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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Improving ADAM through an implicit-explicit (IMEX) time-stepping approach

The ADAM optimizer, often used in machine learning for neural network training, corresponds to an underlying ordinary differential equation (ODE) in the limit of very small learning rates. Here, this work shows that the classical ADAM algorithm is a first-order implicit-explicit (IMEX) Euler discretization of the underlying ODE. Employing the time discretization point of view, we propose new extensions of the ADAM scheme obtained by using higher-order IMEX methods to solve the ODE. Based on this approach, we derive a new optimization algorithm for neural network training that performs better than classical ADAM on several regression and classification problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

ADAM: A web platform for graph-based modeling and optimization of supply chains

Modeling and optimization are essential tasks that arise in the analysis and design of supply chains (SCs). SC models are essential for understanding emergent behavior such as transactions between participants, inherent value of products exchanged, as well as impact of externalities (e.g., policy and climate) and of constraints. Unfortunately, most users of SC models have limited expertise in mathematical optimization, and this hinders the adoption of advanced decision-making tools. Here, in this work, we present ADAM, a web platform that enables the modeling and optimization of SCs. ADAM facilitates modeling by leveraging intuitive and compact graph-based abstractions that allow the user to express dependencies between locations, products, and participants. ADAM model objects serve as repositories of experimental, technology, and socio-economic data; moreover, the graph abstractions facilitate the organization and exchange of models and provides a natural framework for education and outreach. Here, we discuss the graph abstractions and software design principles behind ADAM, its key functional features and workflows, and application examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Accelerating gradient descent and Adam via fractional gradients

Here we propose a class of novel fractional-order optimization algorithms. We define a fractional-order gradient via the Caputo fractional derivatives that generalizes integer-order gradient. We refer it to as the Caputo fractional-based gradient, and develop an efficient implementation to compute it. A general class of fractional-order optimization methods is then obtained by replacing integer-order gradients with the Caputo fractional-based gradients. To give concrete algorithms, we consider gradient descent (GD) and Adam, and extend them to the Caputo fractional GD (CfGD) and the Caputo fractional Adam (CfAdam). We demonstrate the superiority of CfGD and CfAdam on several large scale optimization problems that arise from scientific machine learning applications, such as ill-conditioned least squares problem on real-world data and the training of neural networks involving non-convex objective functions. Numerical examples show that both CfGD and CfAdam result in acceleration over GD and Adam, respectively. We also derive error bounds of CfGD for quadratic functions, which further indicate that CfGD could mitigate the dependence on the condition number in the rate of convergence and results in significant acceleration over GD.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Augmented Adam-Gibbs model for glass melt viscosity and configuration entropy as functions of temperature and composition

As the temperature of glass melt increases, its structure approaches the state of a simple liquid while the configuration entropy approaches a maximum value. We describe this gradual change using a power law function of inverse temperature. The Adam-Gibbs model for glass viscosity as a function of temperature and glass composition augmented in this way is greatly simplified when applied to common glass families occupying moderate composition regions, such as float glass or nuclear waste glasses, on which properties can be approximated as linear functions of composition. The parsimonious model thus obtained is preferable for use in optimizing glass formulation and mathematical modeling of glass melting and forming. For multicomponent glasses with N viscosity-affecting components, the augmented Adam-Gibbs model requires 2N + 3 adjustable parameters. The model efficacy is demonstrated by fitting the model to a viscosity-temperature-composition dataset for low-activity nuclear waste glasses.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Advancing Agrivoltaic Modeling With ADAM

Designing an agrivoltaic system presents a complex set of tradeoffs around PV system configuration, resulting performance, agricultural needs, and system economics. Developing tools for agrivoltaic analysis can assist system designers when making decisions related to these tradeoffs. We are developing the Agrivoltaics Design and Analysis Model (ADAM) as a free, publicly available web tool for agrivoltaics economics analysis. Features of ADAM include automated calculations for available agrivoltaic cropland, user-friendly configuration changes, inter-row irradiance calculations, and integrated economic modeling of energy and non-energy revenues. We will discuss the iterative process of agrivoltaic tool development including tradeoffs between accuracy and uncertainty, feature prioritization, and ease of use driven by our multi-disciplinary stakeholder design process. Finally, we will share modeling results from existing agrivoltaics systems as a preliminary case study.

14 SOLAR ENERGY↗
DOE OSTI2025

Iridium-cobalt oxide synthesized via surfactant-assisted Adams fusion for efficient oxygen evolution in acidic and alkaline media

A series of iridium-cobalt (Ir-Co) oxide catalysts were synthesized using a modified surfactant-assisted Adams fusion method and evaluated for the oxygen evolution reaction (OER) in both acidic and alkaline media. The effect of varying the Ir/Co ratio was systematically studied and compared with commercial Ir black and pure IrO 2 samples. The actual elemental ratios were quantified using X-ray photoelectron spectroscopy (XPS), energy-dispersive X-ray spectroscopy (EDS), and inductively coupled plasma-mass spectrometry (ICP-MS). Among the synthesized samples, the Ir 6 Co 4 catalyst exhibited the best performance in acidic media, achieving an iR-corrected overpotential of 292 mV. In alkaline conditions, it demonstrated comparable activity to Ir black, with an iR-corrected overpotential of 263 mV. XPS and electron energy loss spectroscopy analyses revealed that increasing Co content led to a higher fraction of metallic Ir (Ir 0 ) in the catalyst. In addition, the role of Ir 3+ in enhancing OER activity was explored. A strong correlation was observed between higher Ir 3+ content and improved OER performance in acidic conditions.

Adams fusion↗
DOE OSTI2021

Analytic continuation of noisy data using Adams Bashforth residual neural network

We propose a data-driven learning framework for the analytic continuation problem in numerical quantum many-body physics. Designing an accurate and efficient framework for the analytic continuation of imaginary time using computational data is a grand challenge that has hindered meaningful links with experimental data. The standard Maximum Entropy (MaxEnt)-based method is limited by the quality of the computational data and the availability of prior information. Also, the MaxEnt is not able to solve the inversion problem under high level of noise in the data. Here we introduce a novel learning model for the analytic continuation problem using a Adams-Bashforth residual neural network (AB-ResNet). Additionally, the advantage of this deep learning network is that it is model independent and, therefore, does not require prior information concerning the quantity of interest given by the spectral function. More importantly, the ResNet-based model achieves higher accuracy than MaxEnt for data with higher level of noise. Finally, numerical examples show that the developed AB-ResNet is able to recover the spectral function with accuracy comparable to MaxEnt where the noise level is relatively small.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Final Report DE-SC0020432 PI: Frank, Adam

This goal of this proposal was a focused study to understand multi‐dimensional Colliding Magnetized Radiative Plasma flows (or CMRPs) relevant to basic plasma physics and jets in the context of stellar evolution astrophysics.

Frank, Adam↗
DOE OSTI2021

ADAM Program Execution Plan LANL Inputs (FY2022)

The National Security Research Center (NSRC) is Los Alamos National Laboratory’s classified library. There are two groups associated with the NSRC, both of who work for LANL’s Weapons Research Services (WRS) division (WRS-SIS and WRS-WMT). These groups are funded in part by the NNSA Archives Program. The NSRC’s collections include tens of millions of documents from the Manhattan Project era through today. It is staffed with an expert, highly trained staff of librarians, archivists, digitizers, historians, and communications specialists. The NSRC traces its lineage to the wartime Technical Library created by J. Robert Oppenheimer during in 1943. Today, it supports a broad range of researchers within the LANL Weapons Program and beyond. The NSRC also has customers across other National Nuclear Security Administration labs and sites, and partners in the Department of Defense. This report highlights LANL accomplishments through NSRC.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

Endocytosis of the non-catalytic ADAM23: Recycling and long half-life properties

A Disintegrin And Metalloprotease 23 (ADAM23) is a member of the ADAMs family of transmembrane proteins, mostly expressed in nervous system, and involved in traffic and stabilization of Kv1-potassium channels, synaptic transmission, neurite outgrowth, neuronal morphology and cell adhesion. Also, ADAM23 has been linked to human pathological conditions, such as epilepsy, cancer metastasis and cardiomyopathy. ADAM23 functionality depends on the molecule presence at the cell surface and along the secretory pathway, as expected for a cell surface receptor. Because endocytosis is an important functional regulatory mechanism of plasma membrane receptors and no information is available about the traffic or turnover of non-catalytic ADAMs, we investigated ADAM23 internalization, recycling and half-life properties. Here, we show that ADAM23 undergoes constitutive internalization from the plasma membrane, a process that depends on lipid raft integrity, and is redistributed to intracellular vesicles, especially early and recycling endosomes. Furthermore, we observed that ADAM23 is recycled from intracellular compartments back to the plasma membrane and thus has longer half-life and higher cell surface stability compared with other ADAMs. Our findings suggest that regulation of ADAM23 endocytosis/stability could be exploited therapeutically in diseases in which ADAM23 is directly involved, such as epilepsy, cancer progression and cardiac hypertrophy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Stochastic gradient descent for optimization for nuclear systems

The use of gradient descent methods for optimizing k-eigenvalue nuclear systems has been shown to be useful in the past, but the use of k-eigenvalue gradients have proved computationally challenging due to their stochastic nature. ADAM is a gradient descent method that accounts for gradients with a stochastic nature. This analysis uses challenge problems constructed to verify if ADAM is a suitable tool to optimize k-eigenvalue nuclear systems. ADAM is able to successfully optimize nuclear systems using the gradients of k-eigenvalue problems despite their stochastic nature and uncertainty. Furthermore, it is clearly demonstrated that low-compute time, high-variance estimates of the gradient lead to better performance in the optimization challenge problems tested here.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Challenges in Training PINNs: A Loss Landscape Perspective

This paper explores challenges in training Physics Informed Neural Networks (PINNs), emphasizing the role of the loss landscape in the training process. We examine difficulties in minimizing the PINN loss function, particularly due to ill conditioning caused by differential operators in the residual term. We compare gradient-based optimizers Adam, L-BFGS, and their combination Adam+L-FGS, showing the superiority of Adam+L-BFGS, and introduce a novel secondorder optimizer, NysNewton-CG (NNCG), which significantly improves PINN performance. Theoretically, our work elucidates the connection between ill-conditioned differential operators and ill-conditioning in the PINN loss and shows the benefits of combining first- and second-order optimization methods. Our work presents valuable insights and more powerful optimization strategies for training PINNs, which could improve the utility of PINNs for solving difficult partial differential equations.

Rathore, Pratik↗
DOE OSTI2022

Glass length: Workability and meltability of glass as a function of glass melt fragility

Workability and meltability of molten glass are properties important for balancing glass formulation with the design and operations of glass melting and forming facilities. The working and melting “lengths” are the temperature intervals within which glass viscosity allows the melting and forming glass to be performed. They are related to glass melt structure through the glass melt fragility, which, by the augmented Adam-Gibbs equation, is a function of configuration entropy. Both working length and melting length are high for strong glasses and decrease as the melt fragility increases. In this work, analytical formulas are obtained for three-parameter viscosity models VFT, AM, and MYEGA. These models agree with each other and with the Adam-Gibbs equation on the relationship between workability and fragility of glasses but diverge at high-temperatures. The compositional effects on workability and meltability are mediated via the fragility as a function of glass composition expressed in terms of mass and mole component coefficients.

36 MATERIALS SCIENCE↗
DOE OSTI2025

DESI 2024: Constraints on physics-focused aspects of dark energy using DESI DR1 BAO data

Baryon acoustic oscillation data from the first year of the Dark Energy Spectroscopic Instrument (DESI) provide near percent-level precision of cosmic distances in seven bins over the redshift range z=0.1–4.2. Here, this paper is the follow-up to the original DESI BAO cosmology paper [A. G. Adame et al. (DESI Collaboration), arXiv:2404.03002], which considered the conventional w 0 w a cold dark matter (CDM) model. We use the novel DESI data, together with other cosmic probes, to constrain the background expansion history using some well-motivated physical classes of dark energy. In particular, we explore three physics-focused behaviors of dark energy from the equation of state and energy density perspectives: the thawing class (matching many simple quintessence potentials), emergent class (where dark energy comes into being recently, as in phase transition models), and mirage class [where phenomenologically the distance to cosmic microwave background (CMB) last scattering is close to that from a cosmological constant Λ despite dark energy dynamics]. All three classes fit the data at least as well as Λ ⁢CDM, and indeed can improve on it by Δ⁢χ 2 ≈ –5 to –17 for the combination of DESI BAO with CMB and supernova data while having one more parameter. The mirage class does essentially as well as w 0 ⁢w a CDM and exhibits moderate to strong Bayesian evidence preference with respect to Λ⁢ CDM. These classes of dynamical behaviors highlight worthwhile avenues for further exploration into the nature of dark energy.

79 ASTRONOMY AND ASTROPHYSICS↗