Search NASA⌕ Search

SEARCH · Search NASA

Results for “Baltimore”

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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Mapping the Perseus galaxy cluster with XRISM: Gas kinematic features and their implications for turbulence

We present extended gas kinematic maps of the Perseus cluster based on a combination of five new XRISM/Resolve pointings observed in 2025 with four performance verification datasets from 2024, totaling a net exposure of 745 ks. To date, Perseus remains the only cluster that has been extensively mapped out to ≃0.7 r 2500 by XRISM/Resolve, while simultaneously offering sufficient spatial resolution to resolve gaseous substructures driven by mergers and active galactic nucleus (AGN) feedback. Our observations cover multiple radial directions and a broad range of dynamical scales, enabling us to characterize the kinematic properties of the intracluster medium up to a scale of ∼500 kpc. In the measurements, we detected high-velocity dispersions (≃300km s −1 ) in the eastern region of the cluster that are spatially coincident with the extended X-ray surface brightness excess and correspond to a nonthermal pressure fraction of ≃7 − 13%. The velocity field outside the AGN-dominant region can be effectively described by a single, large-scale kinematic driver based on the velocity structure function, which statistically favors an energy injection scale of at least a few hundred kpc. The estimated turbulent dissipation energy is comparable to the gravitational potential energy released by a recent merger, implying a significant role of turbulent cascade in the merger energy conversion. In the bulk velocity field, we observed a dipole-like pattern along the east-west direction with an amplitude of ≃ ± 200 − 300 km s −1 , indicating rotational motions induced by the recent merger event. This feature constrains the viewing direction to ≃30° −50° relative to the normal of the merger plane. Our hydrodynamic simulations suggest that Perseus has experienced at least two energetic mergers since redshift z ∼ 1, the most recent of which is associated with the radio galaxy IC310, in agreement with recent SRG/eROSITA findings. This study showcases exciting scientific opportunities for future missions with high-resolution spectroscopic capabilities (e.g., HUBS, LEM, and NewAthena).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

From disorganized data to emergent dynamic models: Questionnaires to partial differential equations

Starting with sets of disorganized observations of spatially varying and temporally evolving systems, obtained at different (also disorganized) sets of parameters, we demonstrate the data-driven derivation of parameter dependent, evolutionary partial differential equation (PDE) models capable of generating the data. This tensor type of data is reminiscent of shuffled (multidimensional) puzzle tiles. The independent variables for the evolution equations (their “space” and “time”) as well as their effective parameters are all emergent , i.e. determined in a data-driven way from our disorganized observations of behavior in them. We use a diffusion map based questionnaire approach to build a smooth parametrization of our emergent space/time/parameter space for the data. This approach iteratively processes the data by successively observing them on the “space,” the “time” and the “parameter” axes of a tensor. Once the data become organized, we use machine learning (here, neural networks) to approximate the operators governing the evolution equations in this emergent space. Our illustrative examples are based (i) on a simple advection–diffusion model; (ii) on a previously developed vertex-plus-signaling model of Drosophila embryonic development; and (iii) on two complex dynamic network models (one neuronal and one coupled oscillator model) for which no obvious smooth embedding geometry is known a priori. This allows us to discuss features of the process like symmetry breaking, translational invariance, and autonomousness of the emergent PDE model, as well as its interpretability.

generative models↗
DOE OSTI2025

Formation of a simple cubic antiferromagnet through charge ordering in a double Dirac material

Following the topological classification of electronic phases, interest has grown in materials with unique electronic or magnetic properties driven by topology and interactions. Here, in this study, we report that the topologically nontrivial mixed valent intermetalllic EuPd 3 S 4 undergoes long-range charge ordering at 𝑇 𝐶⁢𝑂 = 340 K wherein 𝐽 = 7/2 Eu 2+ and Van Vleck 𝐽 = 0 Eu 3+ ions on a body-centered-cubic lattice separate into two interpenetrating simple cubic sublattices. The reduced symmetry transmutes 8-fold double Dirac states into 4-fold Dirac states and leads to a simple cubic Heisenberg antiferromagnet with G-type antiferromagnetic order for 𝑇 <⁢ 𝑇 𝑁 = 2.85⁢(6) K. While time reversal symmetry is broken, its combination with nearest neighbor lattice translation can form a nonsymmorphic symmetry preserving the 4-fold Dirac point. The application of a magnetic field yields a spin flop transition at the lowest temperatures but, as a consequence of the extreme isotropy of Eu, that phase transition turns into a cross-over at higher temperatures. Our work demonstrates how charge order modifies topology in EuPd 3 S 4 and exposes an archetypal simple cubic Heisenberg antiferromagnet.

Berry, Tanya [Johns Hopkins Univ., Baltimore, MD (↗
DOE OSTI2026

Primary defect production from molecular dynamics simulations of high-energy displacement cascades in NbMoTaW alloys

In this work, we report on large-scale molecular-dynamics (MD) simulations of displacement cascades in equiatomic NbMoTaW alloys at PKA energies ranging from 0.15 to 150 keV. We find defect production to be strongly dependent on recoil energy, scaling sublinearly up to 10 keV, and linearly thereafter. We find the sublinear regime to be defined by low values of surviving Frenkel pairs, typically found as isolated point defects or small defect clusters, while at higher recoil energies dense cascades become more frequent, leading to splitting into subcascades and the production of relatively large prismatic-dislocation loops with ⟨111⟩ and ⟨001⟩ Burgers vectors. These loops immobilize large fractions of defects, leading to a rapid growth of the number of surviving defects in the linear regime. We also anneal post-cascade defect configurations using object-kinetic Monte Carlo (OKMC) simulations to account for intracascade recombination on time scales not accessible to MD simulations. Cascade annealing is strongly temperature dependent, with the OKMC simulations only showing significant recovery at 1000 K but not below. Our results are in general agreement with existing published data for refractory concentrated alloys.

Zhou, Xinran [Lawrence Berkeley National Laborator↗
DOE OSTI2025

Low-energy electrodynamics and a hidden Fermi liquid in the heavy-fermion compound CeCoIn 5

We present time-domain THz spectroscopy of thin films of the heavy-fermion superconductor CeCoIn 5 . Below the ≈40 K Kondo coherence temperature, a narrow Drude-like peak forms, as a result of the 𝑓-orbital–conduction-electron hybridization and the formation of the heavy-fermion state. The complex optical conductivity is analyzed through a Drude model and extended Drude model analysis. Via the extended Drude model analysis, we measure the frequency-dependent scattering rate (1/𝜏) and effective mass (𝑚*/𝑚 𝑏 ). This scattering rate shows a linear dependence on temperature, which matches the dependence of the resistivity as expected. Nevertheless, the width of the low-frequency Drude peak itself that is set by the renormalized quasiparticle scattering rate (1/𝜏*=𝑚 𝑏 /𝑚*⁢𝜏) shows a 𝑇 2 dependence. This is the scattering rate that characterizes the relaxation time of the renormalized quasiparticles. In conclusion, this gives evidence for a Fermi liquid state, which in conventional transport experiments is hidden by the strong temperature dependent mass.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Quenched disorder in the triangular lattice antiferromagnet YbZn 2⁢ GaO 5

We investigate the crystal electric field (CEF) excitations of Yb 3+ ions in powder samples of the triangular-lattice rare-earth-based antiferromagnet YbZn 2⁢ GaO 5 using inelastic neutron scattering (INS). Three CEF excitations from the ground-state Kramers doublet were observed, each exhibiting significant broadening beyond instrumental resolution. Combining temperature-dependent INS and neutron powder diffraction, we identify a significant static contribution to this broadening and attribute it to heterogeneous coordination of Yb 3+ ions due to Ga 3+ /Zn 2+ site mixing. Rietveld refinement of neutron powder diffraction indicates that 35% of Ga occupies the Zn site and 60% of Zn occupies the Ga site. We show with a point charge model for the CEF Hamiltonian that heterogeneous coordination of Yb3+ ions leads to broadened CEF peaks. First-principles calculations demonstrate that the random Ga 3+ /Zn 2+ distribution can produce the distortions of the YbO 6 octahedra observed from neutron diffraction. Because the documented heterogeneity will extend to exchange interactions, our results suggest that disorder is a significant factor in the unusual magnetism previously reported in YbZn 2 ⁢GaO 5 , including broad low-energy magnetic excitations and the absence of magnetic ordering down to 0.3 K.

Crystal field excitations↗
DOE OSTI2025

Bacterial and fungal growth on fungal necromass and its diverse components: Shared profiles and divergent constraints revealed by high‐throughput phenotyping

1. While fungal necromass is increasingly recognized as a major source of persistent carbon (C) in soils, the relative functional roles of bacteria and fungi in decomposing necromass are not fully resolved, and the processes that select for necromass decomposer communities from the broader soil microbial community are an emerging area of interest. 2. In this study, we characterized the growth of 52 bacterial and 83 fungal strains isolated from necromass and soil on 22 C substrates, including different necromass phenotypes, fungal cell wall polymers, dimers and monomers. 3. We found that the isolation habitat of the strains used in this experiment (necromass vs. soil) had no effect on the substrates they were able to use. Isolates from both microbial domains were able to grow on different labile carbon substrates, polymers and necromass phenotypes. However, fungal growth was most limited by necromass melanin content, while bacterial growth was more limited by the abundance of cell wall polysaccharides. Additionally, overall differences in substrate use between bacteria and fungi were most pronounced on polymer substrates. 4. Collectively, our results suggest that there is substantial functional overlap in necromass substrate use across microbial domains, but some notable differences in bacterial and fungal utilization of cell wall polymers, which can function as a direct energy source or a means of accessing other compounds within necromass. Future studies assessing bacteria and fungi decomposing necromass together rather than in isolation will help to uncover potential physical and chemical interactions within and between these two domains during the decay of this important source of persistent soil C.

dead fungal biomass↗
DOE OSTI2025

Absolute timing calibration of the Resolve microcalorimeter spectrometer on the X-ray Imaging and Spectroscopy Mission

The Resolve microcalorimeter spectrometer on the X-ray Imaging and Spectroscopy Mission (XRISM) is designed to have a good timing capability with the mission-level requirement of 1 ms as the absolute time tagging accuracy to suffice the needs for observatory science. In the ground calibration campaign, the absolute and relative timing offsets were measured using pulsed X-rays from the modulated X-ray sources. These offsets were used to determine calibration parameters, the Resolve timing coefficients, which are used in the offline correction of event times. In the orbit, reevaluation of the timing coefficients was carried out using the Crab pulsar, which was observed in two periods, one in the performance verification phase and the other in the guest observation phase. We report the absolute timing calibration of XRISM/Resolve using the ground and in-orbit data. Although the requirement is likely satisfied with the ground parameters, the timing coefficients have been refined using the in-orbit calibration to improve the timing accuracy. In addition, for the first time, we present the interpretation of the absolute timing offset originating from the analog and digital processing of X-ray events unique to a microcalorimeter spectrometer and present a complete view of the timing error of the Resolve instrument.

Astronomy and AstroPhysics↗
DOE OSTI2026

Toward Trustworthy Autonomous Science: A Two-Year Community Roadmap

One year ago, the AISLE roadmap argued that autonomous laboratories operated as isolated islands and proposed a grassroots network organized around five critical dimensions. The field has since moved faster than that roadmap anticipated: multi-agent systems have produced experimentally validated hypotheses, self-driving laboratories have grown more interoperable and orchestrated, reasoning-trained and domain foundation models have raised the capability ceiling, and the Genesis Mission has placed autonomous experimentation at the center of U.S. federal science strategy, with industry emerging as a primary actor. Progress has met a sobering counter-current, including a corrected flagship discovery result, benchmarks showing that agents which rival experts on closed-ended questions still complete only a fraction of open-ended research, and fabricated citations surfacing at leading venues. We read this as the defining tension of the field: producing a candidate discovery is no longer the hard part, but verifying it is, and this asymmetry now limits autonomous science more than raw model capability. Accordingly, we update the roadmap around seven dimensions, revisiting the original five and elevating two former cross-cutting concerns, trust, verification, and reproducibility, and safety, security, and governance, to first-class status. We assess the original milestones (M1 through M14) as achieved, partially achieved, reframed, or open, add four new milestones (M15 through M18) for the elevated dimensions, and scope the path forward to a two-year horizon, with the first year concentrating on interfaces, protocol adoption, and the scaffolding of verification, and the second targeting federation, zero-trust coordination, and governance. Throughout, we position the grassroots network as the interoperability fabric that lets national programs, international initiatives, and commercial platforms connect rather than re-silo.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

High-spectral-resolution X-Ray Observations of the Evolved Supermassive Stellar Binary System η Carinae: Fe K α Band Profile Revealed with XRISM

The supermassive binary system, η Carinae, is experiencing enormous wind-driven mass loss at a rate unparalleled in the rest of the Galaxy. Their wind–wind collision (WWC) continuously produces shock heated, X-ray-emitting plasmas. The XRISM X-ray observatory observed the system in 2023 and 2024 when the X-ray emission began to increase toward periastron passage in 2025. This paper reports unprecedentedly high-resolution X-ray spectra in the Fe Kα band between 6.2 and 7.1 keV, obtained with the Resolve X-ray microcalorimeter. The hydrogen-like (Lyα) and helium-like (Heα) lines reveal three velocity components. Two of them are broadened with maximum velocities of 2000–3000 km s −1 , likely originating from the postshock companion wind. The other is relatively narrow, with a Gaussian broadening of only ∼290 km s −1 in 1σ, which may originate from the postshock companion wind at the WWC stagnation point or penetrating the primary wind. The Fe fluorescent lines exhibit a moderate blueshift and broadening with velocities at 100–200 km s −1 , consistent with the primary wind’s velocity field. The spectra also confirm a Compton shoulder of the Heα line complex for the first time. Both fluorescing and scattering spectral profiles indicate that the binary system is seen from the companion side during these observations. The flux ratio of the Compton-scattering emission to the fluorescent line suggests substantial hydrogen depletion of the primary wind, expected from CNO-cycled hydrogen nuclear fusion gas.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

JWST Observations of Starbursts: Dust Processing in the M82 Superwind

We present JWST MIRI and NIRCam imaging of the inner ∼5 kpc of the M82 superwind at ~ $0''_{.}05 - 0''_{.}375$(∼0.9–6.5 pc) resolution. Targeted filters probe emission from polycyclic aromatic hydrocarbons (PAHs; F335M, F360M, F770W, F1130W) and continuum (F250M, F360M) The images reveal a network of cool wind filaments traced by PAHs. PAH surface brightness declines with the inverse square of distance to the midplane, suggesting that the incident radiation field from the starburst drives the observed PAH intensity out to ±2.5 kpc. The 3.3/11.3 and 3.3/7.7 μm band ratios show uniformity with distance from the starburst, though comparisons with mid-IR dust emission models indicate a modest shift toward larger PAHs. Outside the disk, 11.3/7.7 μm increases moderately, reflecting that PAHs become more neutral with distance from the starburst as they are exposed to a declining radiation field and ionization parameter. Overall, PAHs in the wind are consistent with standard-to-large sizes and standard-to-high ionization states. Including Spitzer and Herschel data, PAH abundance (q PAH ) is set at ∼1% in the starburst and remains unchanging out to ±5 kpc off the disk. This flat q PAH profile suggests that PAHs are shielded from the hot wind, perhaps residing in the surface layers of cool clouds, with possible replenishment from cloud interiors and enrichment of the halo from previous bursts. In this picture, clouds are not dense enough to promote PAH growth, and they likely undergo radiative cooling and mixing with the hot phase to survive the gauntlet for at least ∼20 Myr.

Cronin, Serena A. [University of Maryland, College↗
DOE OSTI2025

X-Ray Polarization of the Magnetar 1E 1841−045

We report on IXPE and NuSTAR observations beginning 40 days after the 2024 outburst onset of magnetar 1E 1841−045, marking the first IXPE observation of a magnetar in an enhanced state. Our spectropolarimetric analysis indicates that both a blackbody (BB) plus double power-law (PL) and a double blackbody plus power-law spectral model fit the phase-averaged intensity data well, with a hard PL tail (Γ = 1.19 and 1.35, respectively) dominating above ≈5 keV. For the former model, we find the soft PL (the dominant component at soft energies) exhibits a polarization degree (PD) of ≈30% while the hard PL displays a PD of ≈40%. Similarly, the cool BB of the 2BB+PL model possesses a PD of ≈15% and a hard PL PD of ≈57%. For both models, each component has a polarization angle (PA) compatible with celestial north. Model-independent polarization analysis supports these results, wherein the PD increases from ≈15% to ≈70% in the 2–3 keV and 6–8 keV ranges, respectively, while the PA remains nearly constant. We find marginal evidence for phase-dependent variability of the polarization properties, namely a higher PD at phases coinciding with the hard X-ray pulse peak. We compare the hard X-ray PL to the expectation from resonant inverse Compton scattering (RICS) and secondary pair cascade synchrotron radiation from primary high-energy RICS photons; both present reasonable spectropolarimetric agreement with the data, albeit the latter does so more naturally. We suggest that the soft PL X-ray component may originate from a Comptonized corona in the inner magnetosphere.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Zero-gap microbial electrolysis cells for efficient hydrogen production from real liquid waste streams

Zero-gap microbial electrolysis cells (MECs) have demonstrated large current and hydrogen production rates from defined substrates in synthetic media, but operation with real waste streams has yet to be proved. This study evaluated the performance and 30-days stability of zero-gap MECs operated with effluent from a single-stage anaerobic digester. The system achieved a maximum current density of 8.8 ± 0.3 A/m 2 with a hydrogen production rate of 32 ± 6 L/L-d, and during 30 days of continuous operation, sustained an average current density of 7 ± 2 A/m 2 and a hydrogen production rate of 20.8 ± 0.2 L/L-d. Carbonate precipitation was identified as a major challenge to long-term stability, and mild acid washing effectively mitigated its adverse effects. The low buffer capacity of the effluent was primarily limiting performance. Furthermore, these findings underscore the significant impact of wastewater chemistry on MEC operation and validate the feasibility of utilizing real waste streams as viable feedstocks for biohydrogen production in zero-gap configurations.

Acid wash↗
DOE OSTI2025

Generative learning of densities on manifolds

A generative modeling framework is proposed that combines diffusion models and manifold learning to efficiently sample data densities on manifolds. The approach utilizes Diffusion Maps to uncover possible low-dimensional underlying (latent) spaces in the high-dimensional data (ambient) space. Two approaches for sampling from the latent data density are described. The first is a score-based diffusion model, which is trained to map a standard normal distribution to the latent data distribution using a neural network. The second one involves solving an Itô stochastic differential equation in the latent space. Additional realizations of the data are generated by lifting the samples back to the ambient space using Double Diffusion Maps , a recently introduced technique typically employed in studying dynamical system reduction; here the focus lies in sampling densities rather than system dynamics. The proposed approaches enable sampling high dimensional data densities restricted to low-dimensional, a priori unknown manifolds. The efficacy of the proposed framework is demonstrated through a benchmark problem and a material with multiscale structure.

Double diffusion maps↗
DOE OSTI2025

Accelerating Hamiltonian Monte Carlo for Bayesian inference in neural networks and neural operators

Hamiltonian Monte Carlo (HMC) is a powerful and accurate method to sample from the posterior distribution in Bayesian inference. However, HMC techniques are computationally demanding for Bayesian neural networks due to the high dimensionality of the network’s parameter space and the non-convexity of their posterior distributions. Therefore, various approximation techniques, such as variational inference (VI) or stochastic gradient MCMC, are often employed to infer the posterior distribution of the network parameters. Such approximations introduce inaccuracies in the inferred distributions, resulting in unreliable uncertainty estimates. In this work, we propose a hybrid approach that combines inexpensive VI and accurate HMC methods to efficiently and accurately quantify uncertainties in neural networks and neural operators. The proposed approach leverages an initial VI training on the full network. We examine the influence of individual parameters on the prediction uncertainty, which shows that a large proportion of the parameters do not contribute substantially to uncertainty in the network predictions. This information is then used to significantly reduce the dimension of the parameter space, and HMC is performed only for the subset of network parameters that strongly influence prediction uncertainties. This yields a framework for accelerating the full batch HMC for posterior inference in neural networks. We demonstrate the efficiency and accuracy of the proposed framework on deep neural networks and operator networks, showing that inference can be performed for large networks with tens to hundreds of thousands of parameters. Finally, we show that this method can effectively learn surrogates for complex physical systems by modeling the operator that maps from upstream conditions to wall-pressure data on a cone in hypersonic flow.

Bayesian inference↗
DOE OSTI2025

Physics-informed latent neural operator for real-time predictions of time-dependent parametric PDEs

Deep operator network (DeepONet) has shown significant promise as surrogate models for systems governed by partial differential equations (PDEs), enabling accurate mappings between infinite-dimensional function spaces. However, when applied to systems with high-dimensional input-output mappings arising from large numbers of spatial and temporal collocation points, these models often require heavily overparameterized networks, leading to long training times. Latent DeepONet addresses some of these challenges by introducing a two-step approach: first learning a reduced latent space using a separate model, followed by operator learning within this latent space. While efficient, this method is inherently data-driven and lacks mechanisms for incorporating physical laws, limiting its robustness and generalizability in data-scarce settings. Here, in this work, we propose PI-Latent-NO, a physics-informed latent neural operator framework that integrates governing physics directly into the learning process. Our architecture features two coupled DeepONets trained end-to-end: a Latent-DeepONet that learns a low-dimensional representation of the solution, and a Reconstruction-DeepONet that maps this latent representation back to the physical space. By embedding PDE constraints into the training via automatic differentiation, our method eliminates the need for labeled training data and ensures physics-consistent predictions. The proposed framework is both memory and compute-efficient, exhibiting near-constant scaling with problem size and demonstrating significant speedups over traditional physics-informed operator models. We validate our approach on a range of parametric PDEs, showcasing its accuracy, scalability, and suitability for real-time prediction in complex physical systems.

Latent representations↗
DOE OSTI2025

Growth-induced Donnan exclusion influences swelling kinetics in highly charged dynamic polymerization hydrogels

Polymeric gels crosslinked by DNA sequences can exploit DNA strand-displacement reactions to promote swelling through dynamic polymerization. The degree of swelling and the rate of swelling must be directly tunable to achieve the promise of programmable soft matter. Though the kinetics of the strand-displacement reaction provide insertion rates up to 10 4 /Molar/second as measured in bulk solution, DNA hydrogel swelling can take upwards of 30 h to complete. Computational modeling of the reaction-induced swelling of these gels with our recently-developed reactive electrochemomechanical theory (Zimmerman et al., 2024) suggests that their extraordinarily slow swelling is partly due to a scaling mismatch between the addition of charge and the addition of fluid volume, leading to a large transient increase in the fixed charge density. The significant increase in the gel’s fixed charge density, due to the binding of negatively charged DNA, sharply restricts the concentration of mobile hairpins through the phenomenon of Donnan charge exclusion, an effect commonly exploited in nanofiltration applications using polymeric membranes. The scaling problem is overcome when the mean additional swelling provided to the hydrogel by addition of a crosslink is above a critical value, thus the swelling outpaces the charge accumulation, leading the fixed charge density to drop and significantly accelerating the swelling process. This study shows that Donnan exclusion can explain the kinetics of DNA hydrogel swelling, and studies ways to modulate the reaction speed by either modifying the salt concentration or increasing or decreasing the number of base pairs in each DNA sequence.

42 ENGINEERING↗
DOE OSTI2025

Neural operators for stochastic modeling of nonlinear structural system response to natural hazards

Traditionally, neural networks have been employed to learn the mapping between finite-dimensional Euclidean spaces. However, recent research has opened up new horizons, focusing on the utilization of deep neural networks to learn operators capable of mapping infinite-dimensional function spaces. Here, in this work, we employ two state-of-the-art neural operators, the deep operator network (DeepONet) and the Fourier neural operator (FNO) for the prediction of the nonlinear time history response of structural systems exposed to natural hazards, such as earthquakes and windstorms. Specifically, we propose two architectures, a self-adaptive FNO and a fast Fourier transform-based DeepONet (DeepFNOnet), where we employ a FNO beyond the DeepONet to learn the discrepancy between the ground truth and the solution predicted by the DeepONet. To demonstrate the efficiency and applicability of the architectures, two problems are considered. In the first, we use the proposed model to predict the seismic nonlinear dynamic response of a six-story shear building subject to stochastic ground motions. In the second problem, we employ the operators to predict the wind-induced nonlinear dynamic response of a high-rise building while explicitly accounting for the stochastic nature of the wind excitation. In both cases, the trained metamodels achieve high accuracy while being orders of magnitude faster than their corresponding high-fidelity models.

DeepONet↗