Quasi-Adam: Accelerating Adam using quasi-Newton approximations
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Mathematics and Computing↗
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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↗
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↗
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↗
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↗
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↗
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↗
Modeling viscosity of commercial glass melts: Adam–Gibbs equation and Gaussian process regression
Not Available
36 MATERIALS SCIENCE↗
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↗
Digitization of Bashforth and Adams Table II for Axisymmetric Capillary Surface Profiles
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Post Guillen, Donna↗
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↗
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↗
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↗
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 Δχ 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↗
2021–2022 Can Do Colorado E-Bike Full-Scale Pilot Program Study
In 2021–2022, the Colorado Energy Office conducted a full-scale pilot program study on e-bike usage as part of the Can Do Colorado initiative, providing e-bikes to low-income participants across the state. A [2020 mini pilot program study](https://www.nlr.gov/transportation/secure-transportation-data/tsdc-2020-can-do-colorado-e-bike-pilot-program.html) informed the full-scale study. Both studies used pedal-assist e-bikes, which feature an electric motor and battery to help power the bike. The motor amplifies the power behind each pedal stroke, augmenting the energy you put into the bike. #### Data Collection Agency The Colorado Energy Office conducted the study in partnership with local organizations in Adams and Broomfield counties (Smart Commute Metro North), Boulder (Community Cycles), Durango (Four Corners Office for Resource Efficiency), Fort Collins (City of Fort Collins), Pueblo (Pueblo County), and Vail (Town of Vail). #### Survey Methodology Program participants received an e-bike and accessories at no cost and manually submitted travel data and feedback via the CanBikeCO smartphone app. Developed in partnership with NLR, the app used a customized version of the open-source [NLR OpenPATH platform](https://www.nlr.gov/transportation/openpath.html). #### Survey Records and Data Survey records include 170 participants. The six datasets contain up to 18 months of partially automated travel diaries, combining sensed and surveyed travel behavior data—patterns of multimodal, end-to-end, individual human mobility—as well as demographic information from participants. The number of e-bike trips and e-bike miles traveled per location are 1,560 and 4,179 for Adams and Broomfield counties; 8,481 and 27,000 for Boulder; 2,815 and 6,307 for Durango; 3,483 and 7,080 for Fort Collins; 4,022 and 14,887 for Pueblo, and 1,206 and 3,3361 for Vail.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Hardtack II
Armed and ready to fire, Adams remained suspended under a balloon high over the Nevada Test Site throughout the day and night of October 31, 1958. Shortly after midnight, Adams was lowered to the ground and disarmed. With that, Operation Hardtack II ended and a test moratorium, primarily a gentlemen’s agreement between the United States and the Soviet Union, took effect. As the possibility of a moratorium became more and more likely in late 1957, the Atomic Energy Commission and its two weapon laboratories sought Presidential approval for an unprecedented number of tests for the coming year, including a proposal by the UCRL for a series of underground and safety tests in an operation called Millrace. Not wanting to jeopardize ongoing international disarmament talks as well as the moratorium, itself, Eisenhower resisted giving approval for Millrace until late August 1958, barely two months before the anticipated start of the moratorium. Millrace, quickly renamed Hardtack II, was expanded to thirty-seven tests beginning with Otero on September 12 th and concluding with Titania on October 30 th . Eighteen devices, including Otero and Titania, were safety tests. Three of these tests explored “safety characteristics for underground detonations” in tunnels. Two of the three such tests vented. In this respect, Hardtack II was a harbinger of the future in that the problem of venting was never fully resolved. Other safety tests, designed to give no yield, were more successful with San Juan, Oberon, and Ganymede having “no measurable yield.”
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
Crossing The Gap Using Variational Quantum Eigensolver: A Comparative Study
Within the evolving domain of quantum computational chemistry, the Variational Quantum Eigensolver (VQE) has been developed to explore not only the ground state but also the excited states of molecules. In this study, we compare the performance of Variational Quantum Deflation (VQD) and Subspace-Search Variational Quantum Eigensolver (SSVQE) methods in determining the low-lying excited states of $LiH$. Our investigation reveals that while VQD exhibits a slight advantage in accuracy, SSVQE stands out for its efficiency, allowing the determination of all low-lying excited states through a single parameter optimization procedure. We further evaluate the effectiveness of optimizers, including Gradient Descent (GD), Quantum Natural Gradient (QNG), and Adam optimizer, in obtaining $LiH$'s first excited state, with the Adam optimizer demonstrating superior efficiency in requiring the fewest iterations. Moreover, we propose a novel approach combining Folded Spectrum VQE (FS-VQE) with either VQD or SSVQE, enabling the exploration of highly excited states. We test the new approaches for finding all three $H_4$'s excited states. Folded Spectrum SSVQE (FS-SSVQE) can find all three highly excited states near $-1.0$ Ha with only one optimizing procedure, but the procedure converges slowly. In contrast, although Folded spectrum VQD (FS-VQD) gets highly excited states with individual optimizing procedures, the optimizing procedure converges faster.
Chen, I-Chi↗
Exploring the Structure–Function Relationship in Iridium–Cobalt Oxide Catalyst for Oxygen Evolution Reaction across Different Electrolyte Media
Renewable hydrogen generation from water electrolysis offers a viable path to decarbonization if the costs can be reduced. The iridium-based anode catalyst is one of the most expensive components in electrolyzers. We propose reducing iridium usage by substituting Ir with Co, a more affordable metal, in the mixed oxide phase to enhance the catalytic activity while minimizing Ir consumption. A modified surfactant-assisted Adams fusion synthesis technique was developed as a scalable method for producing IrCo oxide nanoparticles. The synthesized material outperforms the commercial baseline, iridium oxide with carbon (IrOx_C), in both acidic and alkaline media. Acid etching (IrCo_ae) further enhances activity by selectively removing Co to expose more active sites. IrCo_ae achieved a significantly lower overpotential at 10 mA/cm 2 compared to IrOx_C, with reductions of approximately 18% under acidic conditions and 14% under alkaline conditions. This work demonstrates that the proposed synthesis method enables efficient Ir utilization and can be adapted to enhance catalyst stability for renewable hydrogen production.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗