Optimization in Engineering with Embedded Linear Model Decision Trees
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Ammari, Bashar↗
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Bayesian Model Calibration with an Embedded Statistical Characterization of Model Error.
Abstract not provided.
Huan, Xun↗
Modeling Land-Atmosphere Coupling at Cloud-Resolving Scale Within the Multiple Atmosphere Multiple Land (MAML) Framework in SP-E3SM
Representing subgrid variabilities of land surface processes and their upscaled effects is crucial for global climate modeling. Here, we implement a Multiple Atmosphere Multiple Land (MAML) framework in the superparamaterized version of E3SM (SP32 E3SM) to explicitly simulate the subgrid variabilities of land states and fluxes at cloud-resolving scale and their interactions with atmosphere. Comparing to the standard SP-E3SM in which all the atmospheric columns of the cloud resolving model embedded within the global atmospheric model grid interact with the same land surface (i.e., Multiple Atmosphere Single Land (MASL)), the impact of MAML on the strength of land-atmosphere coupling is limited, partly because the current implementation mainly facilitates one-way coupling between the cloud-resolving model and the land surface model. Despite such limitation, MAML increases the surface latent heat flux at the expense of sensible heat flux, and increases precipitation in India, Amazon, and Central Africa, reducing the model dry bias compared to the standard SP-E3SM. By employing a normalized gross moist stability (NGMS) diagnostic framework, we find that the increase in precipitation minus evaporation (PE) is primarily driven by the change in large-scale moisture convergence, particularly by the increase of water vapor in the lower atmosphere, while the local effect of total surface energy flux plays a minor role in the P-E change. More specifically, MAML changes the surface energy partitioning (evaporative fraction), increases the atmosphere water vapor, and further increases P-E by decreasing the NGMS. Finally, future development in the MAML framework is discussed.
54 ENVIRONMENTAL SCIENCES↗
Impact of the continuum on the γ decay of the lowest 2 + states in 14 C
The half-life, T 1/2 =14.6(33) fs, of the 7012-keV 2$^{+}_{1}$ state in 14 C was measured in an experiment employing the 9 Be( 6 Li,pγ) fusion-evaporation reaction and the GODDESS setup. The γ decay from the 2$^{+}_{2}$ near-threshold resonance, located 142 keV above the 8176-keV neutron-emission threshold, was also investigated. An upper limit of 4.0 × 10 –5 was established for the γ-decay branching ratio with respect to the neutron-decay channel. The B(E2) transition probabilities for these 2$^{+}_{1}$ and 2$^{+}_{2}$ states were compared to predictions from the Shell Model Embedded in the Continuum (SMEC). Significant modifications of these B(E2) probabilities, with respect to the standard shell model, are expected due to the coupling to the continuum. For calculations using the YSOX interaction, agreement was found for large negative values of V 0 , the coupling constant to the continuum. The central value V 0 = −645 MeV fm 3 results in a retardation by a factor ∼ 3.5 for the 2$^{+}_{1}$ → 0$^{+}_{1}$ transition, and an enhanced probability by a factor ∼ 2.5 for the γ-decay out of the 2$^{+}_{2}$ state. The latter factor reflects the effect of collectivization of the 2$^{+}_{2}$ excitation when the coupling to the continuum is taken into account.
14C↗
Using Topic Modeling and Text Embeddings to Predict Deleted Tweets
Predictive models for tweet deletion have been a relatively unexplored area of Twitter-related computational research. We first approach the deletion of tweets as a spam detection problem, applying a small set of handcrafted features to improve upon the current state-of-the- art in predicting deleted tweets. Next, we apply our approach to a dataset of deleted tweets that better reflects the current deletion rate. Since tweets are deleted for reasons beyond just the presence of spam, we apply topic modeling and text embeddings in order to capture the semantic content of tweets that can lead to tweet deletion. Our goal is to create an effective model that has a low-dimensional feature space and is also language-independent. A lean model would be computationally advantageous processing high-volumes of Twitter data, which can reach 9,885 tweets per second. Our results show that a small set of spam-related features combined with word topics and character-level text embeddings provide the best f1 when trained with a random forest model. The highest precision of the deleted tweet class is achieved by a modification of paragraph2vec to capture author identity.
Potash, Peter J.↗
An unsupervised machine learning based approach to identify efficient spin-orbit torque materials
Materials with large spin–orbit torque (SOT) hold considerable significance for many spintronic applications because of their potential for energy-efficient magnetization switching. Unfortunately, most of the existing materials exhibit an SOT efficiency factor that is much less than unity, requiring a large current for magnetization switching. The search for new materials that can exhibit an SOT efficiency much greater than unity is a topic of active research, and only a few such materials have been identified using conventional approaches. In this paper, we present a machine learning-based approach using a word embedding model that can identify new results by deciphering non-trivial correlations among various items in a specialized scientific text corpus. We show that such a model can be used to identify materials likely to exhibit high SOT and rank them according to their expected SOT strengths. The model captured the essential spintronics knowledge embedded in scientific abstracts within various materials science, physics, and engineering journals and identified 97 new materials to exhibit high SOT. Among them, 16 candidate materials are expected to exhibit an SOT efficiency greater than unity, and one of them has recently been confirmed with experiments with quantitative agreement with the model prediction.
Sayed, Shehrin↗
Numerical Study on the Effect of Methane Doping in Hydrogen-Air Rotating Detonation Engines for Various Temperatures and Pressures
Rotating detonation engines (RDEs) have gained attention as a promising technology for future aviation engines. However, the numerical studies of these systems pose severe challenges due to the broad range of spatial and temporal scales. In this study, we use an adaptive mesh refinement based compressible, reactive solver PeleC to resolve the broad range of scales and accurately capture shock and detonation waves using high-resolution numerical schemes. Multi- species transport along with compressible Navier-Stokes equations are solved in the model along with a finite-rate based chemistry model. Embedded boundary method is used to model the complex geometry consisting of discrete fuel nozzles and the combustion chamber. The fuel consisting predominantly of hydrogen is doped with varying levels of methane while air is used as the oxidizer. For a specified total pressure and temperature, the number of stable detonation waves is found to decrease with increasing methane concentration in the fuel mixture. Additionally, no stable detonation solutions are observed for methane composition higher than 20% by volume for the range of operating conditions studied (300-900K, 10 Atm). The increased presence of high temperature zones is also indicative of higher thermal NOx emissions at low methane concentrations. The effect of fuel-air mixture composition and temperature on the detonability, detonation wave structure, mode transitions and their stability are analyzed in this study in addition to their implication on NOx emission.
automatic mesh refinement↗
DMS role in ENSO cycle in the tropics
We examined the multiyear mean and variability of dimethyl sulfide (DMS) and its relationship to sulfate aerosols, as well as cloud microphysical and radiative properties. We conducted a 150 year simulation using preindustrial conditions produced by the Community Earth System Model embedded with a dynamic DMS module. The model simulated the mean spatial distribution of DMS emissions and burden, as well as sulfur budgets associated with DMS, SO 2 , H 2 SO 4 , and sulfate that were generally similar to available observations and inventories for a variety of regions. Changes in simulated sea-to-air DMS emissions and associated atmospheric abundance, along with associated aerosols and cloud and radiative properties, were consistently dominated by El Niño–Southern Oscillation (ENSO) cycle in the tropical Pacific region. Simulated DMS, aerosols, and clouds showed a weak positive feedback on sea surface temperature. This feedback suggests a link among DMS, aerosols, clouds, and climate on interannual timescales. The variability of DMS emissions associated with ENSO was primarily caused by a higher variation in wind speed during La Niña events. As a result, the simulation results also suggest that variations in DMS emissions increase the frequency of La Niña events but do not alter ENSO variability in terms of the standard deviation of the Niño 3 sea surface temperature anomalies.
58 GEOSCIENCES↗
Beyond Point Estimates: Benchmarking Uncertainty Quantification Methods on the AION-1 Astronomical Foundation Model
Foundation models for astronomical surveys offer powerful learned representations that can be transferred to downstream regression tasks such as galaxy property estimation. However, point predictions alone are insufficient for scientific inference; reliable uncertainty quantification (UQ) is essential. We compare seven UQ methods on galaxy property regression using frozen AION-1 foundation-model embeddings, predicting redshift, stellar mass, stellar-population age, gas-phase metallicity, and specific star-formation rate, from Legacy Survey photometry/imaging and DESI spectra, with PROVABGS-derived labels. Distribution-free conformal methods achieve marginal coverage within $\sim$1 pp of the nominal 90% across all properties, while non-conformal baselines (Deep Ensembles, MC~Dropout) fail to calibrate reliably. Among conformal approaches, Conformalized Quantile Regression (CQR) delivers the best coverage in the bin with the poorest model predictions. More importantly, only the Locally Valid and Discriminative (LVD) framework -- particularly when operating on AION-1 embeddings -- also provides finite-sample \emph{local validity}, producing intervals that adapt to each galaxy's local prediction difficulty rather than relying on marginal guarantees alone. These results establish conformal prediction, and LVD in particular, as the preferred UQ framework for uncertainty-aware inference on foundation-model embeddings in astrophysics.
Tame-Narvaez, Karla [Fermilab] (ORCID:000000022249↗
Bayesian modeling of inconsistent plastic response due to material variability
The advent of fabrication techniques such as additive manufacturing has focused attention on the considerable variability of material response due to defects and other microstructural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. To account for material response variability through variations in physical parameters, we adapt a recent Bayesian embedded modeling error calibration technique. We use Bayesian model selection to determine the most plausible of a variety of plasticity models and the optimal embedding of parameter variability. To expedite model selection, we develop an adaptive importance-sampling-based numerical integration scheme to compute the Bayesian model evidence. In conclusion, we demonstrate that the new framework provides predictive realizations that are superior to more traditional ones, and how these UQ techniques can be used in model selection and assessing the quality of calibrated physical parameters.
36 MATERIALS SCIENCE↗
Accelerating Asymptotically Exact MCMC for Computationally Intensive Models via Local Approximations
We construct a new framework for accelerating Markov chain Monte Carlo in posterior sampling problems where standard methods are limited by the computational cost of the likelihood, or of numerical models embedded therein. Our approach introduces local approximations of these models into the Metropolis–Hastings kernel, borrowing ideas from deterministic approximation theory, optimization, and experimental design. Previous efforts at integrating approximate models into inference typically sacrifice either the sampler’s exactness or efficiency; our work seeks to address these limitations by exploiting useful convergence characteristics of local approximations. We prove the ergodicity of our approximate Markov chain, showing that it samples asymptotically from the exact posterior distribution of interest. We describe variations of the algorithm that employ either local polynomial approximations or local Gaussian process regressors. Our theoretical results reinforce the key observation underlying this article: when the likelihood has some local regularity, the number of model evaluations per Markov chain Monte Carlo (MCMC) step can be greatly reduced without biasing the Monte Carlo average. Numerical experiments demonstrate multiple order-of-magnitude reductions in the number of forward model evaluations used in representative ordinary differential equation (ODE) and partial differential equation (PDE) inference problems, with both synthetic and real data. Supplementary materials for this article are available online.
97 MATHEMATICS AND COMPUTING↗
Vacuum f(R) thick brane solution with a modified Gaussian warp function
This work deals with f(R) modified gravity in five dimensional space–time. The modified Gaussian thick brane is shown to be a suitable exact solution in the framework of f(R) gravity in five dimensions, confirming with the AdS brane far from the brane. Response of the brane to gravitational fluctuations is discussed, and by working in the Einstein frame, the stability of the solution against linear perturbations is obtained, which also shows that gravitational zero mode is localized on the brane. For cosmological purposes, we consider FLRW model embedded in a proposed brane. Therefore, we arrive at two models, resembling the radiation dominated era and bouncing cosmological models.
79 ASTRONOMY AND ASTROPHYSICS↗
Machine learning based simultaneous control of air handling unit discharge air and condenser water temperatures set-point for minimized cooling energy in an office building
In this study, an artificial intelligence based real-time prediction and control model to optimize condenser water temperature and discharge air temperature (DAT) set-points in water-cooled air handling unit (AHU) system has been developed. EnergyPlus-MATLAB co-simulation has been conducted to analyze the developed model's effectiveness. Here, to develop artificial neural networks (ANN) model, embedded neural network objects in MATLAB was utilized. The developed model could decide an optimal temperature set-points based on outdoor air wet-bulb temperature to reflect the Korean climate context. As a result, the developed ANN prediction model showed the predictive performance of Cv(RMSE) of approximately 21%. Compared to the conventional fixed temperature algorithm, which fixes AHU DAT at 14°C and condenser water temperature at 32°C, the ANN based optimized control showed a 22% total cooling energy reduction. These results show that significant energy savings can be achieved by simultaneously controlling condenser water temperature and AHU DAT set-points considering Korean climatic characteristics using AI technologies such as ANN models.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Visible Light Absorption of N-Doped TiO2 Rutile Using (LR/RT)-TDDFT and Active Space EOMCCSD Calculations
We have performed detailed ground and excited state calculations of pure and N-doped TiO2 rutile to model and analyze the experimentally observed UV/Vis spectrum. Using our embedding model we have performed both linear-response (LR) and real-time (RT) TDDFT calculations of the excited states of the pure and N-doped systems. We have also studied the lowest excitations using high-level active space equation-of-motion coupled cluster (EOMCC) approaches involving all single and inter-band double excitations. We compare and contrast the nature of the excitations in detail for the pure and doped systems and also provide an analysis of the excited-state density using our RT-TDDFT calculations. Our calculations indicate a lowering of the band gap and verify the role of the N3- states on the observed spectrum of N-doped TiO2 rutile as suggested by experimental findings. Both RT-TDDFT and EOMCC calculations show that the excitations in pure TiO2 are more delocalized compared with the N-doped system.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
model-of-perception
Algorithms for perceptually grounded evaluation of scientific visualizations, using neural reconstruction and embedding models to quantify clarity and interpretability.
Bujack, Roxana↗
Invariant-mass spectroscopy of O 14 excited states
Excited states in 14 O have been investigated both experimentally and theoretically. Experimentally, these states were produced via neutron-knockout reactions with a fast 15 O beam and the invariant-mass technique was employed to isolate the 1 p and 2 p decay channels and determine their branching ratios. The spectrum of excited states was also calculated with the shell model embedded in the continuum that treats bound and scattering states in a unified model. By comparing energies, widths, and decay branching patterns, spin and parity assignments for all experimentally observed levels below 8 MeV are made. This includes the location of the second 2 + state that we find is in near degeneracy with the third 0 + state. An interesting case of sequential 2 p decay through a pair of degenerate 13 N excited states with opposite parities was found where the interference between the two sequential decay pathways produces an unusual relative-angle distribution between the emitted protons.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Hierarchical Conceptual Model Event and Activity Domains for Forecasting State-Sponsored Civil Nuclear Power Activities
In FY20, the Savannah River National Laboratory and the Sanghani Center for Artificial Intelligence and Data Analytics (Virginia Tech) entered a collaboration funded by the Department of Energy’s Office of Defense Nuclear Nonproliferation Research and Development to develop a machine learning based modeling pipeline to extract proliferation events of interest from open data sources. The prototype modeling pipeline that was developed relies on the use of time dependent word embedding models to identify contextual shifts in key words and phrases that act as indicators of events of interest. The FY20- 21 efforts were focused on a narrow topical domain of forecasting “fissile core fabrication” at the Savannah River Site prior to its official announcement in 2018. In FY22, the research team was funded to continue development of the modeling pipeline by applying it to the problem of forecasting new, and/or significant changes to existing, civil nuclear power reactors around the world. In this effort, the development will focus on proving applicability to a broader topical domain and in data environments that may contain more sparse information, relative to the United States. In this report, the worldwide landscape of civil nuclear reactors is outlined, the timeline of interest is defined, and a hierarchical conceptual model is established to identify the activity domains of interest and the event domains of interest. This preliminary effort will guide the curation of a glossary of key terms for data acquisition, as well as the downstream modeling efforts.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Machine learning prediction of enzyme optimum pH
The relationship between pH and enzyme catalytic activity, especially the optimal pH (pH opt ) at which enzymes function, is critical for biotechnological applications. Hence, computational methods to predict pH opt will enhance enzyme discovery and design by facilitating accurate identification of enzymes that function optimally at specific pH levels, and by elucidating sequence-function relationships. Here, in this study, we proposed and evaluated various machine learning methods for predicting pH opt , conducting extensive hyperparameter optimization and training over 11,000 model instances. Our results demonstrate that models utilizing language model embeddings markedly outperform other methods in predicting pHopt. We present EpHod, the best-performing model, to predict pHopt, making it publicly available to researchers. From sequence data, EpHod directly learns structural and biophysical features that relate to pH opt , including proximity of residues to the catalytic centre and the accessibility of solvent molecules. Overall, EpHod presents a promising advancement in pH opt prediction and will potentially speed up the development of enzyme technologies.
97 MATHEMATICS AND COMPUTING↗