many universities and institutes. Cryogenic MicroED, like other forms of cryo-EM, begins with rapidly freezing biological material on electron microscopy grids. In the case of MicroED, micro- to nano-crystals (<500 nm thick) are deposited onto electron microscopy grids and plunge-frozen for subsequent electron diffraction data collection. Here, we have incorporated GDVN technology developed originally for XFEL experiments into the freezing process as a first step towards time-resolved studies. We describe the limited deposition efficiency of the model MicroED protein proteinase K on TEM grids using GDVNs, preceding sample vitrification and successful MicroED data collection. We discuss both the initial results from such experiments and the methodological challenges in developing this approach into a reliable workflow for millisecond-to-second time-resolved structural studies of macromolecules. Our results promise a strategy to deposit crystals on grids using GDVNs and determine high-resolution structures by MicroED, constituting a first step towards development of time-resolved MicroED experiments.

MicroED↗
DOE OSTI2024

Probabilistic Sizing of Energy Storage Systems for Reliability and Frequency Security in Wind-Rich Power Grids

The penetration of wind energy has increased significantly in the power grid in recent times. Although wind is abundant, environment-friendly, and cheap, it is variable in nature and does not contribute to system inertia as much as conventional synchronous generators. Coupled with the low inertia contribution, the generation intermittency of wind power leads to reliability and stability issues in the power system. Energy storage systems (ESSs) are among the most prominent alternatives to alleviate these concerns associated with high wind penetration. This paper proposes a planning strategy to size ESS for the reliability and frequency security of wind-rich power grids. A probabilistic methodology for ESS sizing is developed utilizing a composite reliability-based framework with sequential Monte Carlo simulation (MCS). The MCS generates composite reliability indices for the power system, which are employed to obtain the capacity for a reliability energy storage system (RESS). Simultaneously, the MCS-derived probability of synchronization of conventional generators is integrated into an analytical approach for sizing a frequency support energy storage system (FESS). The effect of wind farm dispersion across geographical regions is incorporated in the framework to study possible reductions in the ESS size while maintaining the system reliability and frequency security. Furthermore, the efficacy of the proposed strategy is demonstrated on the RTS-GMLC test system.

25 ENERGY STORAGE↗
DOE OSTI2023

A net-zero emissions strategy for China’s power sector using carbon-capture utilization and storage

Decarbonized power systems are critical to mitigate climate change, yet methods to achieve a reliable and resilient near-zero power system are still under exploration. This study develops an hourly power system simulation model considering high-resolution geological constraints for carbon-capture-utilization-and-storage to explore the optimal solution for a reliable and resilient near-zero power system. This is applied to 31 provinces in China by simulating 10,450 scenarios combining different electricity storage durations and interprovincial transmission capacities, with various shares of abated fossil power with carbon-capture-utilization-and-storage. Here, we show that allowing up to 20% abated fossil fuel power generation in the power system could reduce the national total power shortage rate by up to 9.0 percentages in 2050 compared with a zero fossil fuel system. A lowest-cost scenario with 16% abated fossil fuel power generation in the system even causes 2.5% lower investment costs in the network (or $16.8 billion), and also increases system resilience by reducing power shortage during extreme climatic events.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

State-of-the-art and review of condensation heat transfer for small modular reactor passive safety: Computational studies

The small modular reactor (SMR) is a promising option with added safety features, economical manufacturing, reliable parts, portability, and scalable energy capacity that emits no greenhouse gas during its operating lifespan. The SMR safety systems, however, need to be evaluated for design and licensing. Thus, they require the verification and validation of the physics models and correlations. This study focuses on state-of-the-art condensation heat transfer analysis and a review of previous studies related to the passive containment cooling system (PCCS) of a SMR. In the PCCS of a SMR, due to its smaller size containment, filmwise condensation is dominant and therefore emphasized in this study. Furthermore, previous condensation heat transfer studies for PCCSs did not make the SMR the primary focus, so a critical review for formulating the state-of-the-art is necessary. A previous review covered experimental condensation heat transfer studies with a brief overview of associated test facilities and empirical correlations. This review covers the empirical, resistance-layer and theoretical (numerical and commercial CFD) approaches.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

3DBFSVBF (3D BatFinder Smart Video BioFilter and Multi-class BatFinder Smart Video BioFilter) [SWR-22-88]

Bats are notoriously difficult to study, therefore, identifying specific behavioral trends and the precise environmental conditions at the time of collision requires a monitoring solution that can reliably collect relevant data. To date, thermal infrared video surveillance has been extensively applied to study bats and has proven to be a powerful yet cumbersome tool. Current analytical approaches are time consuming because data processing data has not been fully automated. In the past, steps have been taken to record avian and bat activity in conjunction with complicated image processing techniques that separate species from other moving objects within the field of view (i.e. clouds and portions of the wind turbine). Once the videos are collected, the post-processing does not allow real time monitoring and identification, leading to a delay in both studying the behavior of these species and determining the effectiveness of any impact reduction strategy being studied. Moreover, object identification capability is lacking, thus limiting the usefulness of video data. To resolve these issues, we are using open source 3D computer vision and machine learning techniques allowing for automatic detection of objects in real-time with the ability to correlate these objects with environmental variables and recording the flight paths of each object. The machine learning has been trained on 3D data and allows for automated real-time data collection, identification and tracking, thereby eliminating the need for long and tedious post-analysis processing of the videos. This machine learning model is an added feature to the previous BatFinder Smart Video BioFilter and increases the accuracy of that systems classification by increasing the accuracy of identifying bats (90% accuracy) and insects (69% accuracy) to a 97% accuracy. There are two object classifier machine learning models, Binary and multi-classification. Binary object classifier labeled BatFinder_Smart_Video_BioFilter.h5 distinguishes between biological objects and non-biological objects. The main goal of this object classifier is to ignore the turbine blades while detecting biological object flying withing the rotor swept area of the turbine. Non-biological objects have a probability of 0 and biological objects have a probability of 1. Multi-classifier labeled Multiclass_BatFinder_Smart_Video_BioFilter.h5 distinguishes between bats, birds, insects and non-biological.

Yarbrough, John↗
DOE OSTI2022

BFSVBF (BatFinder Smart Video BioFilter) [SWR-22-87] and Multi-class BatFinder Smart Video BioFilter Keras

Bats are notoriously difficult to study, therefore, identifying specific behavioral trends and the precise environmental conditions at the time of collision requires a monitoring solution that can reliably collect relevant data. To date, thermal infrared video surveillance has been extensively applied to study bats and has proven to be a powerful yet cumbersome tool. Current analytical approaches are time consuming because data processing data has not been fully automated. In the past, steps have been taken to record avian and bat activity in conjunction with complicated image processing techniques that separate species from other moving objects within the field of view (i.e. clouds and portions of the wind turbine). Once the videos are collected, the post-processing does not allow real time monitoring and identification, leading to a delay in both studying the behavior of these species and determining the effectiveness of any impact reduction strategy being studied. Moreover, object identification capability is lacking, thus limiting the usefulness of video data. To resolve these issues, we are using open source computer vision and machine learning techniques allowing for automatic detection of objects in real-time with the ability to correlate these objects with environmental variables and recording the flight paths of each object. The code has gone through five rounds of development with images used to train the models. This advancement allows for automated real-time data collection, identification, and tracking, thereby eliminating the need for long and tedious post-analysis processing of the videos. We will discuss the two open source and publicly available machine learning models developed within this scope of this work: 1) a binary model with a 97.5% accuracy in identifying the difference between an object and an empty scene, including wind turbine and clouds; and 2) a multiple classification model with the capability of identifying the type of object detected: bats (90% accuracy), birds (83% accuracy), insects (69% accuracy) and non-biological (99% accuracy).

Yarbrough, John↗
DOE OSTI2022

Extreme events, energy security and equality through micro- and macro-levels: Concepts, challenges and methods

Low-income households face long-standing challenges of energy insecurity and inequality (EII). During extreme events (e.g., disasters and pandemics) these challenges are especially severe for vulnerable populations reliant on energy for health, education, and well-being. However, many EII studies rarely incorporate the micro- and macro-perspectives of resilience and reliability of energy and internet infrastructure and social-psychological factors. To remedy this gap, we first address the impacts of extreme events on EII among vulnerable populations. Second, we evaluate the driving factors of EII and how they change during disasters. Third, we situate these inequalities within broader energy systems and pinpoint the importance of equitable infrastructure systems by examining infrastructure reliability and resilience and the role of renewable technologies. Then, we consider the factors influencing energy consumption, such as energy practices, socio-psychological factors, and internet access. Finally, we propose interdisciplinary research methods to study these issues during extreme events and provide recommendations.

Chen, CF↗
DOE OSTI2025

Bayesian modeling of traffic-related air pollutants: A case study of urban transportation and air quality dynamics in Columbia, South Carolina

Traffic emissions significantly impact near-road air quality and public health. This research applies a Bayesian modeling framework to investigate these impacts using high-resolution traffic and air pollutant data from an urban corridor in Columbia, South Carolina. Despite a data collection period truncated by the COVID-19 lockdown, the Bayesian approach successfully identified significant predictors and quantified model uncertainty. Employing Bayesian Model Selection and Averaging enhanced prediction accuracy and evaluated model uncertainty. Findings indicate that higher temperatures and increased moisture levels elevate particulate matter (PM 1.0 , PM 2.5 , PM 10 ) concentrations, while traffic speed significantly affects nitrogen dioxide (NO 2 ) levels. Specifically, higher average traffic speeds (indicative of smoother flow) correspond to lower NO 2 concentrations, suggesting that less congested conditions reduce NO 2 emissions. This study highlights the robustness of Bayesian methods for generating reliable air quality insights even under data-constrained conditions. The findings underscore the importance of traffic flow management (e.g., reducing congestion) for mitigating near-road NO 2 exposure and provide a basis for developing targeted public health strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Power generation-cooling water Nexus: Impacts of cooling water shortage on power system operation - a simulation case study in Illinois, U.S

Cooling water shortage, frequently attributed to drought and heat waves, poses a significant threat to the operations of thermoelectric power plants and further poses a challenge for the entire power system and environmental stakeholders. Recognizing the critical nexus between power generation and cooling water availability and the potential ability of power generations to adjust generation schedules during cooling water shortages, this paper introduces a security-constrained unit commitment and economic dispatch model considering water-energy nexus. In specific, the model is augmented with a unit-level cooling water requirement (CWR) model and multi-level cooling water availability (CWA) constraints. The unit-level CWR model quantifies the cooling water withdrawal per MWh of power generation, taking into account factors such as thermoelectric generation technologies, cooling system technologies, and environmental parameters. The multi-level CWA constraints incorporate pump-level, plant-level, watershed-level, and forced minimum power constraints, utilizing data derived from actual-based cooling water shortage scenarios. Using a simulation case study in Illinois, United States, this research examines the reliability, economic, and environmental implications of cooling water shortages on power system operations. The results show that Illinois may experience 10-15% daily load curtailment and severe congestion between certain regions from the east to central during cooling water shortages, while once-through and wet-tower units experience a 52% and 17% reduction in power generation. In conclusion, overall cooling water withdrawal decreases by 24-38% as severity intensifies.

Cooling water shortage↗
DOE OSTI2024

The solubility of La hydroxide and stability of La 3+ and La hydroxyl complexes at acidic to mildly acidic pH from 25 to 250 °C

The mobility of rare earth elements (REE) in natural hydrothermal systems can be assessed using geochemical modeling, which requires reliable thermodynamic data of relevant aqueous species. In this study, we evaluate the controls of pH and temperature on La speciation and the role of hydroxyl complexes in REE transport at hydrothermal conditions. Batch-type hydrothermal solubility experiments were conducted using synthetic La hydroxide powders equilibrated in perchloric acid-based aqueous solutions at temperatures between 150 and 250 °C and starting pH of 2 to 5. The La hydroxide solubility is retrograde with temperature and displays a strong pH dependence with a decrease in La concentrations from acidic to mildly acidic pH spanning between 3 and 5 orders of magnitude (e.g. log La molality of –2.5 to –7.2 at 250 °C). Thermodynamic optimizations using GEMSFITS allow to retrieve the standard partial molal Gibbs energies for the La 3+ aqua ion and the formation constants for the La hydroxyl species (i.e., LaOH 2+ , La(OH) 2 + , La(OH) 3 0 ) between 25 and 250 °C. A comparison between the experimentally derived thermodynamic properties with the calculated values from the Helgeson-Kirkham-Flowers equation of state parameters indicates an increased divergence with temperature. Discrepancies in standard partial molal Gibbs energies range between ~ 1 – 12 kJ/mol and result in a predicted La hydroxide solubility differing by up to 3 orders of magnitude at 250 °C. Speciation calculations indicate a higher stability of La 3+ and LaOH 2+ over the other La hydroxyl species in the studied pH range of 3.4 to 6. Here, the optimized thermodynamic properties for La aqueous species have important implications for modeling the solubility of REE minerals such as monazite and the mobility of REE in hydrothermal systems.

58 GEOSCIENCES↗
DOE OSTI2020

Mercury linked to Deccan Traps volcanism, climate change and the end-Cretaceous mass extinction

Mercury (Hg) anomalies linked to Large Igneous Provinces (LIP) volcanism have been identified in sediments across all five major mass extinctions in Earth's history. This study tests whether Hg in marine sediments is a reliable proxy linking Deccan Traps volcanic eruptions to late Maastrichtian global climate warming and the mass extinction at the Cretaceous-Paleogene boundary (KPB). Our primary test site is the Elles section in Tunisia, the auxiliary Global Stratotype Section and Point (GSSP) to El Kef. Elles has the most complete marine sedimentary record and a high average sedimentation rate of ~4.7 cm/ky. Here, we chose the Hor Hahar section in Israel to corroborate the geographic distribution of Hg fallout from Deccan volcanism. Reliability of the Hg proxy over the last 550 ky of the Maastrichtian to early Danian was evaluated based on high-resolution age control (orbital cyclostratigraphy), stable isotope climate record, Hg concentrations, biotic turnover and mass extinction. These results were correlated with the pulsed Deccan eruptive history constrained previously by U-Pb zircon geochronology. Our results support Hg as robust proxy for Deccan volcanism with large Hg spikes marking “extreme event” (EE) pulsed eruptions correlative with climate warming peaks separated by steady, less intense eruptions. Long-term global climate warming began near ~350 ky pre-KPB, reached maximum warming (3–4 °C) between 285 and 200 ky pre-KPB, followed by gradual cooling and rapid temperature drop between 45 and 25 ky pre-KPB. During the last 25 ky before the KPB, multiple Hg EE eruptions correlate with hyperthermal warming that culminated in the rapid mass extinction at Elles during ≤1000 years of the Cretaceous. These latest Cretaceous Hg peaks may correlate with massive, distal, Deccan-sourced lava flows (>1000 km long) that traversed the Indian subcontinent and flowed into the Bay of Bengal, bracketing the mass extinction. These results support Deccan volcanism as a primary driver of the end-Cretaceous mass extinction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

The past, present, and future of peaking thermal power plants in the United States

Today's power systems rely on "peaker plants" to reliably serve load during peak demand periods. In this study we consider the present competitiveness of different peaking options, how much and what kinds of plants have provided U.S. peaking capacity, and potential future peaking fleet compositions. We explore how capital intensity impacts the breakeven capacity factor between two potential resources: combustion turbines (CTs) and combined cycle plants (CCs). CTs outcompete CCs below 12%-17% annual capacity factor at today's prices, but can shift with changes to fuel or start costs. Historically, gas CTs and petroleum steam plants most closely fit the role of peakers. Peaking capacity could grow from approximately 280 GW today to 460-770 GW in 2050 composed of a wider range of resources. We conclude by discussing implications of this shift, with a focus on the potential planning considerations for shifting to a greater utilization of CCs for peaking needs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons

Neural networks (NNs) are currently changing the computational paradigm on how to combine data with mathematical laws in physics and engineering in a profound way, tackling challenging inverse and ill-posed problems not solvable with traditional methods. However, quantifying errors and uncertainties in NN-based inference is more complicated than in traditional methods. This is because in addition to aleatoric uncertainty associated with noisy data, there is also uncertainty due to limited data, but also due to NN hyperparameters, overparametrization, optimization and sampling errors as well as model misspecification. Although there are some recent works on uncertainty quantification (UQ) in NNs, there is no systematic investigation of suitable methods towards quantifying the total uncertainty effectively and efficiently even for function approximation, and there is even less work on solving partial differential equations and learning operator mappings between infinite-dimensional function spaces using NNs. In this work, we present a comprehensive framework that includes uncertainty modeling, new and existing solution methods, as well as evaluation metrics and post-hoc improvement approaches. Further, to demonstrate the applicability and reliability of our framework, we present an extensive comparative study in which various methods are tested on prototype problems, including problems with mixed input-output data, and stochastic problems in high dimensions. In the Appendix, we include a comprehensive description of all the UQ methods employed. Further, to help facilitate the deployment of UQ in Scientific Machine Learning research and practice, we present and develop in [1] an open-source Python library (github.com/Crunch-UQ4MI/neuraluq), termed NeuralUQ, that is accompanied by an educational tutorial and additional computational experiments.

11 physics-informed neural networks↗